A typical Whipple shield consists of double-layered plates with a certain gap.The space debris impacts the outer plate and is broken into a debris cloud(shattered,molten,vaporized)with dispersed energy and momentum,wh...A typical Whipple shield consists of double-layered plates with a certain gap.The space debris impacts the outer plate and is broken into a debris cloud(shattered,molten,vaporized)with dispersed energy and momentum,which reduces the risk of penetrating the bulkhead.In the realm of hypervelocity impact,strain rate(>10^(5)s^(-1))effects are negligible,and fluid dynamics is employed to describe the impact process.Efficient numerical tools for precisely predicting the damage degree can greatly accelerate the design and optimization of advanced protective structures.Current hypervelocity impact research primarily focuses on the interaction between projectile and front plate and the movement of debris cloud.However,the damage mechanism of debris cloud impacts on rear plates-the critical threat component-remains underexplored owing to complex multi-physics processes and prohibitive computational costs.Existing approaches,ranging from semi-empirical equations to a machine learningbased ballistic limit prediction method,are constrained to binary penetration classification.Alternatively,the uneven data from experiments and simulations caused these methods to be ineffective when the projectile has irregular shapes and complicate flight attitude.Therefore,it is urgent to develop a new damage prediction method for predicting the rear plate damage,which can help to gain a deeper understanding of the damage mechanism.In this study,a machine learning(ML)method is developed to predict the damage distribution in the rear plate.Based on the unit velocity space,the discretized information of debris cloud and rear plate damage from rare simulation cases is used as input data for training the ML models,while the generalization ability for damage distribution prediction is tested by other simulation cases with different attack angles.The results demonstrate that the training and prediction accuracies using the Random Forest(RF)algorithm significantly surpass those using Artificial Neural Networks(ANNs)and Support Vector Machine(SVM).The RF-based model effectively identifies damage features in sparsely distributed debris cloud and cumulative effect.This study establishes an expandable new dataset that accommodates additional parameters to improve the prediction accuracy.Results demonstrate the model's ability to overcome data imbalance limitations through debris cloud features,enabling rapid and accurate rear plate damage prediction across wider scenarios with minimal data requirements.展开更多
This paper presents a method for searching the weak story by using the ultimate shear force coefficient on the multi-story brick buildings with two frame-shear-wall-supported stories. The method of seismic damage pred...This paper presents a method for searching the weak story by using the ultimate shear force coefficient on the multi-story brick buildings with two frame-shear-wall-supported stories. The method of seismic damage prediction is discussed according to different weak stories. When the first story is t theweak one,the damage state of the building can be determined by the displacement ratio. The prediction method is also used in a practical engineering project.展开更多
HP40Nb steel, used as a candidate material for ethylene cracking furnace tube, suffers creep and carburization damage from the complex environment of high temperature, high carbon potential and low oxygen partial pres...HP40Nb steel, used as a candidate material for ethylene cracking furnace tube, suffers creep and carburization damage from the complex environment of high temperature, high carbon potential and low oxygen partial pressure, and they lead to failure of the furnace tubes ahead of designed life. In order to investigate damage evolution under the complex condition, coupled creep damage and carburization damage constitutive equations were developed according to continuum damage mechanics theory. Based on the finite element ABAQUS code, user subroutines were developed for analyz- ing damage evolution of ethylene furnace tube under the action of coupled creep- carburization. The results show that carburization accelerates the damage process dramatically, damage value reaches the critical value along the inner surface after serving for 75,000 h under the action of creep-carburization, meanwhile the damage value is only 0.53 along the outer surface after operating the same time under the action of creep alone, which means that microcracks are generated along the inner surface under the action of coupled creep-carburization, fracture begins along the outer surface of tube under the action of creep alone.展开更多
There are several underground mines in India which operate in close proximity to an operating surface mine.Under such scenario,the blast induced stress waves generated due to surface blasting may be a potential source...There are several underground mines in India which operate in close proximity to an operating surface mine.Under such scenario,the blast induced stress waves generated due to surface blasting may be a potential source to cause instability of adjoining underground mine structures.Using seismographs,54 blast induced vibration data were recorded at various locations in the roof,floor and pillars of the underground mine at Hingir Rampur mine of Coal India Limited by synchronizing the timing of surface blasting carried at an adjacent Samleshwari opencast mine.Results of this study show that Artificial Neural Network(ANN)has better prediction potential of peak particle velocity(PPV)and damage to adjacent underground structures due to surface blasting as compared to conventional regression methods.In order to assess and predict the impact of surface blasts on underground workings,Blast Damage Factor(BDF)has been evolved.The study shows that site specific charts can predict the blast damage class at an underground location due to surface blasting for known distances and explosive charge per delay.The severe damage in case study mine site took place when peak particle velocity exceeded 162 mm/s and PPV less than 51 mm/s had no probability of damage to underground structures due to surface blasting.展开更多
Seismic damage indices of structure are widely used to quantificationally analyze structural damage levels under earthquake action. In this paper, a five-storey building model and a seventeen-storey building model are...Seismic damage indices of structure are widely used to quantificationally analyze structural damage levels under earthquake action. In this paper, a five-storey building model and a seventeen-storey building model are established. According to seven typical indices and different earthquake-inputs, a structural damage prediction is performed, with the results showing serious uncertainty of structural damage prediction due to different indices. Understanding of this phenomenon aids the comprehension and application of the results of earthquake damage prediction.展开更多
