Stainless-steel provides substantial advantages for structural uses,though its upfront cost is notably high.Consequently,it’s vital to establish safe and economically viable design practices that enhance material uti...Stainless-steel provides substantial advantages for structural uses,though its upfront cost is notably high.Consequently,it’s vital to establish safe and economically viable design practices that enhance material utilization.Such development relies on a thorough understanding of the mechanical properties of structural components,particularly connections.This research advances the field by investigating the behavior of stainless-steel connections through the use of a four-parameter fitting technique and explainable artificial intelligence methods.Training was conducted on eight different machine learning algorithms,namely,Decision Tree,Random Forest,K-nearest neighbors,Gradient Boosting,Extreme Gradient Boosting,Light Gradient Boosting,Adaptive Boosting,and Categorical Boosting.SHapley Additive Explanations was applied to interpret model predictions,highlighting features like spacing between bolts in tension and end-plate height as highly impactful on the initial rotational stiffness and plastic moment resistance.Results showed that Extreme Gradient Boosting achieved a coefficient of determination score of 0.99 for initial stiffness and plastic moment resistance,while Gradient Boosting model had similar performance with maximum moment resistance and ultimate rotation.A user-friendly graphical user interface(GUI)was also developed,allowing engineers to input parameters and get rapid moment–rotation predictions.This framework offers a data-driven,interpretable alternative to conventional methods,supporting future design recommendations for stainless-steel beam-to-column connections.展开更多
In addition to accounting for non-gradient nonlocal elastic stress,a nonlocal strain gradient theory(NSGT)also considers the nonlocality of higher-order strain gradients;thus,it is applicable to small-scale structures...In addition to accounting for non-gradient nonlocal elastic stress,a nonlocal strain gradient theory(NSGT)also considers the nonlocality of higher-order strain gradients;thus,it is applicable to small-scale structures and can account for both hardening and softening effects.An analytical model is constructed in this research endeavor to depict the free vibration characteristics of sandwich functionally graded porous(FGP)doubly-curved nanoshell integrated with piezoelectric surface layers consists of three distinct layers,taking into account flexoelectrici effect based on NSGT and novel refined high-order shear deformation hypothesis.The novelty of this study is that the two nonlocal coefficients and material length scale of the core layer are variable along thickness,like other material characteristics.The equilibrium equation of motion of the doubly-curved nanoshell is derived from Hamilton’s principle,then the Galerkin method is applied to derive the natural vibration frequency values of the doubly-curved nanoshell with different boundary conditions(BCs).The influence of parameters such as flexoelectric effect,nonlocal and length scale factors,elastic medium stiffness factor,porosity factor,and BCs on the free vibration esponse of the nanoshell is detected and comprehensively studied.This paper is claimed to provide a theoretical predicition on the impact of the size-small dependent and flexoelectric effect upon the oscillation of FGP nanoshell integrated with piezoelectric surface layers,thus sheding light on understanding the underlying physics of electromechanical coupling at the nanoshell to some extent.展开更多
Deep mixing,also known as deep stabilization,is a widely used ground improvement method in Nordic countries,particularly in urban and infrastructural projects,aiming to enhance the properties of soft,sensitive clays.U...Deep mixing,also known as deep stabilization,is a widely used ground improvement method in Nordic countries,particularly in urban and infrastructural projects,aiming to enhance the properties of soft,sensitive clays.Understanding the shear strength of stabilized soils and identifying key influencing factors are essential for ensuring the structural stability and durability of engineering structures.This study introduces a novel explainable artificial intelligence framework to investigate critical soil properties affecting shear strength,utilizing a data set derived from stabilization tests conducted on laboratory samples from the 1990s.The proposed framework investigates the statistical variability and distribution of crucial parameters affecting shear strength within the collected data set.Subsequently,machine learning models are trained and tested to predict soil shear strength based on input features such as water/binder ratio and water content,etc.Global model analysis using feature importance and Shapley additive explanations is conducted to understand the influence of soil input features on shear strength.Further exploration is carried out using partial dependence plots,individual conditional expectation plots,and accumulated local effects to uncover the degree of dependency and important thresholds between key stabilized soil parameters and shear strength.Heat map and feature interaction analysis techniques are then utilized to investigate soil properties interactions and correlations.Lastly,a more specific investigation is conducted on particular soil samples to highlight the most influential soil properties locally,employing the local interpretable model-agnostic explanations technique.The validation of the framework involves analyzing laboratory test results obtained from uniaxial compression tests.The framework demonstrates an ability to predict the shear strength of stabilized soil samples with an accuracy surpassing 90%.Importantly,the explainability results underscore the substantial impact of water content and the water/binder ratio on shear strength.展开更多
Sustainable development in the concrete industry necessitates a standardized framework for material development,despite promising experimental results.High-volume fly ash(HVFA)self-compacting concrete’s(SCC)strength ...Sustainable development in the concrete industry necessitates a standardized framework for material development,despite promising experimental results.High-volume fly ash(HVFA)self-compacting concrete’s(SCC)strength characteristics are investigated in this study through the use of sophisticated modeling techniques such as random forest(RF),RF-particle swarm optimization,RF-Bayesian optimization,and RF-differential evolution(RF-DE).Cement was partially replaced with HVFA and silica fume(SF),enhancing fresh and hardened concrete properties such as compressive and split-tensile strengths,passing ability,and filler capacity.Input parameters included cement,SF,fly ash,T-500-time,maximum spread diameter,L-box blocking ratio,J-ring test,V-funnel time,and age.Statistical tools like uncertainty analysis,SHapley Additive exPlanations,and regression error characteristic curves validated the models.The RF-DE model showed the best predictive accuracy among them.Machine learning(ML)is great at predicting compressive strength(CS),but SCC-mix engineers have a hard time understanding it because of its“black-box”nature.To address this,an open-source graphical user interface based on RF-DE was developed,offering precise CS predictions for diverse mix conditions.This user-friendly tool empowers engineers to optimize mix proportions,supporting sustainable concrete design and facilitating the practical application of ML in the industry.展开更多
A robust analytical model of Eccentric Braced Frames (EBFs), as a well-known seismic resistance system, helps to comprehensive earthquake-induced risk assessment of buildings in different performance levels. Recently,...A robust analytical model of Eccentric Braced Frames (EBFs), as a well-known seismic resistance system, helps to comprehensive earthquake-induced risk assessment of buildings in different performance levels. Recently, the modeling parameters have been introduced to simulate the hysteretic behavior of shear links in EBFs with specific Coefficient of Variation associated with each parameter to consider the uncertainties. The main purpose of this paper is to assess the effect of these uncertainties in the seismic response of EBFs by combining different sources of aleatory and epistemic uncertainties while making a balance between the required computational effort and the accuracy of the responses. This assessment is carried out in multiple performance levels using Endurance Time (ET) method as an efficient Nonlinear Time History Analysis. To demonstrate the method, a 4-story EBF that considers behavioral parameters has been considered. First, a sensitivity analysis using One-Variable-At-a-Time procedure and the ET method has been utilized to sort the parameters with regard to their importance in seismic responses in two intensity levels. A sampling-based reliability method is first used to propagate the modeling uncertainties into the fragility curves of the structure. Radial Basis Function Networks are then utilized to estimate the structural responses, which makes it feasible to propagate the uncertainties with an affordable computational effort. The Design of Experiments technique is implemented to acquire the training data, reducing the required data. The results show that the mathematical relationships defined by Artificial Neural Networks and using the ET method can estimate the median Intensity Measures and shifts in dispersions with acceptable accuracy.展开更多
