The rapid increase in traffic loads and frequencies has rendered conventional asphalt pavement inadequate to maintain its durability under tropical climates.This challenge has necessitated the exploration of new sourc...The rapid increase in traffic loads and frequencies has rendered conventional asphalt pavement inadequate to maintain its durability under tropical climates.This challenge has necessitated the exploration of new sources of modified asphalt with enhanced stiffness and superior performance at high temperatures.Natural rubber(NR)is a renewable biopolymer that has received growing interest as a modifier for asphalt binders.Cup lump rubber(CLR),a type of NR,is used to enhance asphalt properties and improve the performance of road pavements.This study evaluates the influence of wax-based surfactants(WS)on CLR-modified asphalt binder(CMB).The assessment focuses on changes in chemical characteristics,rheological behaviour,activation energy,and morphology.Four concentrations of WS(0.1%,0.15%,0.2%,and 0.25%)were incorporated into CMB.Analysis of CMB chemical changes showed that viscosity increased due to higher sulfoxide,carbonyl,and aromatic bond indices.These chemical modifications contributed to improved resistance of the binder to heat-induced deterioration.In both unaged and aged CMB samples,the incorporation of WS reduced the sulfoxide index of the binder.Rheological analysis indicated that CMB improved rutting resistance and anti-ageing performance,while WS further enhanced fatigue resistance.Activation energy analysis suggested that the combination of CMB with 0.15%WS produced the most favourable enhancement.Micrograph results showed that WS improved binder homogeneity and interconnectivity.In conclusion,the findings indicated that incorporating 0.15%WS into CMB enhanced the performance and durability of the asphalt pavement.展开更多
Reinforced concrete(RC)flat slabs,a popular choice in construction due to their flexibility,are susceptible to sudden and brittle punching shear failure.Existing design methods often exhibit significant bias and varia...Reinforced concrete(RC)flat slabs,a popular choice in construction due to their flexibility,are susceptible to sudden and brittle punching shear failure.Existing design methods often exhibit significant bias and variability.Accurate estimation of punching shear strength in RC flat slabs is crucial for effective concrete structure design and management.This study introduces a novel computation method,the jellyfish-least square support vector machine(JS-LSSVR)hybrid model,to predict punching shear strength.By combining machine learning(LSSVR)with jellyfish swarm(JS)intelligence,this hybrid model ensures precise and reliable predictions.The model’s development utilizes a real-world experimental data set.Comparison with seven established optimizers,including artificial bee colony(ABC),differential evolution(DE),genetic algorithm(GA),and others,as well as existing machine learning(ML)-based models and design codes,validates the superiority of the JS-LSSVR hybrid model.This innovative approach significantly enhances prediction accuracy,providing valuable support for civil engineers in estimating RC flat slab punching shear strength.展开更多
The bending capacity of the precast decks is greatly dependent on the flexural strength exhibited by the joints between them.However,due to the complexity and diversity of this system,precise predictive models are cur...The bending capacity of the precast decks is greatly dependent on the flexural strength exhibited by the joints between them.However,due to the complexity and diversity of this system,precise predictive models are currently unavailable.This study introduces an effective and precise methodology for assessing flexural strength using Monte Carlo Model Averaging(MCMA),a statistical technique that combines the strengths of model averaging(MA)and Monte Carlo simulation.To construct the MCMA model,input variables were derived by analyzing the experimental results,and a database of 433 bending test specimens was compiled.The MCMA model incorporated four different machine learning models,namely decision tree(DT),linear regression(LR),adaptive boosting(AdaBoost),and multilayer perceptron(MLP).Comparative analyses revealed that the MCMA model outperformed baseline models(DT,AdaBoost,LR,and MLP)across all employed metrics.The impact of three different categories on flexural capacity was explored through boxplot analysis.Furthermore,a comparison between the MCMA model and the strut and tie model highlighted the superior performance of the MCMA model.The impact of input variables on the flexural strength prediction was further examined through Shapley Additive exPlanations based feature importance and global interpretation,as well as parametric study.展开更多
The conventional admittance approach utilizing statistical evaluation metrics offers limited information about the damage location,especially when damage introduces nonlinearities in admittance features.This study pro...The conventional admittance approach utilizing statistical evaluation metrics offers limited information about the damage location,especially when damage introduces nonlinearities in admittance features.This study proposes a novel automated damage localization method for plate-like structures based on deep learning of raw admittance signals.A one-dimensional(1D)convolutional neural network(CNN)-based model is designed to automate processing of raw admittance response and prediction of damage probabilities across multiple locations in a monitored structure.Raw admittance data set is augmented with white noise to simulate realistic measurement conditions.Stratified K-fold cross-validation technique is employed for training and testing the network.The experimental validation of the proposed method shows that the proposed method can accurately identify the state and damage location in the plate with an average accuracy of 98%.Comparing with established 1D CNN models reveals superior performance of the proposed method,with significantly lower testing error.The proposed method exhibits the ability to directly handle raw electromechanical admittance responses and extract optimal features,overcoming limitations associated with traditional piezoelectric admittance approaches.By eliminating the need for signal preprocessing,this method holds promise for real-time damage monitoring of plate structures.展开更多
基金the financial support provided under the Fundamental Research Grant Scheme(FRGS),Project Code:FRGS/1/2021/TK01/USM/02/1,which made this research possible,The kind assistance and collaboration of all material suppliers were also gratefully acknowledged.Additional appreciation is extended to the Public Works Department Malaysia for the scholarship provided through the Hadiah Latihan Persekutuan programme for professional and managerial officers pursuing doctoral studies(JPA-1-840622086040)The APC was funded by Prof.Hui Yao from Beijing University of Technology.