Effective isolation between the cement sheath and the sandstone is crucial for the development and production of oil and gas wells in sandstone formations.In this study,a cement-sandstone composite(CSC)was prepared,an...Effective isolation between the cement sheath and the sandstone is crucial for the development and production of oil and gas wells in sandstone formations.In this study,a cement-sandstone composite(CSC)was prepared,and based onμ-CT three-dimensional reconstruction imaging and finite element analysis(FEA)techniques,the stress distribution and potential failure mechanism at the cement-sandstone bonding interface under axial loading were analyzed.The key findings are as follows:(1)stress concentrations are highly likely to form at the gap between the cement and sandstone interface and around interfacial voids,with Von Mises stress reaching critical levels of 18.0-20.0 MPa at these locations,significantly exceeding the stress magnitudes in well-bonded regions;(2)the phenomenon of local stress concentration driven by interfacial defects can be identified as the main basis for predicting damage location in interfacial debonding and continuous shear under axial load;(3)ensuring tight cementation at the cement-sandstone interface and minimizing interfacial voids are paramount for preventing stress-induced failure;(4)the critical Von Mises stress value of 20 MPa at the interface defect can be used as a benchmark for material selection and designed to ensure long-term integrity in oil and gas well applications subjected to similar axial loads.These findings contribute to a more accurate understanding of the failure mechanism of the cement-sandstone interface and to the precise design of material properties,thereby ensuring the long-term integrity of oil and gas well applications subjected to similar axial loads.展开更多
Strong aftershocks generally occur following a significant earthquake.Aftershocks further damage buildings weakened by mainshocks.Thus,the accurate and efficient prediction of aftershock-induced damage to buildings on...Strong aftershocks generally occur following a significant earthquake.Aftershocks further damage buildings weakened by mainshocks.Thus,the accurate and efficient prediction of aftershock-induced damage to buildings on a regional scale is crucial for decision making for post-earthquake rescue and emergency response.A framework to predict regional seismic damage of buildings under a mainshock-aftershock(MS-AS)sequence is proposed in this study based on city-scale nonlinear time-history analysis(THA).Specifically,an MS-AS sequence-generation method is proposed to generate a potential MS-AS sequence that can account for the amplification,spectrum,duration,magnitude,and site condition of a target area.Moreover,city-scale nonlinear THA is adopted to predict building seismic damage subjected to MS-AS sequences.The accuracy and reliability of city-scale nonlinear THA for an MS-AS sequence are validated by as-recorded seismic responses of buildings and simulation results in published literature.The town of Longtoushan,which was damaged during the Ludian earthquake,is used as a case study to illustrate the detailed procedure and advantages of the proposed framework.The primary conclusions are as follows.(1)Regional seismic damage of buildings under an MS-AS sequence can be predicted reasonably and accurately by city-scale nonlinear THA.(2)An MS-AS sequence can be generated reasonably by the proposed MS-AS sequencegeneration method.(3)Regional seismic damage of buildings under different MS-AS scenarios can be provided efficiently by the proposed framework,which in turn can provide a useful reference for earthquake emergency response and scientific decision making for earthquake disaster relief.展开更多
This study examines the feasibility of using a machine learning approach for rapid damage assessment of rein-forced concrete(RC)buildings after the earthquake.Since the real-world damaged datasets are lacking,have lim...This study examines the feasibility of using a machine learning approach for rapid damage assessment of rein-forced concrete(RC)buildings after the earthquake.Since the real-world damaged datasets are lacking,have limited access,or are imbalanced,a simulation dataset is prepared by conducting a nonlinear time history analy-sis.Different machine learning(ML)models are trained considering the structural parameters and ground motion characteristics to predict the RC building damage into five categories:null,slight,moderate,heavy,and collapse.The random forest classifier(RFC)has achieved a higher prediction accuracy on testing and real-world damaged datasets.The structural parameters can be extracted using different means such as Google Earth,Open Street Map,unmanned aerial vehicles,etc.However,recording the ground motion at a closer distance requires the installation of a dense array of sensors which requires a higher cost.For places with no earthquake recording station/device,it is difficult to have ground motion characteristics.For that different ML-based regressor models are developed utilizing past-earthquake information to predict ground motion parameters such as peak ground acceleration and peak ground velocity.The random forest regressor(RFR)achieved better results than other regression models on testing and validation datasets.Furthermore,compared with the results of similar research works,a better result is obtained using RFC and RFR on validation datasets.In the end,these models are uti-lized to predict the damage categories of RC buildings at Saitama University and Okubo Danchi,Saitama,Japan after an earthquake.This damage information is crucial for government agencies or decision-makers to respond systematically in post-disaster situations.展开更多
Rapid damage prediction for wind disasters is significant in emergency response and disaster mitigation,although it faces many challenges.In this study,a 1-km grid of wind speeds was simulated by the Holland model usi...Rapid damage prediction for wind disasters is significant in emergency response and disaster mitigation,although it faces many challenges.In this study,a 1-km grid of wind speeds was simulated by the Holland model using the 6-h interval records of maximum wind speed(MWS) for tropical cyclones(TC) from 1949 to 2020 in South China.The MWS during a TC transit was used to build damage rate curves for affected population and direct economic losses.The results show that the Holland model can efficiently simulate the grid-level MWS,which is comparable to the ground observations with R2 of0.71 to 0.93 and mean absolute errors(MAEs) of 3.3 to 7.5 m/s.The estimated damage rates were in good agreement with the reported values with R^(2)=0.69-0.87 for affected population and R^(2)=0.65-0.84 for GDP loss.The coastal areas and the Guangdong-Hong Kong-Macao Greater Bay Area have the greatest risk of wind disasters,mainly due to the region’s high density of population and developed economy.Our proposed method is suitable for rapid damage prediction and supporting emergency response and risk assessment at the community level for TCs in the coastal areas of China.展开更多