Significant damage to structures has been observed in several major seismic events within the Himalayan region recently,highlighting the need for further investigation into their potential vulnerability.While building...Significant damage to structures has been observed in several major seismic events within the Himalayan region recently,highlighting the need for further investigation into their potential vulnerability.While building codes are frequently improved especially after a huge earthquake disaster,existing structures remain susceptible and should be retrofitted to enhance their performance and decrease vulnerability.This study aims to endorse public safety and wellbeing by lowering the potential risk of casualties and fatalities resulting from earthquakes effects on existing reinforced concrete(RC)structures,especially in the Himalayan region.The goal is to assess the seismic vulnerability of RC structures and to identify a suitable retrofit solution using a multi-faceted approach,where the impact of the retrofit solution is estimated,based on reducing the seismic vulnerability,retrofit cost,and carbon dioxide(CO_(2))emission.A multi-story RC frame structure is a case study built in the seismically prone Himalayan region.Various indicators are employed in this study to evaluate the seismic vulnerability of the building including collapse fragility functions,vulnerability index(VI)based on capacity spectrum method,and other soft-story related parameters such as story shear,inter-story drift,plastic hinge mechanism,damage state,and stress history in soft-story columns,in assessing how seismic retrofitting affects structural performance.Four different retrofitting scenarios are considered to reduce the vulnerability of the existing structure so that the optimized one can be selected based on the proposed multi-faceted approach.This study focuses solely on retrofitting ground story columns,as it is expected to have a minimal economic,social,and environmental impact,making it an easy choice for decision-makers to implement.Finally,the costeffectiveness is quantified based on the retrofit cost and global warming potential of considered retrofit materials,and the optimization of retrofitting strategies based on the proposed multi-faceted approach,using VI,retrofit cost,and CO_(2) emission.展开更多
The surface chloride concentration of concrete is a critical factor in determining the service life of concrete in tidal environments. This study aims to identify an effective Machine Learning (ML) model for predictin...The surface chloride concentration of concrete is a critical factor in determining the service life of concrete in tidal environments. This study aims to identify an effective Machine Learning (ML) model for predicting and assessing surface chloride concentration in such conditions. Using a database that includes 12 input variables and 386 samples of surface chloride concentration in seawater-exposed concrete, the study evaluates the predictive performance of nine ML models. Among these models, the Gradient Boosting (GB) model, using default hyperparameters, demonstrates the best performance, achieving a coefficient of determination (R2) of 0.920 and a root mean square error of 0.103% by weight of concrete for the testing data set. Furthermore, an Excel file based on the GB model is created to estimate surface chloride concentration, simplifying the mix design process according to concrete durability requirements. The Shapley additive explanation values and partial dependence plot one dimension offer a detailed analysis of the impact of the 12 variables on surface chloride concentration. The four most influential factors are, in descending order, fine aggregate content, exposure time, annual mean temperature, and coarse aggregate content. Specifically, surface chloride concentration increases linearly with prolonged exposure time, stabilizing after a certain period, while higher fine aggregate content leads to a reduction in surface chloride concentration.展开更多
The failure risk of defected reinforced concrete(RC)beams is considered a potential threat.This risk is experimentally identified,numerically analyzed,and thoroughly diminished to enhance structural safety and sustain...The failure risk of defected reinforced concrete(RC)beams is considered a potential threat.This risk is experimentally identified,numerically analyzed,and thoroughly diminished to enhance structural safety and sustainability to mitigate the potential for structural collapse during construction.This research investigates the efficacy of an external post-tensioning mechanism in enhancing the behavior of defected RC beams lacking shear reinforcement,employing both experimental and numerical approaches.Fourteen RC beams were tested to evaluate the impact of posttensioning force levels and the inclination angle of post-tensioning bars.The study found that regardless of force magnitude or angle,post-tensioning improved the failure characteristics of the non-stirrup beam.The failure mode transitioned from brittle to ductile,resulting in a more advantageous distribution of cracks.Reinforced beams exhibited increased cracking and ultimate loads,with the enhancement more pronounced at higher post-tensioning force levels.Inclined post-tensioning at angles of 75°,60°,and 45°demonstrated substantial enhancement in cracking and ultimate loads,as well as elastic stiffness.The findings highlighted the superiority of inclined post-tensioning configurations,especially at 60°,for reinforced beams.Moreover,the study revealed a significant increase in absorbed energy with the proposed strengthening system.Additionally,finite element modelling(FEM)was used to replicate the tested beams.FEM accurately predicted the crack development,ultimate capacity,and deformation,aligning well with experimental observations.展开更多
Detection of cracks in concrete structures is critical for their safety and the sustainability of maintenance processes.Traditional inspection techniques are costly,time-consuming,and inefficient regarding human resou...Detection of cracks in concrete structures is critical for their safety and the sustainability of maintenance processes.Traditional inspection techniques are costly,time-consuming,and inefficient regarding human resources.Deep learning architectures have become more widespread in recent years by accelerating these processes and increasing their efficiency.Deep learning models(DLMs)stand out as an effective solution in crack detection due to their features such as end-to-end learning capability,model adaptation,and automatic learning processes.However,providing an optimal balance between model performance and computational efficiency of DLMs is a vital research topic.In this article,three different methods are proposed for detecting cracks in concrete structures.In the first method,a Separable Convolutional with Attention and Multi-layer Enhanced Fusion Network(SCAMEFNet)deep neural network,which has a deep architecture and can provide a balance between the depth of DLMs and model parameters,has been developed.This model was designed using a convolutional neural network,multi-head attention,and various fusion techniques.The second method proposes a modified vision transformer(ViT)model.A two-stage ensemble learning model,deep featurebased two-stage ensemble model(DFTSEM),is proposed in the third method.In this method,deep features and machine learning methods are used.The proposed approaches are evaluated using the Concrete Cracks Image Data set,which the authors collected and contains concrete cracks on building surfaces.The results show that the SCAMEFNet model achieved an accuracy rate of 98.83%,the ViT model 97.33%,and the DFTSEM model 99.00%.These findings show that the proposed techniques successfully detect surface cracks and deformations and can provide practical solutions to realworld problems.In addition,the developed methods can contribute as a tool for BIM platforms in smart cities for building health.展开更多
Traditional evaluation of reinforced rebar in concrete elements involves destructive methods that may harm the building.This paper introduces a framework that adopts non-destructive techniques to classify rebar in rei...Traditional evaluation of reinforced rebar in concrete elements involves destructive methods that may harm the building.This paper introduces a framework that adopts non-destructive techniques to classify rebar in reinforced concrete elements.The framework integrates Ground Penetrating Radar(GPR)with deep learning to automate rebar detection and analysis in concrete elements.The framework consists of four stages:Data sets Creation,Data sets Processing,Steel Rebar Detection Model,and Transfer Learning.Different deep learning models are tested to choose the highest-performing model.The YOLO v8 model outperforms Faster R-CNN and YOLO v7.The selected YOLO v8 model is trained on experimental and site data and then tested on real data from the building to validate the model’s accuracy and ability to classify rebar diameter.Integrating GPR with deep learning can potentially improve the accuracy and efficiency of rebar detection in structural assessments.展开更多