文摘The rapid increase in traffic loads and frequencies has rendered conventional asphalt pavement inadequate to maintain its durability under tropical climates.This challenge has necessitated the exploration of new sources of modified asphalt with enhanced stiffness and superior performance at high temperatures.Natural rubber(NR)is a renewable biopolymer that has received growing interest as a modifier for asphalt binders.Cup lump rubber(CLR),a type of NR,is used to enhance asphalt properties and improve the performance of road pavements.This study evaluates the influence of wax-based surfactants(WS)on CLR-modified asphalt binder(CMB).The assessment focuses on changes in chemical characteristics,rheological behaviour,activation energy,and morphology.Four concentrations of WS(0.1%,0.15%,0.2%,and 0.25%)were incorporated into CMB.Analysis of CMB chemical changes showed that viscosity increased due to higher sulfoxide,carbonyl,and aromatic bond indices.These chemical modifications contributed to improved resistance of the binder to heat-induced deterioration.In both unaged and aged CMB samples,the incorporation of WS reduced the sulfoxide index of the binder.Rheological analysis indicated that CMB improved rutting resistance and anti-ageing performance,while WS further enhanced fatigue resistance.Activation energy analysis suggested that the combination of CMB with 0.15%WS produced the most favourable enhancement.Micrograph results showed that WS improved binder homogeneity and interconnectivity.In conclusion,the findings indicated that incorporating 0.15%WS into CMB enhanced the performance and durability of the asphalt pavement.
基金Acknowledgements This research was supported by the Research Program funded by Seoul National University of Science and Technology(SeoulTech).
文摘Reinforced concrete(RC)flat slabs,a popular choice in construction due to their flexibility,are susceptible to sudden and brittle punching shear failure.Existing design methods often exhibit significant bias and variability.Accurate estimation of punching shear strength in RC flat slabs is crucial for effective concrete structure design and management.This study introduces a novel computation method,the jellyfish-least square support vector machine(JS-LSSVR)hybrid model,to predict punching shear strength.By combining machine learning(LSSVR)with jellyfish swarm(JS)intelligence,this hybrid model ensures precise and reliable predictions.The model’s development utilizes a real-world experimental data set.Comparison with seven established optimizers,including artificial bee colony(ABC),differential evolution(DE),genetic algorithm(GA),and others,as well as existing machine learning(ML)-based models and design codes,validates the superiority of the JS-LSSVR hybrid model.This innovative approach significantly enhances prediction accuracy,providing valuable support for civil engineers in estimating RC flat slab punching shear strength.
文摘The bending capacity of the precast decks is greatly dependent on the flexural strength exhibited by the joints between them.However,due to the complexity and diversity of this system,precise predictive models are currently unavailable.This study introduces an effective and precise methodology for assessing flexural strength using Monte Carlo Model Averaging(MCMA),a statistical technique that combines the strengths of model averaging(MA)and Monte Carlo simulation.To construct the MCMA model,input variables were derived by analyzing the experimental results,and a database of 433 bending test specimens was compiled.The MCMA model incorporated four different machine learning models,namely decision tree(DT),linear regression(LR),adaptive boosting(AdaBoost),and multilayer perceptron(MLP).Comparative analyses revealed that the MCMA model outperformed baseline models(DT,AdaBoost,LR,and MLP)across all employed metrics.The impact of three different categories on flexural capacity was explored through boxplot analysis.Furthermore,a comparison between the MCMA model and the strut and tie model highlighted the superior performance of the MCMA model.The impact of input variables on the flexural strength prediction was further examined through Shapley Additive exPlanations based feature importance and global interpretation,as well as parametric study.
文摘The conventional admittance approach utilizing statistical evaluation metrics offers limited information about the damage location,especially when damage introduces nonlinearities in admittance features.This study proposes a novel automated damage localization method for plate-like structures based on deep learning of raw admittance signals.A one-dimensional(1D)convolutional neural network(CNN)-based model is designed to automate processing of raw admittance response and prediction of damage probabilities across multiple locations in a monitored structure.Raw admittance data set is augmented with white noise to simulate realistic measurement conditions.Stratified K-fold cross-validation technique is employed for training and testing the network.The experimental validation of the proposed method shows that the proposed method can accurately identify the state and damage location in the plate with an average accuracy of 98%.Comparing with established 1D CNN models reveals superior performance of the proposed method,with significantly lower testing error.The proposed method exhibits the ability to directly handle raw electromechanical admittance responses and extract optimal features,overcoming limitations associated with traditional piezoelectric admittance approaches.By eliminating the need for signal preprocessing,this method holds promise for real-time damage monitoring of plate structures.