Near-field underwater explosions can cause substantial damage to offshore ship structures,presenting considerable risks to their integrity.This study focused on rapidly predicting girder structure deformation in ship ...Near-field underwater explosions can cause substantial damage to offshore ship structures,presenting considerable risks to their integrity.This study focused on rapidly predicting girder structure deformation in ship hulls subjected to near-field explosions from small equivalent-weight spherical charges underwater.The Runge-Kutta discontinuous Galerkin method(RKDG)was employed to calculate the explosive load generated by the spherical charge.This load was then applied to the nonlinear finite element solver software,ABAQUS,to determine the maximum deformation of the ship hull girder structure under the impulse load.By comparing the results with experimental data,the accuracy of the proposed model was validated,confirming that the RKDG finite element coupling calculation effectively simulates the response characteristics of spherical charges in near-field explosion scenarios.Subsequently,two machine learning algorithms driven by data,namely extreme gradient boosting(XGBoost)and random forest(RF),were employed to dynamically predict the maximum girder structure deformation in ship hulls.The analysis demonstrated that both models successfully predicted the maximum deformation.The root mean square error for the XGBoost model(27.67)was lower than that of the RF model(50.31).The XGBoost model also fitted 96%of the training data,compared to 94%for the RF model.Moreover,the relative error of the XGBoost model(6.25%)was lower than that of the RF model(10.38%).Overall,XGBoost is highly suitable for predicting girder structure deformation in ship hulls subjected to underwater explosions.展开更多
Based on the theory of continuum damage mechanics,a bi-variable damage mechanics model is developed,which,according to thermodynamics,is accessible to derivation of damage driving force,damage evolution equation and d...Based on the theory of continuum damage mechanics,a bi-variable damage mechanics model is developed,which,according to thermodynamics,is accessible to derivation of damage driving force,damage evolution equation and damage evolution criteria. Furthermore,damage evolution equations of time rate are established by the generalized Drucker＇s postulate. The damage evolution equation of cycle rate is obtained by integrating the time damage evolution equations,and the fatigue life prediction method for smooth specimens under repeated loading with constant strain amplitude is constructed. Likewise,for notched specimens under the repeated loading with constant strain amplitude,the fatigue life prediction method is obtained on the ground of the theory of conservative integral in damage mechanics. Thus,the material parameters in the damage evolution equation can be obtained by reference to the fatigue test results of standard specimens with stress concentration factor equal to 1,2 and 3.展开更多
Assessing the potential damage caused by earthquakes is crucial for a community’s emergency response.In this study,four machine learning(ML)methods—random forest,extremely randomized trees,AdaBoost(AB),and gradient ...Assessing the potential damage caused by earthquakes is crucial for a community’s emergency response.In this study,four machine learning(ML)methods—random forest,extremely randomized trees,AdaBoost(AB),and gradient boosting(GB)—were employed to develop prediction models for the damage potential of the mainshock(DIMS)and mainshock–aftershock sequences(DIMA).Building structures were modeled using eight single-degree-of-freedom(SDOF)systems with different hysteretic rules.A set of 662 recorded mainshock–aftershock(MS-AS)ground motions was selected from the PEER database.Seven intensity measures(IMs)were chosen to represent the characteristics of the mainshock and aftershock.The results revealed that the selected ML methods can well predict the structural damage potential of the SDOF systems,except for the AB method.The GB model exhibited the best performance,making it the recommended choice for predicting DIMS and DIMA among the four ML models.Additionally,the impact of input variables in the prediction was investigated using the shapley additive explanations(SHAP)method.The high-correlation variables were sensitive to the structural period(T).At T=1.0 s,the mainshock peak ground velocity(PGVM)and aftershock peak ground displacement(PGDA)significantly influenced the prediction of DIMA.When T increased to 5.0 s,the primary high-correlation factor of the mainshock IMs changed from PGVM to the mainshock peak ground displacement(PGDM);however,the highcorrelation variable of the aftershock IMs remained PGDA.The high-correlation factors for DIMS showed trends similar to those of DIMA.Finally,a table summarizing the first and second high-correlation variables for predicting DIMS and DIMA were provided,offering a valuable reference for parameter selection in seismic damage prediction for mainshock–aftershock sequences.展开更多
GIS technology has been applied to building damage analysis around the world. However, most previous studies focused on the application of 2-D GIS technology, and the results from traditional earthquake damage predict...GIS technology has been applied to building damage analysis around the world. However, most previous studies focused on the application of 2-D GIS technology, and the results from traditional earthquake damage prediction are displayed in 2-D figures and charts, which is incapable of demonstrating the 3-D spatial characteristics of buildings. Taking brick-concrete building as an example, we study the characteristics of building damage, and effectively combine the information of building textures and earthquake damage. Then, we apply Google SketchUp techniques to create building models and display them with seismic damage texture in the ArcGIS Engine software development environment. In this paper we propose a solid idea for 3-D simulation of earthquake damage, which is helpful in earthquake damage prediction, virtual emergency rescue practice and earthquake knowledge education.展开更多
Clarifying the correlation of multi-level mechanical parameters of structures in complex dynamic systems is a prerequisite for determining the accruing fatigue damage.In this paper,we adopt the independent component a...Clarifying the correlation of multi-level mechanical parameters of structures in complex dynamic systems is a prerequisite for determining the accruing fatigue damage.In this paper,we adopt the independent component analysis algorithm in unsupervised learning and tap the latent correlation between measured forces and stresses of high-speed train bogies.It is revealed that there exists a strong correlation between the vertical force and the stress at the junction of the transverse beam and the side frame,a site prone to fatigue.Stresses reconstructed by strongly correlated independent components account for more than 70%of the fatigue damage,which in turn supports the finding that the vertical forces are the main contribution to the fatigu e damage at the junction of the transverse beam and the side frame.This strong correlation between vertical forces and stresses effectively reduce the error in fatigue damage prediction and provide insights into fatigue life enhancement of critical structures of dynamic systems beyond high-speed trains.展开更多