To investigate the long-term performance of a 32 m prestressed simply supported box girder,a 1:4 scale prestressed concrete simple supported box girder was cast.The casting procedure adheres to the principle of stress...To investigate the long-term performance of a 32 m prestressed simply supported box girder,a 1:4 scale prestressed concrete simple supported box girder was cast.The casting procedure adheres to the principle of stress equivalence within the concrete in the middle span after tensioning of prestressed tendons.Utilizing the CEB-FIP 90 model as a foundation,we established a long-term deformation calculation model for the box girder.Subsequently,the reliability of the long-term deformation model was confirmed by employing data from a 96 d long-term deformation test conducted on the box girder.Meanwhile,a new database was created by integrating shrinkage and creep experiment data with the shrinkage and creep database developed by Bazant.The shrinkage and creep uncertainty coefficients were introduced to complete the modeling of concrete shrinkage creep uncertainty calculations.The results demonstrate that the long-term deformation prediction model can effectively characterize the tendency of the mid-span upward deflection in the box girder.At 988 d,the upward deflection at the mid-span of the 1:4 scale model was expected to reach approximately 3.67 mm.It is worth noting that the CEB-FIP 90 model tends to slightly overestimate long-term deformation compared with experimental results.Additionally,it significantly underestimates the shrinkage strain observed in the test results.The uncertainty associated with the long-term deformation prediction of the structural system increased as the prediction time extended.展开更多
This study addresses the application of advanced boosting-based ensemble machine learning techniques such as extreme gradient boosting(XGBoost),random forest(RF),category-aware gradient boosting(CATBoost),and adaptive...This study addresses the application of advanced boosting-based ensemble machine learning techniques such as extreme gradient boosting(XGBoost),random forest(RF),category-aware gradient boosting(CATBoost),and adaptive boosting(ADABoost)algorithms to study the bond behavior of fiber-reinforced polymer(FRP)bars in reinforced concrete(RC)beams.To forecast the peak load(P_(max))of the bond behavior between the FRP bars and concrete,five total input variables,namely,the elastic modulus of the bar(E_(f)),the tensile strength of the bar(F_(f)),the compressive strength of the concrete(f_(c)'),the diameter of the bar(d_(b)),and the bar embedment length(l_(b)),were selected for machine learning model construction.The accuracy of the constructed predictive machine learning models was compared using several metric performances.However,rank analysis has also been used to ascertain which models perform the best.According to the findings of rank analysis using several metric performances,XGBoost outperformed RF,ADABoost,and CATBoost.Utilizing the developed advanced machine learning methods to examine the bond behavior of FRP bars in RC beams yields tangible advantages for the construction sector.This approach refines the design precision,minimizes expenses,and elevates the overall effectiveness and longevity of structures reinforced with FRP.展开更多
Corrosion significantly impacts the integrity of steel structures,making them more prone to damage and failure.Coating the steel surface with anti-corrosion paint is a prevalent method.Nevertheless,these coatings are ...Corrosion significantly impacts the integrity of steel structures,making them more prone to damage and failure.Coating the steel surface with anti-corrosion paint is a prevalent method.Nevertheless,these coatings are susceptible to damage,and corrosion tends to initiate at and spread from the damaged points,potentially leading to severe localized deterioration.Accurately predicting the progression of corrosion and coating deterioration at these critical points is essential for effective maintenance of steel structures.This study focused on two different paint-coatings applied to SM400 steel,onto which defects of varied sizes and shapes were artificially induced to mimic real-world paint-coating damage.These specimens underwent the accelerated corrosion test(ISO 16539 Method B)to generate data on corrosion depth at various time intervals.Subsequently,a modified generative adversarial network(GAN)model was employed to develop a highly accurate prediction model for the deterioration of steel surfaces,where the inputs to the model are four sequential corrosion depth measurements,and the output is the predicted future corrosion depth distribution.The performance of the proposed model was quantitatively evaluated using the root mean square error(RMSE).The model demonstrated outstanding predictive accuracy across all defect scenarios presented in this study.Compared with both traditional GAN variants(Conditional GAN and Information Maximizing GAN),the proposed model demonstrated a lower RMSE in predictive accuracy.This finding underscores its capability for precise corrosion prediction in steel structures,even with a relatively small data set.This predictive capability holds significant potential for predictive maintenance and failure analysis in steel infrastructure.This study not only validates the use of GAN in predictive maintenance but also provides a novel approach for the early detection and management of corrosion,crucial for extending the lifespan of critical infrastructure.展开更多
Steel fiber reinforced concrete-reinforced concrete(SFRC-RC)composite linings are popular in shield tunnel construction due to exceptional strength and waterproofing properties.Non-destructive testing methods are esse...Steel fiber reinforced concrete-reinforced concrete(SFRC-RC)composite linings are popular in shield tunnel construction due to exceptional strength and waterproofing properties.Non-destructive testing methods are essential for assessing the quality of these linings and ensuring tunnel construction safety.This study investigates the potential and parameters of ground penetrating radar(GPR)detection for the composite linings,using the Deep Tunnel Sewerage System-Phase 2 project in Singapore as a case study.The gprMax simulations incorporated the random distribution and precise parameters of steel fibers to conduct preliminary frequency selection studies.The structural setup of the model experiments mirrored that of the actual tunnel,allowing for an analysis of GPR penetration capabilities at various frequencies.Field testing provided authentic GPR data,validating conclusions drawn from simulations and model experiments and examining GPR power attenuation patterns.Findings indicate that GPR is effective for the quality detection of composite linings.The optimal frequency for detecting SFRC-RC composite linings is 300 MHz,which resolves the interfaces of different layered media.Based on single-parameter exponential and power function fitting,empirical formulas for power attenuation quantitatively characterize GPR signal attenuation in SFRC-RC composite linings.This paper offers valuable references for GPR detection of SFRC-RC composite linings.展开更多
The recurring occurrence of seismic hazards constitutes a significant and imminent threat to subway stations.Consequently,a meticulous assessment of the seismic resilience of subway stations becomes imperative for enh...The recurring occurrence of seismic hazards constitutes a significant and imminent threat to subway stations.Consequently,a meticulous assessment of the seismic resilience of subway stations becomes imperative for enhancing urban safety and ensuring sustained functionality.This study strives to introduce a probabilistic framework tailored to assess the seismic resilience of stations when confronted with seismic hazards.The framework aims to precisely quantify station resilience by determining the integral ratio between the station performance curve and the corresponding station recovery time.To achieve this goal,a series of finite element models of the soil-station system were developed and employed to investigate the impact of site type,seismic intensity,and station structural type on the dynamic response of the station.Then,the seismic fragility functions were generated by developing the relationships between seismic intensity and damage index,taking into account multidimensional uncertainties encompassing factors such as earthquake characteristics and construction quality.The resilience assessment was subsequently conducted based on the station’s fragility and the corresponding economic loss,while also considering the recovery path and recoverability.Additionally,the impacts of diverse factors,including structural characteristics,site types,functional recovery models,and peak ground acceleration(PGA)intensities,on the resilience of stations with distinct structural forms were also discussed.This work contributes to the resilience-based design and management of metro networks to support adaptation to seismic hazards,thereby facilitating the efficient allocation of resources by relevant decision makers.展开更多
