Given the growing emphasis on life-cycle analysis in bridge design,the design community is transitioning from the concept of performance-based design in structural engineering to a performance-based design approach wi...Given the growing emphasis on life-cycle analysis in bridge design,the design community is transitioning from the concept of performance-based design in structural engineering to a performance-based design approach within a life-cycle context.This approach considers various indicators,including cost,environmental impact,and societal factors when designing bridges.This shift enables a comprehensive assessment of structural resilience by exam-ining the bridge’s ability to endure various hazards throughout its lifespan.This study provides a comprehensive review of two key research domains that have emerged in the field of bridge life-cycle analysis,namely life-cycle sustainability(LCS)and life-cycle performance(LCP).The discussion on the LCS of bridges encompasses both assessment-based and optimization-based studies,while the exploration of LCP focuses on research examining structures subjected to deterioration over their service life due to deprecating phenomena such as corrosion and relative humidity changes,as well as extreme hazards like earthquakes and floods.Moreover,this study discusses the integration between LCS and LCP,highlighting how combined consideration of these factors can minimize damage costs,improve resiliency,and extend the lifespan of the structure.A detailed evaluation encompasses various life-cycle metrics,structural performance indicators,time-dependent modelling techniques,and analy-sis methods proposed in the literature.Additionally,the research identifies critical gaps and trends in life-cycle analysis within the realm of bridge engineering,providing a concise yet thorough overview for advancing con-siderations in the life-cycle design of bridges.展开更多
Efficient seismic vulnerability and resilience assessment is essential for ultra-high-performance concrete(UHPC)bridges,given their distinctive mechanical and structural properties.However,existing single-parameter-ba...Efficient seismic vulnerability and resilience assessment is essential for ultra-high-performance concrete(UHPC)bridges,given their distinctive mechanical and structural properties.However,existing single-parameter-based probabilistic seismic demand(PSD)models overlook critical bridge‐specific characteristics and uncertainties.Besides,studies on seismic vulnerability and resilience assessment of UHPC bridges are scarce.Thus,this study proposes a hybrid machine learning(ML)-enabled multivariate bridge-specific seismic vulnerability and resilience assessment framework for UHPC bridges.Key design parameters and associated uncertainties are identified,and a Latin Hypercube Sampling(LHS)technique is employed to establish a representative UHPC bridge database,which is used to develop a hybrid ML model-based multivariate PSD model.A comparative analysis with the conventional PSD model,as well as widely used ML algorithms,demonstrated that the proposed PSD model achieves the highest predictive performance,characterized by the highest coefficient of determination and lowest prediction errors.Additionally,SHapley Additive exPlanation(SHAP)analysis is used to investigate the effect of different parameters on the PSD of UHPC bridges.The results of SHAP show the peak ground acceleration(PGA)as the most important factor,followed by bridge span and column diameter.The hybrid ML-enabled multi-variate bridge-specific fragility analysis results are used to investigate the functionality recovery and resilience of the bridge,which demonstrate the reduction in the residual functionality and overall bridge resilience with the increase in the ground motion intensity.展开更多
The reliable prediction of hoop strain of fiber-reinforced polymer(FRP)-confined concrete is crucial for assessing confinement efficiency and ensuring structural integrity.Existing empirical models often fall short as...The reliable prediction of hoop strain of fiber-reinforced polymer(FRP)-confined concrete is crucial for assessing confinement efficiency and ensuring structural integrity.Existing empirical models often fall short as a result of idealized assumptions and limited generalizability across diverse materials and geometries.This study presents a novel,data-driven machine learning(ML)approach to estimate the effective hoop strain of FRP-confined circular concrete columns.A refined database comprising 309 experimental specimens,including Carbon,glass,and aramid FRPs,was used.Eight ML algorithms,encompassing both single(K-Nearest Neighbors,Kernel Ridge Regression,Support Vector Regression,Decision Tree)and ensemble(AdaBoost,Gradient Boosting Machine,Extreme Gradient Boosting,Random Forest)models,were trained and optimized using Optuna with 10-fold cross-validation.The top-performing models have coefficient of determination of greater than 95%as well as low residual variance and error on the full data set.Accordingly,SHapley Additive exPlanations were incorporated for global and local interpretability of the model predictions.The best-performing model was deployed in a user-friendly graphical interface,aiding an accurate and interpretable tool for practitioners.The proposed framework significantly outperforms conventional empirical models,offering a scalable solution for assessing hoop strain of FRP-confined concrete.展开更多