Prediction,prevention,and control of forest fires are crucial on at all scales.Developing effective fire detection systems can aid in their control.This study proposes a novel CNN(convolutional neural network)using an...Prediction,prevention,and control of forest fires are crucial on at all scales.Developing effective fire detection systems can aid in their control.This study proposes a novel CNN(convolutional neural network)using an attention blocks module which combines an attention module with numerous input layers to enhance the performance of neural networks.The suggested model focuses on predicting the damage affected/burned areas due to possible wildfires and evaluating the multilateral interactions between the pertinent factors.The results show the impacts of CNN using attention blocks for feature extraction and to better understand how ecosystems are affected by meteorological factors.For selected meteorological data,RMSE 12.08 and MAE 7.45 values provide higher predictive power for selecting relevant and necessary features to provide optimal performance with less operational and computational costs.These findings show that the suggested strategy is reliable and effective for planning and managing fire-prone regions as well as for predicting forest fire damage.展开更多
This paper describes briefly the recent advances and achievements of the research projects conducted by the Institute of Engineering Mechanics (IEM) in the period of the Ninth Five-Year Plan (1995～2000) with the supp...This paper describes briefly the recent advances and achievements of the research projects conducted by the Institute of Engineering Mechanics (IEM) in the period of the Ninth Five-Year Plan (1995～2000) with the support of the China Seismological Bureau (CSB). These projects are related with key problems in the field of earthquake engineering. They are: development of the methods for determining earthquake resistant design load level, study on mechanisms of earthquake damage to buildings; development of new technology of base isolation, and study on earthquake damage prediction and seismic loss assessment methods. Through these studies, quite a number of problems have been solved and some of them have been applied in earthquake engineering design and practice.展开更多
Adhesive failure in the interfacial transition zone caused by aggregate particles is a major contributor to various forms of distress in asphalt mixtures.Accurate prediction of such failure holds significant research ...Adhesive failure in the interfacial transition zone caused by aggregate particles is a major contributor to various forms of distress in asphalt mixtures.Accurate prediction of such failure holds significant research value.However,conventional predictive models often struggle with the complex,highly nonlinear relationships between input variables and the damage indicator,and they generally lack interpretability—hindering understanding of the prediction process.This study introduces the Extremely Randomized Trees(Extra Trees)algorithm,which demonstrates superior performance in modeling non-linear relationships and offers strong interpretability.By applying Extra Trees,highly accurate and stable damage predictions were achieved(coefficient of determination,R2=0.984;mean absolute percentage error=8.14%).Model interpretability was further explored through correlation analysis,feature importance evaluation,and partial dependence plots,with results validated via ablation experiments.Additionally,the model's sensitivity to dataset size was investigated.Experimental findings confirm that Extra Trees significantly outperforms other algorithms in terms of both accuracy and stability.Its treebased structure also facilitates a deeper understanding of the roles of input features in the damage prediction process.展开更多
A novel method is presented to evaluate the complicated fatigue behavior of gears made of20Cr2Ni4 A.Fatigue tests are conducted in a high-frequency push-pull fatigue tester,and acoustic emission（AE）technique is used...A novel method is presented to evaluate the complicated fatigue behavior of gears made of20Cr2Ni4 A.Fatigue tests are conducted in a high-frequency push-pull fatigue tester,and acoustic emission（AE）technique is used to acquire metal fatigue signals.After analyzing large number of AE frequency spectrum,we find that：the crack extension can be expressed as the energy of specific frequency band,which is abbreviated as F-energy.To further validate the fatigue behavior,some correlation analysis is applied between F-energy and some AE parameters.Experimental results show that there is significant correlation among the Fenergy,root mean square（RMS）,relative energy,and hits.The findings can be used to validate the effectiveness of the F-energy in predicting fatigue crack propagation and remaining life for parts in-service.F-energy,as a new AE parameter,is first put forward in the area of fatigue crack growth.展开更多
The electrostatic discharge(ESD)protection circuit widely exists in the input and output ports of CMOS digital circuits,and fast rising time electromagnetic pulse(FREMP)coupled into the device not only interacts with ...The electrostatic discharge(ESD)protection circuit widely exists in the input and output ports of CMOS digital circuits,and fast rising time electromagnetic pulse(FREMP)coupled into the device not only interacts with the CMOS circuit,but also acts on the protection circuit.This paper establishes a model of on-chip CMOS electrostatic discharge protection circuit and selects square pulse as the FREMP signals.Based on multiple physical parameter models,it depicts the distribution of the lattice temperature,current density,and electric field intensity inside the device.At the same time,this paper explores the changes of the internal devices in the circuit under the injection of fast rising time electromagnetic pulse and describes the relationship between the damage amplitude threshold and the pulse width.The results show that the ESD protection circuit has potential damage risk,and the injection of FREMP leads to irreversible heat loss inside the circuit.In addition,pulse signals with different attributes will change the damage threshold of the circuit.These results provide an important reference for further evaluation of the influence of electromagnetic environment on the chip,which is helpful to carry out the reliability enhancement research of ESD protection circuit.展开更多
Vibration-based damage detection methods have become widely used because of their advantages over traditional methods.This paper presents a new approach to identify the crack depth in steel beam structures based on vi...Vibration-based damage detection methods have become widely used because of their advantages over traditional methods.This paper presents a new approach to identify the crack depth in steel beam structures based on vibration analysis using the Finite Element Method(FEM)and Artificial Neural Network(ANN)combined with Butterfly Optimization Algorithm(BOA).ANN is quite successful in such identification issues,but it has some limitations,such as reduction of error after system training is complete,which means the output does not provide optimal results.This paper improves ANN training after introducing BOA as a hybrid model(BOA-ANN).Natural frequencies are used as input parameters and crack depth as output.The data are collected from improved FEM using simulation tools(ABAQUS)based on different crack depths and locations as the first stage.Next,data are collected from experimental analysis of cracked beams based on different crack depths and locations to test the reliability of the presented technique.The proposed approach,compared to other methods,can predict crack depth with improved accuracy.展开更多
基金supported by National Natural Science Foundation of China(Grant No.12432018,12372346)the Innovative Research Groups of the National Natural Science Foundation of China(Grant No.12221002).