While creating structural model,it is required that evaluation different and various alternative scenarios to provide sustainable conditions for the environment,and nature besides that structures have characteristics ...While creating structural model,it is required that evaluation different and various alternative scenarios to provide sustainable conditions for the environment,and nature besides that structures have characteristics as strength and serviceability.However,this process needs extremely long times together with much effort to find out the desired properties.Concordantly,optimization technologies can be evaluated to use in overcoming the mentioned disadvantages.Regarding this,in this study,reinforced concrete cylindrical wall was dealt for generating an optimum structure by providing cost-minimization besides making possible eco-friendly design conditions.The best structural models were also evaluated according to variable concrete strengths and wall heights in separate cases as single and multi-objective ones.Meanwhile,a metaheuristic method as flower pollination algorithm was handled to detect the best values of structural parameters including total reinforcement and concrete amount,appropriate spacing between reinforcements,etc.Also,a different optimization methodology was applied for reinforced concrete structures in order to evaluate different aims,like both sustainability and economic conditions,besides independent objectives.In this respect,the minimum cost,and CO_(2) can be determined together for different structural parameters like concrete compressive strength,wall height,etc.By this regard,multi-objective optimization processes can be utilized to investigate different structural models in order to focus on fundamental purposes like minimum cost,and emission values besides maximum seismic safety of structures.展开更多
Site effects study has always been a key research topic in earthquake engineering.This study proposes a hybrid method to analyze large-scale three-dimensional sedimentary basin under Rayleigh(R)wave incidence.The prop...Site effects study has always been a key research topic in earthquake engineering.This study proposes a hybrid method to analyze large-scale three-dimensional sedimentary basin under Rayleigh(R)wave incidence.The proposed hybrid method includes two steps:1)calculate the free field responses of layered sites subjected to R-wave using the frequency-wavenumber method;2)Simulate the local site region using spectral element method with the equivalent forces input computed from the free field responses.A comprehensive verification study is conducted demonstrating the accuracy of this method.To investigate the effect of sedimentary basin on R-wave propagation,a parametric study is performed on the medium impedance contrast ratio of sedimentary basins and the incident seismic wave predominant frequency,revealing the scattering patterns of sedimentary basins under R-wave incidence.Finally,a practical case of the Wudu Basin in the Tibetan Plateau region of China is simulated.Results indicate significant amplification of R-wave by sedimentary basin,and the proposed hybrid method could serve as a reliable and efficient approach for large-scale R-wave propagation simulation.展开更多
Underground group tanks(UGTs)for edible oil offer benefits in land conservation,ecological sustainability,and oil quality preservation.However,ensuring their structural integrity is a critical concern.This study inves...Underground group tanks(UGTs)for edible oil offer benefits in land conservation,ecological sustainability,and oil quality preservation.However,ensuring their structural integrity is a critical concern.This study investigates the mechanical behavior and stability of tank walls with inner steel plate lining in the empty tank,employing both full-scale tests and numerical simulations.Parameters such as internal forces,circumferential deformation,and wall stability under earth pressure were comprehensively examined.Findings reveal that the circumferential internal forces in walls proximal to the junction are more influenced by the junction and adjacent tank walls than those in walls located further away.The numerical results deviate by only 7.7%and 13.3%from the experimental results,verifying the efficacy and accuracy of the numerical model employed.Additionally,it was determined that for tank walls with heights below 5 m,the internal force can be computed using retaining wall force calculations;for greater heights,arch action force calculations are more suitable.Based on stability analysis,a formula for assessing the stability of double-layer,heterogeneous material group tank walls under earth pressure is introduced.It is advised that the thickness of the concrete tank wall should exceed 150 mm to ensure structural stability.These findings offer valuable insights into the rational design of UGTs.展开更多
3D printed concrete undergoes compressive deformation when printed fresh, often overlooked by traditional methods, impacting buildability prediction accuracy. In this paper, the buildability prediction model is modifi...3D printed concrete undergoes compressive deformation when printed fresh, often overlooked by traditional methods, impacting buildability prediction accuracy. In this paper, the buildability prediction model is modified by incorporating the Mohr–Coulomb damage criterion and focusing on the compressive deformation during the printing process. The prediction model combines the following key components: 1) the utilization of bilinear stress−time loading curves to simulate nonlinear stress−time loading curves during the actual printing process;2) conducting uniaxial unconfined compression tests on cylindrical fresh specimens with different aspect ratios (ranging from 0.25 to 2) to extract the stress–strain response of the material;3) the refinement of material parameters (including elastic modulus and plastic yield stress) and their variations with aspect ratio derived from the uniaxial unconfined tests. The material experimentation results indicate that the green strength exponentially decreases with increasing aspect ratio, while Young’s modulus exhibits a linear increase with the same parameter. Experimental comparisons were made during hollow drum printing tests using two different printing materials against the Mohr–Coulomb buildability prediction model. The results from these experiments demonstrate the improved accuracy of the new model in predicting failure heights (with relative error rates of 5.4% and 10.5%).展开更多
This study focuses on a reasonable lateral isolation system for a typical long-span single-tower cable-stayed bridge with a significantly asymmetric span arrangement that is particularly suitable for mountainous areas...This study focuses on a reasonable lateral isolation system for a typical long-span single-tower cable-stayed bridge with a significantly asymmetric span arrangement that is particularly suitable for mountainous areas. Based on the Jinsha River Bridge, the significant structural asymmetry and its effects on structural seismic responses were analyzed. The significantly asymmetric characteristics could result in complex dynamic behavior in seismic conditions and the lateral seismic responses of the structure are governed by multiple modes. A multilinear model composed of an ideal elastoplastic element and a multilinear elastic element was used to simulate different hysteresis, and a parametric analysis was conducted to investigate the appropriate damping hysteresis for the lateral seismic isolation of such a bridge. It shows that the inverted S-shaped hysteresis has relatively smaller secant stiffness and could help to balance the great difference in the lateral stiffness of the tower/piers. Thus, the inverted S-shaped hysteresis could lead to more efficient damping effects and less base shear forces of the tower/piers. A correlation between the reasonable yield forces of the dampers in the lateral isolation system, determined through an influence matrix-based method, and the shear forces of the corresponding bearings in the lateral fixed system was also observed. Moreover, the influence of geological conditions including different terrain and site conditions on the reasonable lateral isolation system was further investigated. It suggests to use dampers at all tower/pier locations when the side span crosses a steep valley slope, while a lateral isolation system without using dampers at the auxiliary piers could be employed when the side span crosses a gentle valley slope. Soft sites require larger damper yield forces and cause greater seismic responses compared to hard sites.展开更多
文摘Stainless-steel provides substantial advantages for structural uses,though its upfront cost is notably high.Consequently,it’s vital to establish safe and economically viable design practices that enhance material utilization.Such development relies on a thorough understanding of the mechanical properties of structural components,particularly connections.This research advances the field by investigating the behavior of stainless-steel connections through the use of a four-parameter fitting technique and explainable artificial intelligence methods.Training was conducted on eight different machine learning algorithms,namely,Decision Tree,Random Forest,K-nearest neighbors,Gradient Boosting,Extreme Gradient Boosting,Light Gradient Boosting,Adaptive Boosting,and Categorical Boosting.SHapley Additive Explanations was applied to interpret model predictions,highlighting features like spacing between bolts in tension and end-plate height as highly impactful on the initial rotational stiffness and plastic moment resistance.Results showed that Extreme Gradient Boosting achieved a coefficient of determination score of 0.99 for initial stiffness and plastic moment resistance,while Gradient Boosting model had similar performance with maximum moment resistance and ultimate rotation.A user-friendly graphical user interface(GUI)was also developed,allowing engineers to input parameters and get rapid moment–rotation predictions.This framework offers a data-driven,interpretable alternative to conventional methods,supporting future design recommendations for stainless-steel beam-to-column connections.