文摘Given the growing emphasis on life-cycle analysis in bridge design,the design community is transitioning from the concept of performance-based design in structural engineering to a performance-based design approach within a life-cycle context.This approach considers various indicators,including cost,environmental impact,and societal factors when designing bridges.This shift enables a comprehensive assessment of structural resilience by exam-ining the bridge’s ability to endure various hazards throughout its lifespan.This study provides a comprehensive review of two key research domains that have emerged in the field of bridge life-cycle analysis,namely life-cycle sustainability(LCS)and life-cycle performance(LCP).The discussion on the LCS of bridges encompasses both assessment-based and optimization-based studies,while the exploration of LCP focuses on research examining structures subjected to deterioration over their service life due to deprecating phenomena such as corrosion and relative humidity changes,as well as extreme hazards like earthquakes and floods.Moreover,this study discusses the integration between LCS and LCP,highlighting how combined consideration of these factors can minimize damage costs,improve resiliency,and extend the lifespan of the structure.A detailed evaluation encompasses various life-cycle metrics,structural performance indicators,time-dependent modelling techniques,and analy-sis methods proposed in the literature.Additionally,the research identifies critical gaps and trends in life-cycle analysis within the realm of bridge engineering,providing a concise yet thorough overview for advancing con-siderations in the life-cycle design of bridges.
文摘Efficient seismic vulnerability and resilience assessment is essential for ultra-high-performance concrete(UHPC)bridges,given their distinctive mechanical and structural properties.However,existing single-parameter-based probabilistic seismic demand(PSD)models overlook critical bridge‐specific characteristics and uncertainties.Besides,studies on seismic vulnerability and resilience assessment of UHPC bridges are scarce.Thus,this study proposes a hybrid machine learning(ML)-enabled multivariate bridge-specific seismic vulnerability and resilience assessment framework for UHPC bridges.Key design parameters and associated uncertainties are identified,and a Latin Hypercube Sampling(LHS)technique is employed to establish a representative UHPC bridge database,which is used to develop a hybrid ML model-based multivariate PSD model.A comparative analysis with the conventional PSD model,as well as widely used ML algorithms,demonstrated that the proposed PSD model achieves the highest predictive performance,characterized by the highest coefficient of determination and lowest prediction errors.Additionally,SHapley Additive exPlanation(SHAP)analysis is used to investigate the effect of different parameters on the PSD of UHPC bridges.The results of SHAP show the peak ground acceleration(PGA)as the most important factor,followed by bridge span and column diameter.The hybrid ML-enabled multi-variate bridge-specific fragility analysis results are used to investigate the functionality recovery and resilience of the bridge,which demonstrate the reduction in the residual functionality and overall bridge resilience with the increase in the ground motion intensity.
文摘The reliable prediction of hoop strain of fiber-reinforced polymer(FRP)-confined concrete is crucial for assessing confinement efficiency and ensuring structural integrity.Existing empirical models often fall short as a result of idealized assumptions and limited generalizability across diverse materials and geometries.This study presents a novel,data-driven machine learning(ML)approach to estimate the effective hoop strain of FRP-confined circular concrete columns.A refined database comprising 309 experimental specimens,including Carbon,glass,and aramid FRPs,was used.Eight ML algorithms,encompassing both single(K-Nearest Neighbors,Kernel Ridge Regression,Support Vector Regression,Decision Tree)and ensemble(AdaBoost,Gradient Boosting Machine,Extreme Gradient Boosting,Random Forest)models,were trained and optimized using Optuna with 10-fold cross-validation.The top-performing models have coefficient of determination of greater than 95%as well as low residual variance and error on the full data set.Accordingly,SHapley Additive exPlanations were incorporated for global and local interpretability of the model predictions.The best-performing model was deployed in a user-friendly graphical interface,aiding an accurate and interpretable tool for practitioners.The proposed framework significantly outperforms conventional empirical models,offering a scalable solution for assessing hoop strain of FRP-confined concrete.