文摘A typical Whipple shield consists of double-layered plates with a certain gap.The space debris impacts the outer plate and is broken into a debris cloud(shattered,molten,vaporized)with dispersed energy and momentum,which reduces the risk of penetrating the bulkhead.In the realm of hypervelocity impact,strain rate(>10^(5)s^(-1))effects are negligible,and fluid dynamics is employed to describe the impact process.Efficient numerical tools for precisely predicting the damage degree can greatly accelerate the design and optimization of advanced protective structures.Current hypervelocity impact research primarily focuses on the interaction between projectile and front plate and the movement of debris cloud.However,the damage mechanism of debris cloud impacts on rear plates-the critical threat component-remains underexplored owing to complex multi-physics processes and prohibitive computational costs.Existing approaches,ranging from semi-empirical equations to a machine learningbased ballistic limit prediction method,are constrained to binary penetration classification.Alternatively,the uneven data from experiments and simulations caused these methods to be ineffective when the projectile has irregular shapes and complicate flight attitude.Therefore,it is urgent to develop a new damage prediction method for predicting the rear plate damage,which can help to gain a deeper understanding of the damage mechanism.In this study,a machine learning(ML)method is developed to predict the damage distribution in the rear plate.Based on the unit velocity space,the discretized information of debris cloud and rear plate damage from rare simulation cases is used as input data for training the ML models,while the generalization ability for damage distribution prediction is tested by other simulation cases with different attack angles.The results demonstrate that the training and prediction accuracies using the Random Forest(RF)algorithm significantly surpass those using Artificial Neural Networks(ANNs)and Support Vector Machine(SVM).The RF-based model effectively identifies damage features in sparsely distributed debris cloud and cumulative effect.This study establishes an expandable new dataset that accommodates additional parameters to improve the prediction accuracy.Results demonstrate the model's ability to overcome data imbalance limitations through debris cloud features,enabling rapid and accurate rear plate damage prediction across wider scenarios with minimal data requirements.
文摘This paper presents a method for searching the weak story by using the ultimate shear force coefficient on the multi-story brick buildings with two frame-shear-wall-supported stories. The method of seismic damage prediction is discussed according to different weak stories. When the first story is t theweak one,the damage state of the building can be determined by the displacement ratio. The prediction method is also used in a practical engineering project.
基金the support of National Natural Science Foundation of China (No. 50775107)National High Technical Research and Development Programme of China (No. 2007AA04Z407)Innovation Program for Graduate Students in Nanjing University of Technology (No. BSCX200816)
文摘HP40Nb steel, used as a candidate material for ethylene cracking furnace tube, suffers creep and carburization damage from the complex environment of high temperature, high carbon potential and low oxygen partial pressure, and they lead to failure of the furnace tubes ahead of designed life. In order to investigate damage evolution under the complex condition, coupled creep damage and carburization damage constitutive equations were developed according to continuum damage mechanics theory. Based on the finite element ABAQUS code, user subroutines were developed for analyz- ing damage evolution of ethylene furnace tube under the action of coupled creep- carburization. The results show that carburization accelerates the damage process dramatically, damage value reaches the critical value along the inner surface after serving for 75,000 h under the action of creep-carburization, meanwhile the damage value is only 0.53 along the outer surface after operating the same time under the action of creep alone, which means that microcracks are generated along the inner surface under the action of coupled creep-carburization, fracture begins along the outer surface of tube under the action of creep alone.
文摘There are several underground mines in India which operate in close proximity to an operating surface mine.Under such scenario,the blast induced stress waves generated due to surface blasting may be a potential source to cause instability of adjoining underground mine structures.Using seismographs,54 blast induced vibration data were recorded at various locations in the roof,floor and pillars of the underground mine at Hingir Rampur mine of Coal India Limited by synchronizing the timing of surface blasting carried at an adjacent Samleshwari opencast mine.Results of this study show that Artificial Neural Network(ANN)has better prediction potential of peak particle velocity(PPV)and damage to adjacent underground structures due to surface blasting as compared to conventional regression methods.In order to assess and predict the impact of surface blasts on underground workings,Blast Damage Factor(BDF)has been evolved.The study shows that site specific charts can predict the blast damage class at an underground location due to surface blasting for known distances and explosive charge per delay.The severe damage in case study mine site took place when peak particle velocity exceeded 162 mm/s and PPV less than 51 mm/s had no probability of damage to underground structures due to surface blasting.
基金sponsored by the National Basic Research Programof China (2006BAC13B02)the Science and Technology Special Program for Seismology, China Earthquake Administration (200708003)
文摘Seismic damage indices of structure are widely used to quantificationally analyze structural damage levels under earthquake action. In this paper, a five-storey building model and a seventeen-storey building model are established. According to seven typical indices and different earthquake-inputs, a structural damage prediction is performed, with the results showing serious uncertainty of structural damage prediction due to different indices. Understanding of this phenomenon aids the comprehension and application of the results of earthquake damage prediction.