文摘In addition to accounting for non-gradient nonlocal elastic stress,a nonlocal strain gradient theory(NSGT)also considers the nonlocality of higher-order strain gradients;thus,it is applicable to small-scale structures and can account for both hardening and softening effects.An analytical model is constructed in this research endeavor to depict the free vibration characteristics of sandwich functionally graded porous(FGP)doubly-curved nanoshell integrated with piezoelectric surface layers consists of three distinct layers,taking into account flexoelectrici effect based on NSGT and novel refined high-order shear deformation hypothesis.The novelty of this study is that the two nonlocal coefficients and material length scale of the core layer are variable along thickness,like other material characteristics.The equilibrium equation of motion of the doubly-curved nanoshell is derived from Hamilton’s principle,then the Galerkin method is applied to derive the natural vibration frequency values of the doubly-curved nanoshell with different boundary conditions(BCs).The influence of parameters such as flexoelectric effect,nonlocal and length scale factors,elastic medium stiffness factor,porosity factor,and BCs on the free vibration esponse of the nanoshell is detected and comprehensively studied.This paper is claimed to provide a theoretical predicition on the impact of the size-small dependent and flexoelectric effect upon the oscillation of FGP nanoshell integrated with piezoelectric surface layers,thus sheding light on understanding the underlying physics of electromechanical coupling at the nanoshell to some extent.
文摘Deep mixing,also known as deep stabilization,is a widely used ground improvement method in Nordic countries,particularly in urban and infrastructural projects,aiming to enhance the properties of soft,sensitive clays.Understanding the shear strength of stabilized soils and identifying key influencing factors are essential for ensuring the structural stability and durability of engineering structures.This study introduces a novel explainable artificial intelligence framework to investigate critical soil properties affecting shear strength,utilizing a data set derived from stabilization tests conducted on laboratory samples from the 1990s.The proposed framework investigates the statistical variability and distribution of crucial parameters affecting shear strength within the collected data set.Subsequently,machine learning models are trained and tested to predict soil shear strength based on input features such as water/binder ratio and water content,etc.Global model analysis using feature importance and Shapley additive explanations is conducted to understand the influence of soil input features on shear strength.Further exploration is carried out using partial dependence plots,individual conditional expectation plots,and accumulated local effects to uncover the degree of dependency and important thresholds between key stabilized soil parameters and shear strength.Heat map and feature interaction analysis techniques are then utilized to investigate soil properties interactions and correlations.Lastly,a more specific investigation is conducted on particular soil samples to highlight the most influential soil properties locally,employing the local interpretable model-agnostic explanations technique.The validation of the framework involves analyzing laboratory test results obtained from uniaxial compression tests.The framework demonstrates an ability to predict the shear strength of stabilized soil samples with an accuracy surpassing 90%.Importantly,the explainability results underscore the substantial impact of water content and the water/binder ratio on shear strength.
文摘Sustainable development in the concrete industry necessitates a standardized framework for material development,despite promising experimental results.High-volume fly ash(HVFA)self-compacting concrete’s(SCC)strength characteristics are investigated in this study through the use of sophisticated modeling techniques such as random forest(RF),RF-particle swarm optimization,RF-Bayesian optimization,and RF-differential evolution(RF-DE).Cement was partially replaced with HVFA and silica fume(SF),enhancing fresh and hardened concrete properties such as compressive and split-tensile strengths,passing ability,and filler capacity.Input parameters included cement,SF,fly ash,T-500-time,maximum spread diameter,L-box blocking ratio,J-ring test,V-funnel time,and age.Statistical tools like uncertainty analysis,SHapley Additive exPlanations,and regression error characteristic curves validated the models.The RF-DE model showed the best predictive accuracy among them.Machine learning(ML)is great at predicting compressive strength(CS),but SCC-mix engineers have a hard time understanding it because of its“black-box”nature.To address this,an open-source graphical user interface based on RF-DE was developed,offering precise CS predictions for diverse mix conditions.This user-friendly tool empowers engineers to optimize mix proportions,supporting sustainable concrete design and facilitating the practical application of ML in the industry.
文摘A robust analytical model of Eccentric Braced Frames (EBFs), as a well-known seismic resistance system, helps to comprehensive earthquake-induced risk assessment of buildings in different performance levels. Recently, the modeling parameters have been introduced to simulate the hysteretic behavior of shear links in EBFs with specific Coefficient of Variation associated with each parameter to consider the uncertainties. The main purpose of this paper is to assess the effect of these uncertainties in the seismic response of EBFs by combining different sources of aleatory and epistemic uncertainties while making a balance between the required computational effort and the accuracy of the responses. This assessment is carried out in multiple performance levels using Endurance Time (ET) method as an efficient Nonlinear Time History Analysis. To demonstrate the method, a 4-story EBF that considers behavioral parameters has been considered. First, a sensitivity analysis using One-Variable-At-a-Time procedure and the ET method has been utilized to sort the parameters with regard to their importance in seismic responses in two intensity levels. A sampling-based reliability method is first used to propagate the modeling uncertainties into the fragility curves of the structure. Radial Basis Function Networks are then utilized to estimate the structural responses, which makes it feasible to propagate the uncertainties with an affordable computational effort. The Design of Experiments technique is implemented to acquire the training data, reducing the required data. The results show that the mathematical relationships defined by Artificial Neural Networks and using the ET method can estimate the median Intensity Measures and shifts in dispersions with acceptable accuracy.