基金supported by the National Natural Science Foundation of China(No.52274026)the National Key Research and Development Program(No.2022YFC2806504)the CNOOC Research Project(No.KJGG-2022-17-04 and NO.KJGG-2022-17-05).
文摘Effective isolation between the cement sheath and the sandstone is crucial for the development and production of oil and gas wells in sandstone formations.In this study,a cement-sandstone composite(CSC)was prepared,and based onμ-CT three-dimensional reconstruction imaging and finite element analysis(FEA)techniques,the stress distribution and potential failure mechanism at the cement-sandstone bonding interface under axial loading were analyzed.The key findings are as follows:(1)stress concentrations are highly likely to form at the gap between the cement and sandstone interface and around interfacial voids,with Von Mises stress reaching critical levels of 18.0-20.0 MPa at these locations,significantly exceeding the stress magnitudes in well-bonded regions;(2)the phenomenon of local stress concentration driven by interfacial defects can be identified as the main basis for predicting damage location in interfacial debonding and continuous shear under axial load;(3)ensuring tight cementation at the cement-sandstone interface and minimizing interfacial voids are paramount for preventing stress-induced failure;(4)the critical Von Mises stress value of 20 MPa at the interface defect can be used as a benchmark for material selection and designed to ensure long-term integrity in oil and gas well applications subjected to similar axial loads.These findings contribute to a more accurate understanding of the failure mechanism of the cement-sandstone interface and to the precise design of material properties,thereby ensuring the long-term integrity of oil and gas well applications subjected to similar axial loads.
基金The authors are grateful for the financial support received from the National Key R&D Program(Grant No.2018YFC1504401)the National Natural Science Foundation of China(Grant No.51778341).
文摘Strong aftershocks generally occur following a significant earthquake.Aftershocks further damage buildings weakened by mainshocks.Thus,the accurate and efficient prediction of aftershock-induced damage to buildings on a regional scale is crucial for decision making for post-earthquake rescue and emergency response.A framework to predict regional seismic damage of buildings under a mainshock-aftershock(MS-AS)sequence is proposed in this study based on city-scale nonlinear time-history analysis(THA).Specifically,an MS-AS sequence-generation method is proposed to generate a potential MS-AS sequence that can account for the amplification,spectrum,duration,magnitude,and site condition of a target area.Moreover,city-scale nonlinear THA is adopted to predict building seismic damage subjected to MS-AS sequences.The accuracy and reliability of city-scale nonlinear THA for an MS-AS sequence are validated by as-recorded seismic responses of buildings and simulation results in published literature.The town of Longtoushan,which was damaged during the Ludian earthquake,is used as a case study to illustrate the detailed procedure and advantages of the proposed framework.The primary conclusions are as follows.(1)Regional seismic damage of buildings under an MS-AS sequence can be predicted reasonably and accurately by city-scale nonlinear THA.(2)An MS-AS sequence can be generated reasonably by the proposed MS-AS sequencegeneration method.(3)Regional seismic damage of buildings under different MS-AS scenarios can be provided efficiently by the proposed framework,which in turn can provide a useful reference for earthquake emergency response and scientific decision making for earthquake disaster relief.
文摘This study examines the feasibility of using a machine learning approach for rapid damage assessment of rein-forced concrete(RC)buildings after the earthquake.Since the real-world damaged datasets are lacking,have limited access,or are imbalanced,a simulation dataset is prepared by conducting a nonlinear time history analy-sis.Different machine learning(ML)models are trained considering the structural parameters and ground motion characteristics to predict the RC building damage into five categories:null,slight,moderate,heavy,and collapse.The random forest classifier(RFC)has achieved a higher prediction accuracy on testing and real-world damaged datasets.The structural parameters can be extracted using different means such as Google Earth,Open Street Map,unmanned aerial vehicles,etc.However,recording the ground motion at a closer distance requires the installation of a dense array of sensors which requires a higher cost.For places with no earthquake recording station/device,it is difficult to have ground motion characteristics.For that different ML-based regressor models are developed utilizing past-earthquake information to predict ground motion parameters such as peak ground acceleration and peak ground velocity.The random forest regressor(RFR)achieved better results than other regression models on testing and validation datasets.Furthermore,compared with the results of similar research works,a better result is obtained using RFC and RFR on validation datasets.In the end,these models are uti-lized to predict the damage categories of RC buildings at Saitama University and Okubo Danchi,Saitama,Japan after an earthquake.This damage information is crucial for government agencies or decision-makers to respond systematically in post-disaster situations.
基金financially supported by the National Key R&D Program of China (2021YFC3001000)National Natural Science Foundation of China (41871085)the Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (311021004)。
文摘Rapid damage prediction for wind disasters is significant in emergency response and disaster mitigation,although it faces many challenges.In this study,a 1-km grid of wind speeds was simulated by the Holland model using the 6-h interval records of maximum wind speed(MWS) for tropical cyclones(TC) from 1949 to 2020 in South China.The MWS during a TC transit was used to build damage rate curves for affected population and direct economic losses.The results show that the Holland model can efficiently simulate the grid-level MWS,which is comparable to the ground observations with R2 of0.71 to 0.93 and mean absolute errors(MAEs) of 3.3 to 7.5 m/s.The estimated damage rates were in good agreement with the reported values with R^(2)=0.69-0.87 for affected population and R^(2)=0.65-0.84 for GDP loss.The coastal areas and the Guangdong-Hong Kong-Macao Greater Bay Area have the greatest risk of wind disasters,mainly due to the region’s high density of population and developed economy.Our proposed method is suitable for rapid damage prediction and supporting emergency response and risk assessment at the community level for TCs in the coastal areas of China.