文摘Significant damage to structures has been observed in several major seismic events within the Himalayan region recently,highlighting the need for further investigation into their potential vulnerability.While building codes are frequently improved especially after a huge earthquake disaster,existing structures remain susceptible and should be retrofitted to enhance their performance and decrease vulnerability.This study aims to endorse public safety and wellbeing by lowering the potential risk of casualties and fatalities resulting from earthquakes effects on existing reinforced concrete(RC)structures,especially in the Himalayan region.The goal is to assess the seismic vulnerability of RC structures and to identify a suitable retrofit solution using a multi-faceted approach,where the impact of the retrofit solution is estimated,based on reducing the seismic vulnerability,retrofit cost,and carbon dioxide(CO_(2))emission.A multi-story RC frame structure is a case study built in the seismically prone Himalayan region.Various indicators are employed in this study to evaluate the seismic vulnerability of the building including collapse fragility functions,vulnerability index(VI)based on capacity spectrum method,and other soft-story related parameters such as story shear,inter-story drift,plastic hinge mechanism,damage state,and stress history in soft-story columns,in assessing how seismic retrofitting affects structural performance.Four different retrofitting scenarios are considered to reduce the vulnerability of the existing structure so that the optimized one can be selected based on the proposed multi-faceted approach.This study focuses solely on retrofitting ground story columns,as it is expected to have a minimal economic,social,and environmental impact,making it an easy choice for decision-makers to implement.Finally,the costeffectiveness is quantified based on the retrofit cost and global warming potential of considered retrofit materials,and the optimization of retrofitting strategies based on the proposed multi-faceted approach,using VI,retrofit cost,and CO_(2) emission.
文摘The surface chloride concentration of concrete is a critical factor in determining the service life of concrete in tidal environments. This study aims to identify an effective Machine Learning (ML) model for predicting and assessing surface chloride concentration in such conditions. Using a database that includes 12 input variables and 386 samples of surface chloride concentration in seawater-exposed concrete, the study evaluates the predictive performance of nine ML models. Among these models, the Gradient Boosting (GB) model, using default hyperparameters, demonstrates the best performance, achieving a coefficient of determination (R2) of 0.920 and a root mean square error of 0.103% by weight of concrete for the testing data set. Furthermore, an Excel file based on the GB model is created to estimate surface chloride concentration, simplifying the mix design process according to concrete durability requirements. The Shapley additive explanation values and partial dependence plot one dimension offer a detailed analysis of the impact of the 12 variables on surface chloride concentration. The four most influential factors are, in descending order, fine aggregate content, exposure time, annual mean temperature, and coarse aggregate content. Specifically, surface chloride concentration increases linearly with prolonged exposure time, stabilizing after a certain period, while higher fine aggregate content leads to a reduction in surface chloride concentration.
文摘The failure risk of defected reinforced concrete(RC)beams is considered a potential threat.This risk is experimentally identified,numerically analyzed,and thoroughly diminished to enhance structural safety and sustainability to mitigate the potential for structural collapse during construction.This research investigates the efficacy of an external post-tensioning mechanism in enhancing the behavior of defected RC beams lacking shear reinforcement,employing both experimental and numerical approaches.Fourteen RC beams were tested to evaluate the impact of posttensioning force levels and the inclination angle of post-tensioning bars.The study found that regardless of force magnitude or angle,post-tensioning improved the failure characteristics of the non-stirrup beam.The failure mode transitioned from brittle to ductile,resulting in a more advantageous distribution of cracks.Reinforced beams exhibited increased cracking and ultimate loads,with the enhancement more pronounced at higher post-tensioning force levels.Inclined post-tensioning at angles of 75°,60°,and 45°demonstrated substantial enhancement in cracking and ultimate loads,as well as elastic stiffness.The findings highlighted the superiority of inclined post-tensioning configurations,especially at 60°,for reinforced beams.Moreover,the study revealed a significant increase in absorbed energy with the proposed strengthening system.Additionally,finite element modelling(FEM)was used to replicate the tested beams.FEM accurately predicted the crack development,ultimate capacity,and deformation,aligning well with experimental observations.
文摘Detection of cracks in concrete structures is critical for their safety and the sustainability of maintenance processes.Traditional inspection techniques are costly,time-consuming,and inefficient regarding human resources.Deep learning architectures have become more widespread in recent years by accelerating these processes and increasing their efficiency.Deep learning models(DLMs)stand out as an effective solution in crack detection due to their features such as end-to-end learning capability,model adaptation,and automatic learning processes.However,providing an optimal balance between model performance and computational efficiency of DLMs is a vital research topic.In this article,three different methods are proposed for detecting cracks in concrete structures.In the first method,a Separable Convolutional with Attention and Multi-layer Enhanced Fusion Network(SCAMEFNet)deep neural network,which has a deep architecture and can provide a balance between the depth of DLMs and model parameters,has been developed.This model was designed using a convolutional neural network,multi-head attention,and various fusion techniques.The second method proposes a modified vision transformer(ViT)model.A two-stage ensemble learning model,deep featurebased two-stage ensemble model(DFTSEM),is proposed in the third method.In this method,deep features and machine learning methods are used.The proposed approaches are evaluated using the Concrete Cracks Image Data set,which the authors collected and contains concrete cracks on building surfaces.The results show that the SCAMEFNet model achieved an accuracy rate of 98.83%,the ViT model 97.33%,and the DFTSEM model 99.00%.These findings show that the proposed techniques successfully detect surface cracks and deformations and can provide practical solutions to realworld problems.In addition,the developed methods can contribute as a tool for BIM platforms in smart cities for building health.
文摘Traditional evaluation of reinforced rebar in concrete elements involves destructive methods that may harm the building.This paper introduces a framework that adopts non-destructive techniques to classify rebar in reinforced concrete elements.The framework integrates Ground Penetrating Radar(GPR)with deep learning to automate rebar detection and analysis in concrete elements.The framework consists of four stages:Data sets Creation,Data sets Processing,Steel Rebar Detection Model,and Transfer Learning.Different deep learning models are tested to choose the highest-performing model.The YOLO v8 model outperforms Faster R-CNN and YOLO v7.The selected YOLO v8 model is trained on experimental and site data and then tested on real data from the building to validate the model’s accuracy and ability to classify rebar diameter.Integrating GPR with deep learning can potentially improve the accuracy and efficiency of rebar detection in structural assessments.