基金Supported by the Heilongjiang Provincial Excellent Youth Fund under Grant No.YQ2021E009the Heilongjiang Provincial Key R&D Program under Grant No.GZ20210210the National Major Program under Grant No.J2019-I-0017-0016.
文摘Near-field underwater explosions can cause substantial damage to offshore ship structures,presenting considerable risks to their integrity.This study focused on rapidly predicting girder structure deformation in ship hulls subjected to near-field explosions from small equivalent-weight spherical charges underwater.The Runge-Kutta discontinuous Galerkin method(RKDG)was employed to calculate the explosive load generated by the spherical charge.This load was then applied to the nonlinear finite element solver software,ABAQUS,to determine the maximum deformation of the ship hull girder structure under the impulse load.By comparing the results with experimental data,the accuracy of the proposed model was validated,confirming that the RKDG finite element coupling calculation effectively simulates the response characteristics of spherical charges in near-field explosion scenarios.Subsequently,two machine learning algorithms driven by data,namely extreme gradient boosting(XGBoost)and random forest(RF),were employed to dynamically predict the maximum girder structure deformation in ship hulls.The analysis demonstrated that both models successfully predicted the maximum deformation.The root mean square error for the XGBoost model(27.67)was lower than that of the RF model(50.31).The XGBoost model also fitted 96%of the training data,compared to 94%for the RF model.Moreover,the relative error of the XGBoost model(6.25%)was lower than that of the RF model(10.38%).Overall,XGBoost is highly suitable for predicting girder structure deformation in ship hulls subjected to underwater explosions.
文摘Based on the theory of continuum damage mechanics,a bi-variable damage mechanics model is developed,which,according to thermodynamics,is accessible to derivation of damage driving force,damage evolution equation and damage evolution criteria. Furthermore,damage evolution equations of time rate are established by the generalized Drucker＇s postulate. The damage evolution equation of cycle rate is obtained by integrating the time damage evolution equations,and the fatigue life prediction method for smooth specimens under repeated loading with constant strain amplitude is constructed. Likewise,for notched specimens under the repeated loading with constant strain amplitude,the fatigue life prediction method is obtained on the ground of the theory of conservative integral in damage mechanics. Thus,the material parameters in the damage evolution equation can be obtained by reference to the fatigue test results of standard specimens with stress concentration factor equal to 1,2 and 3.
基金China Postdoctoral Science Foundation under Grant No.2022M710333the Beijing Postdoctoral Research Foundation under Grant No.2023-zz-141the National Natural Science Foundation of China under Grant Nos.52278492 and 52078176。
文摘Assessing the potential damage caused by earthquakes is crucial for a community’s emergency response.In this study,four machine learning(ML)methods—random forest,extremely randomized trees,AdaBoost(AB),and gradient boosting(GB)—were employed to develop prediction models for the damage potential of the mainshock(DIMS)and mainshock–aftershock sequences(DIMA).Building structures were modeled using eight single-degree-of-freedom(SDOF)systems with different hysteretic rules.A set of 662 recorded mainshock–aftershock(MS-AS)ground motions was selected from the PEER database.Seven intensity measures(IMs)were chosen to represent the characteristics of the mainshock and aftershock.The results revealed that the selected ML methods can well predict the structural damage potential of the SDOF systems,except for the AB method.The GB model exhibited the best performance,making it the recommended choice for predicting DIMS and DIMA among the four ML models.Additionally,the impact of input variables in the prediction was investigated using the shapley additive explanations(SHAP)method.The high-correlation variables were sensitive to the structural period(T).At T=1.0 s,the mainshock peak ground velocity(PGVM)and aftershock peak ground displacement(PGDA)significantly influenced the prediction of DIMA.When T increased to 5.0 s,the primary high-correlation factor of the mainshock IMs changed from PGVM to the mainshock peak ground displacement(PGDM);however,the highcorrelation variable of the aftershock IMs remained PGDA.The high-correlation factors for DIMS showed trends similar to those of DIMA.Finally,a table summarizing the first and second high-correlation variables for predicting DIMS and DIMA were provided,offering a valuable reference for parameter selection in seismic damage prediction for mainshock–aftershock sequences.
基金supported by the Special Fund for the Scientific Research of Seismological Field in 2012 ( 201208018)
文摘GIS technology has been applied to building damage analysis around the world. However, most previous studies focused on the application of 2-D GIS technology, and the results from traditional earthquake damage prediction are displayed in 2-D figures and charts, which is incapable of demonstrating the 3-D spatial characteristics of buildings. Taking brick-concrete building as an example, we study the characteristics of building damage, and effectively combine the information of building textures and earthquake damage. Then, we apply Google SketchUp techniques to create building models and display them with seismic damage texture in the ArcGIS Engine software development environment. In this paper we propose a solid idea for 3-D simulation of earthquake damage, which is helpful in earthquake damage prediction, virtual emergency rescue practice and earthquake knowledge education.
基金supported by the National Key Research and Development Program of China(Grant No.2022YFB4301103)the National Natural Science Foundation of China(Grant Nos.11988102,52402485,and U2368215)+1 种基金the China Postdoctoral Science Foundation(Grant No.2024M763362)the Youth Innovation Promotion Association of the Chinese Academy of Sciences(Grant No.Y2022009)。
文摘Clarifying the correlation of multi-level mechanical parameters of structures in complex dynamic systems is a prerequisite for determining the accruing fatigue damage.In this paper,we adopt the independent component analysis algorithm in unsupervised learning and tap the latent correlation between measured forces and stresses of high-speed train bogies.It is revealed that there exists a strong correlation between the vertical force and the stress at the junction of the transverse beam and the side frame,a site prone to fatigue.Stresses reconstructed by strongly correlated independent components account for more than 70%of the fatigue damage,which in turn supports the finding that the vertical forces are the main contribution to the fatigu e damage at the junction of the transverse beam and the side frame.This strong correlation between vertical forces and stresses effectively reduce the error in fatigue damage prediction and provide insights into fatigue life enhancement of critical structures of dynamic systems beyond high-speed trains.