基金funded by the National Natural Science Foundation of China(Grant Nos.52178182,52108262,and U1934217)Science and Technology Research and Development Program Project of China Railway Group Limited(Major special project,Nos.2020-Special-02,2021-Special-08,2022-Special-09,and 2023-Major projects-07,Major project,No.2021-Special-02,Key project,Nos.2021-Key-11,and 2022-Key-46)+1 种基金the Natural Science Foundation for Distinguished Young Scholars of Hunan Province(No.2022JJ10075)Hunan Science and Technology Plan Project(No.2023SK2014).
文摘To investigate the long-term performance of a 32 m prestressed simply supported box girder,a 1:4 scale prestressed concrete simple supported box girder was cast.The casting procedure adheres to the principle of stress equivalence within the concrete in the middle span after tensioning of prestressed tendons.Utilizing the CEB-FIP 90 model as a foundation,we established a long-term deformation calculation model for the box girder.Subsequently,the reliability of the long-term deformation model was confirmed by employing data from a 96 d long-term deformation test conducted on the box girder.Meanwhile,a new database was created by integrating shrinkage and creep experiment data with the shrinkage and creep database developed by Bazant.The shrinkage and creep uncertainty coefficients were introduced to complete the modeling of concrete shrinkage creep uncertainty calculations.The results demonstrate that the long-term deformation prediction model can effectively characterize the tendency of the mid-span upward deflection in the box girder.At 988 d,the upward deflection at the mid-span of the 1:4 scale model was expected to reach approximately 3.67 mm.It is worth noting that the CEB-FIP 90 model tends to slightly overestimate long-term deformation compared with experimental results.Additionally,it significantly underestimates the shrinkage strain observed in the test results.The uncertainty associated with the long-term deformation prediction of the structural system increased as the prediction time extended.
基金supported by Thammasat University Research Unit in Structural and Foundation Engineering.
文摘This study addresses the application of advanced boosting-based ensemble machine learning techniques such as extreme gradient boosting(XGBoost),random forest(RF),category-aware gradient boosting(CATBoost),and adaptive boosting(ADABoost)algorithms to study the bond behavior of fiber-reinforced polymer(FRP)bars in reinforced concrete(RC)beams.To forecast the peak load(P_(max))of the bond behavior between the FRP bars and concrete,five total input variables,namely,the elastic modulus of the bar(E_(f)),the tensile strength of the bar(F_(f)),the compressive strength of the concrete(f_(c)'),the diameter of the bar(d_(b)),and the bar embedment length(l_(b)),were selected for machine learning model construction.The accuracy of the constructed predictive machine learning models was compared using several metric performances.However,rank analysis has also been used to ascertain which models perform the best.According to the findings of rank analysis using several metric performances,XGBoost outperformed RF,ADABoost,and CATBoost.Utilizing the developed advanced machine learning methods to examine the bond behavior of FRP bars in RC beams yields tangible advantages for the construction sector.This approach refines the design precision,minimizes expenses,and elevates the overall effectiveness and longevity of structures reinforced with FRP.
文摘Corrosion significantly impacts the integrity of steel structures,making them more prone to damage and failure.Coating the steel surface with anti-corrosion paint is a prevalent method.Nevertheless,these coatings are susceptible to damage,and corrosion tends to initiate at and spread from the damaged points,potentially leading to severe localized deterioration.Accurately predicting the progression of corrosion and coating deterioration at these critical points is essential for effective maintenance of steel structures.This study focused on two different paint-coatings applied to SM400 steel,onto which defects of varied sizes and shapes were artificially induced to mimic real-world paint-coating damage.These specimens underwent the accelerated corrosion test(ISO 16539 Method B)to generate data on corrosion depth at various time intervals.Subsequently,a modified generative adversarial network(GAN)model was employed to develop a highly accurate prediction model for the deterioration of steel surfaces,where the inputs to the model are four sequential corrosion depth measurements,and the output is the predicted future corrosion depth distribution.The performance of the proposed model was quantitatively evaluated using the root mean square error(RMSE).The model demonstrated outstanding predictive accuracy across all defect scenarios presented in this study.Compared with both traditional GAN variants(Conditional GAN and Information Maximizing GAN),the proposed model demonstrated a lower RMSE in predictive accuracy.This finding underscores its capability for precise corrosion prediction in steel structures,even with a relatively small data set.This predictive capability holds significant potential for predictive maintenance and failure analysis in steel infrastructure.This study not only validates the use of GAN in predictive maintenance but also provides a novel approach for the early detection and management of corrosion,crucial for extending the lifespan of critical infrastructure.
基金supported by the National Key R&D Program of China(No.2023YFC3806705)the National Natural Science Foundation of China(Grant Nos.52038008 and 52378408)+1 种基金the Science and Technology Innovation Plan of Shanghai Science and Technology Commission(No.22dz1203004)the Science and Technology Project of State Grid Corporation of China(No.5200-202417104A-1-1-ZN).
文摘Steel fiber reinforced concrete-reinforced concrete(SFRC-RC)composite linings are popular in shield tunnel construction due to exceptional strength and waterproofing properties.Non-destructive testing methods are essential for assessing the quality of these linings and ensuring tunnel construction safety.This study investigates the potential and parameters of ground penetrating radar(GPR)detection for the composite linings,using the Deep Tunnel Sewerage System-Phase 2 project in Singapore as a case study.The gprMax simulations incorporated the random distribution and precise parameters of steel fibers to conduct preliminary frequency selection studies.The structural setup of the model experiments mirrored that of the actual tunnel,allowing for an analysis of GPR penetration capabilities at various frequencies.Field testing provided authentic GPR data,validating conclusions drawn from simulations and model experiments and examining GPR power attenuation patterns.Findings indicate that GPR is effective for the quality detection of composite linings.The optimal frequency for detecting SFRC-RC composite linings is 300 MHz,which resolves the interfaces of different layered media.Based on single-parameter exponential and power function fitting,empirical formulas for power attenuation quantitatively characterize GPR signal attenuation in SFRC-RC composite linings.This paper offers valuable references for GPR detection of SFRC-RC composite linings.
基金supported by the National Key Research and Development Program of China(No.2021YFF0502200)Shanghai Science and Technology Committee Program(No.22dz1201202)+3 种基金the National Natural Science Foundation of China(Grants No.52408435,52478410)Natural Science Foundation of Chongqing,China(No.CSTB2023NSCQ-MSX0808)Young Elite Scientists Sponsorship Program by CAST(No.2023QNRC001)the Fundamental Research Funds for the Central Universities.