文摘Prediction,prevention,and control of forest fires are crucial on at all scales.Developing effective fire detection systems can aid in their control.This study proposes a novel CNN(convolutional neural network)using an attention blocks module which combines an attention module with numerous input layers to enhance the performance of neural networks.The suggested model focuses on predicting the damage affected/burned areas due to possible wildfires and evaluating the multilateral interactions between the pertinent factors.The results show the impacts of CNN using attention blocks for feature extraction and to better understand how ecosystems are affected by meteorological factors.For selected meteorological data,RMSE 12.08 and MAE 7.45 values provide higher predictive power for selecting relevant and necessary features to provide optimal performance with less operational and computational costs.These findings show that the suggested strategy is reliable and effective for planning and managing fire-prone regions as well as for predicting forest fire damage.
文摘This paper describes briefly the recent advances and achievements of the research projects conducted by the Institute of Engineering Mechanics (IEM) in the period of the Ninth Five-Year Plan (1995～2000) with the support of the China Seismological Bureau (CSB). These projects are related with key problems in the field of earthquake engineering. They are: development of the methods for determining earthquake resistant design load level, study on mechanisms of earthquake damage to buildings; development of new technology of base isolation, and study on earthquake damage prediction and seismic loss assessment methods. Through these studies, quite a number of problems have been solved and some of them have been applied in earthquake engineering design and practice.
基金supported by the National Natural Science Foundation of China(Grant No.52478449)the Basic Research Priorities Program of Jiangsu(Grant No.BK20232036)the Natural Science Foundation of Jiangsu(Grant No.BK20221468)。
文摘Adhesive failure in the interfacial transition zone caused by aggregate particles is a major contributor to various forms of distress in asphalt mixtures.Accurate prediction of such failure holds significant research value.However,conventional predictive models often struggle with the complex,highly nonlinear relationships between input variables and the damage indicator,and they generally lack interpretability—hindering understanding of the prediction process.This study introduces the Extremely Randomized Trees(Extra Trees)algorithm,which demonstrates superior performance in modeling non-linear relationships and offers strong interpretability.By applying Extra Trees,highly accurate and stable damage predictions were achieved(coefficient of determination,R2=0.984;mean absolute percentage error=8.14%).Model interpretability was further explored through correlation analysis,feature importance evaluation,and partial dependence plots,with results validated via ablation experiments.Additionally,the model's sensitivity to dataset size was investigated.Experimental findings confirm that Extra Trees significantly outperforms other algorithms in terms of both accuracy and stability.Its treebased structure also facilitates a deeper understanding of the roles of input features in the damage prediction process.
基金Supported by the National Natural Science Foundation of China(50975030)
文摘A novel method is presented to evaluate the complicated fatigue behavior of gears made of20Cr2Ni4 A.Fatigue tests are conducted in a high-frequency push-pull fatigue tester,and acoustic emission（AE）technique is used to acquire metal fatigue signals.After analyzing large number of AE frequency spectrum,we find that：the crack extension can be expressed as the energy of specific frequency band,which is abbreviated as F-energy.To further validate the fatigue behavior,some correlation analysis is applied between F-energy and some AE parameters.Experimental results show that there is significant correlation among the Fenergy,root mean square（RMS）,relative energy,and hits.The findings can be used to validate the effectiveness of the F-energy in predicting fatigue crack propagation and remaining life for parts in-service.F-energy,as a new AE parameter,is first put forward in the area of fatigue crack growth.
基金National Natural Science Foundation of China(61974116)。
文摘The electrostatic discharge(ESD)protection circuit widely exists in the input and output ports of CMOS digital circuits,and fast rising time electromagnetic pulse(FREMP)coupled into the device not only interacts with the CMOS circuit,but also acts on the protection circuit.This paper establishes a model of on-chip CMOS electrostatic discharge protection circuit and selects square pulse as the FREMP signals.Based on multiple physical parameter models,it depicts the distribution of the lattice temperature,current density,and electric field intensity inside the device.At the same time,this paper explores the changes of the internal devices in the circuit under the injection of fast rising time electromagnetic pulse and describes the relationship between the damage amplitude threshold and the pulse width.The results show that the ESD protection circuit has potential damage risk,and the injection of FREMP leads to irreversible heat loss inside the circuit.In addition,pulse signals with different attributes will change the damage threshold of the circuit.These results provide an important reference for further evaluation of the influence of electromagnetic environment on the chip,which is helpful to carry out the reliability enhancement research of ESD protection circuit.
文摘Vibration-based damage detection methods have become widely used because of their advantages over traditional methods.This paper presents a new approach to identify the crack depth in steel beam structures based on vibration analysis using the Finite Element Method(FEM)and Artificial Neural Network(ANN)combined with Butterfly Optimization Algorithm(BOA).ANN is quite successful in such identification issues,but it has some limitations,such as reduction of error after system training is complete,which means the output does not provide optimal results.This paper improves ANN training after introducing BOA as a hybrid model(BOA-ANN).Natural frequencies are used as input parameters and crack depth as output.The data are collected from improved FEM using simulation tools(ABAQUS)based on different crack depths and locations as the first stage.Next,data are collected from experimental analysis of cracked beams based on different crack depths and locations to test the reliability of the presented technique.The proposed approach,compared to other methods,can predict crack depth with improved accuracy.