文摘The recurring occurrence of seismic hazards constitutes a significant and imminent threat to subway stations.Consequently,a meticulous assessment of the seismic resilience of subway stations becomes imperative for enhancing urban safety and ensuring sustained functionality.This study strives to introduce a probabilistic framework tailored to assess the seismic resilience of stations when confronted with seismic hazards.The framework aims to precisely quantify station resilience by determining the integral ratio between the station performance curve and the corresponding station recovery time.To achieve this goal,a series of finite element models of the soil-station system were developed and employed to investigate the impact of site type,seismic intensity,and station structural type on the dynamic response of the station.Then,the seismic fragility functions were generated by developing the relationships between seismic intensity and damage index,taking into account multidimensional uncertainties encompassing factors such as earthquake characteristics and construction quality.The resilience assessment was subsequently conducted based on the station’s fragility and the corresponding economic loss,while also considering the recovery path and recoverability.Additionally,the impacts of diverse factors,including structural characteristics,site types,functional recovery models,and peak ground acceleration(PGA)intensities,on the resilience of stations with distinct structural forms were also discussed.This work contributes to the resilience-based design and management of metro networks to support adaptation to seismic hazards,thereby facilitating the efficient allocation of resources by relevant decision makers.
文摘While creating structural model,it is required that evaluation different and various alternative scenarios to provide sustainable conditions for the environment,and nature besides that structures have characteristics as strength and serviceability.However,this process needs extremely long times together with much effort to find out the desired properties.Concordantly,optimization technologies can be evaluated to use in overcoming the mentioned disadvantages.Regarding this,in this study,reinforced concrete cylindrical wall was dealt for generating an optimum structure by providing cost-minimization besides making possible eco-friendly design conditions.The best structural models were also evaluated according to variable concrete strengths and wall heights in separate cases as single and multi-objective ones.Meanwhile,a metaheuristic method as flower pollination algorithm was handled to detect the best values of structural parameters including total reinforcement and concrete amount,appropriate spacing between reinforcements,etc.Also,a different optimization methodology was applied for reinforced concrete structures in order to evaluate different aims,like both sustainability and economic conditions,besides independent objectives.In this respect,the minimum cost,and CO_(2) can be determined together for different structural parameters like concrete compressive strength,wall height,etc.By this regard,multi-objective optimization processes can be utilized to investigate different structural models in order to focus on fundamental purposes like minimum cost,and emission values besides maximum seismic safety of structures.
基金supported by the National Natural Science Foundation of China(Grant Nos.U2139208 and 52178495).
文摘Site effects study has always been a key research topic in earthquake engineering.This study proposes a hybrid method to analyze large-scale three-dimensional sedimentary basin under Rayleigh(R)wave incidence.The proposed hybrid method includes two steps:1)calculate the free field responses of layered sites subjected to R-wave using the frequency-wavenumber method;2)Simulate the local site region using spectral element method with the equivalent forces input computed from the free field responses.A comprehensive verification study is conducted demonstrating the accuracy of this method.To investigate the effect of sedimentary basin on R-wave propagation,a parametric study is performed on the medium impedance contrast ratio of sedimentary basins and the incident seismic wave predominant frequency,revealing the scattering patterns of sedimentary basins under R-wave incidence.Finally,a practical case of the Wudu Basin in the Tibetan Plateau region of China is simulated.Results indicate significant amplification of R-wave by sedimentary basin,and the proposed hybrid method could serve as a reliable and efficient approach for large-scale R-wave propagation simulation.
基金funded input by the Fund Project of Scientific and Technological Breakthrough of Henan Province(No.242102110130)the Research Fund for the Doctoral Program of Henan University of Technology(No.2020BS044)Henan University of Technology Design and Research Academy Co.,LTD.
文摘Underground group tanks(UGTs)for edible oil offer benefits in land conservation,ecological sustainability,and oil quality preservation.However,ensuring their structural integrity is a critical concern.This study investigates the mechanical behavior and stability of tank walls with inner steel plate lining in the empty tank,employing both full-scale tests and numerical simulations.Parameters such as internal forces,circumferential deformation,and wall stability under earth pressure were comprehensively examined.Findings reveal that the circumferential internal forces in walls proximal to the junction are more influenced by the junction and adjacent tank walls than those in walls located further away.The numerical results deviate by only 7.7%and 13.3%from the experimental results,verifying the efficacy and accuracy of the numerical model employed.Additionally,it was determined that for tank walls with heights below 5 m,the internal force can be computed using retaining wall force calculations;for greater heights,arch action force calculations are more suitable.Based on stability analysis,a formula for assessing the stability of double-layer,heterogeneous material group tank walls under earth pressure is introduced.It is advised that the thickness of the concrete tank wall should exceed 150 mm to ensure structural stability.These findings offer valuable insights into the rational design of UGTs.
基金the National Natural Science Foundation of China(Grant No.52130210)the MCC17 Group Co.,Ltd.Fund(No.SQY2023CXY01).
文摘3D printed concrete undergoes compressive deformation when printed fresh, often overlooked by traditional methods, impacting buildability prediction accuracy. In this paper, the buildability prediction model is modified by incorporating the Mohr–Coulomb damage criterion and focusing on the compressive deformation during the printing process. The prediction model combines the following key components: 1) the utilization of bilinear stress−time loading curves to simulate nonlinear stress−time loading curves during the actual printing process;2) conducting uniaxial unconfined compression tests on cylindrical fresh specimens with different aspect ratios (ranging from 0.25 to 2) to extract the stress–strain response of the material;3) the refinement of material parameters (including elastic modulus and plastic yield stress) and their variations with aspect ratio derived from the uniaxial unconfined tests. The material experimentation results indicate that the green strength exponentially decreases with increasing aspect ratio, while Young’s modulus exhibits a linear increase with the same parameter. Experimental comparisons were made during hollow drum printing tests using two different printing materials against the Mohr–Coulomb buildability prediction model. The results from these experiments demonstrate the improved accuracy of the new model in predicting failure heights (with relative error rates of 5.4% and 10.5%).
基金supported by the National Natural Science Foundation of China(Grant No.52278527).
文摘This study focuses on a reasonable lateral isolation system for a typical long-span single-tower cable-stayed bridge with a significantly asymmetric span arrangement that is particularly suitable for mountainous areas. Based on the Jinsha River Bridge, the significant structural asymmetry and its effects on structural seismic responses were analyzed. The significantly asymmetric characteristics could result in complex dynamic behavior in seismic conditions and the lateral seismic responses of the structure are governed by multiple modes. A multilinear model composed of an ideal elastoplastic element and a multilinear elastic element was used to simulate different hysteresis, and a parametric analysis was conducted to investigate the appropriate damping hysteresis for the lateral seismic isolation of such a bridge. It shows that the inverted S-shaped hysteresis has relatively smaller secant stiffness and could help to balance the great difference in the lateral stiffness of the tower/piers. Thus, the inverted S-shaped hysteresis could lead to more efficient damping effects and less base shear forces of the tower/piers. A correlation between the reasonable yield forces of the dampers in the lateral isolation system, determined through an influence matrix-based method, and the shear forces of the corresponding bearings in the lateral fixed system was also observed. Moreover, the influence of geological conditions including different terrain and site conditions on the reasonable lateral isolation system was further investigated. It suggests to use dampers at all tower/pier locations when the side span crosses a steep valley slope, while a lateral isolation system without using dampers at the auxiliary piers could be employed when the side span crosses a gentle valley slope. Soft sites require larger damper yield forces and cause greater seismic responses compared to hard sites.
