Causality,the science of cause and effect,has made it possible to create a new family of models.Such models are often referred to as causal models.Unlike those of mathematical,numerical,empirical,or machine learning(M...Causality,the science of cause and effect,has made it possible to create a new family of models.Such models are often referred to as causal models.Unlike those of mathematical,numerical,empirical,or machine learning(ML)nature,causal models hope to tie the cause(s)to the effect(s)pertaining to a phenomenon(i.e.,data generating process)through causal principles.This paper presents one of the first works at creating causal models in the area of structural and construction engineering.To this end,this paper starts with a brief review of the principles of causality and then adopts four causal discovery algorithms,namely,PC(Peter-Clark),FCI(fast causal inference),GES(greedy equivalence search),and GRa SP(greedy relaxation of the sparsest permutation),have been used to examine four phenomena,including predicting the load-bearing capacity of axially loaded members,fire resistance of structural members,shear strength of beams,and resistance of walls against impulsive(blast)loading.Findings from this study reveal the possibility and merit of discovering complete and partial causal models.Finally,this study also proposes two simple metrics that can help assess the performance of causal discovery algorithms.展开更多
This paper reviews the fire problem in critical transportation infrastructures such as bridges and tunnels.The magnitude of the fire problem is illustrated,and the recent increase in fire problems in bridges and tunne...This paper reviews the fire problem in critical transportation infrastructures such as bridges and tunnels.The magnitude of the fire problem is illustrated,and the recent increase in fire problems in bridges and tunnels is highlighted.Recent research undertaken to address fire problems in transportation structures is reviewed,as well as critical factors governing the performance of those structures.Furthermore,key strategies recommended for mitigating fire hazards in bridges and tunnels are presented,and their applicability to practical situations is demonstrated through a practical case study.Furthermore,research needs and emerging trends for enhancing the“state-of-the-art”in this area are discussed.展开更多
This paper investigates the effect of differential support settlement on shear strength and behavior of continuous reinforced concrete(RC)deep beams.A total of twenty three-dimensional nonlinear finite element models ...This paper investigates the effect of differential support settlement on shear strength and behavior of continuous reinforced concrete(RC)deep beams.A total of twenty three-dimensional nonlinear finite element models were developed taking into account various constitutive laws for concrete material in compression(crushing)and tension(cracking),steel plasticity(i.e.,yielding and strain hardening),bond-slip at the concrete and steel reinforcement interface as well as unique behavior of spring-like support elements.These models are first validated by comparing numerical predictions in terms of load-deflection response,crack propagation,reaction distribution,and failure mode against that of measured experimental data reported in literature.Once the developed models were successfully validated,a parametric study was designed and performed.This parametric study examined number of critical parameters such as ratio and spacing of the longitudinal and vertical reinforcement,compressive and tensile strength of concrete,as well as degree(stiffness)and location of support stiffness to induce varying levels of differential settlement.This study also aims at presenting a numerical approach using finite element simulation,supplemented with coherent assumptions,such that engineers,practitioners,and researchers can carry out simple,but yet effective and realistic analysis of RC structural members undergoing differential settlements due to variety of load actions.展开更多
This paper introduces a machine learning approach to address the challenge of limited data resulting from costly and time-consuming fire experiments by enlarging small fire test data sets and predicting the fire resis...This paper introduces a machine learning approach to address the challenge of limited data resulting from costly and time-consuming fire experiments by enlarging small fire test data sets and predicting the fire resistance of reinforced concrete columns.Our approach begins by creating deep learning models,namely generative adversarial networks and variational autoencoders,to learn the spatial distribution of real fire tests.We then use these models to generate synthetic tabular samples that closely resemble realistic fire resistance values for reinforced concrete columns.The generated data are employed to train state-of-the-art machine learning techniques,including Extreme Gradient Boost,Light Gradient Boosting Machine,Categorical Boosting Algorithm,Support Vector Regression,Random Forest,Decision Tree,Multiple Linear Regression,Polynomial Regression,Support Vector Machine,Kernel Support Vector Machine,Naive Bayes,and K-Nearest Neighbors,which can predict the fire resistance of the columns through regression and classification.Machine learning analyses achieved highly accurate predictions of fire resistance values,outperforming traditional models that relied solely on limited experimental data.Our study highlights the potential for using machine learning and deep learning analyses to revolutionize the field of structural engineering by improving the accuracy and efficiency of fire resistance evaluations while reducing the reliance on costly and time-consuming experiments.展开更多
Causality is the science of cause and effect.It is through causality that explanations can be derived,theories can be formed,and new knowledge can be discovered.This paper presents a modern look into establishing caus...Causality is the science of cause and effect.It is through causality that explanations can be derived,theories can be formed,and new knowledge can be discovered.This paper presents a modern look into establishing causality within structural engineering systems.In this pursuit,this paper starts with a gentle introduction to causality.Then,this paper pivots to contrast commonly adopted methods for inferring causes and effects,i.e.,induction(empiricism)and deduc-tion(rationalism),and outlines how these methods continue to shape our structural engineering philosophy and,by extension,our domain.The bulk of this paper is dedicated to establishing an approach and criteria to tie principles of induction and deduction to derive causal laws(i.e.,mapping functions)through explainable artificial intelligence(XAI)capable of describing new knowledge pertaining to structural engineering phenomena.The proposed approach and criteria are then examined via a case study.展开更多
文摘Causality,the science of cause and effect,has made it possible to create a new family of models.Such models are often referred to as causal models.Unlike those of mathematical,numerical,empirical,or machine learning(ML)nature,causal models hope to tie the cause(s)to the effect(s)pertaining to a phenomenon(i.e.,data generating process)through causal principles.This paper presents one of the first works at creating causal models in the area of structural and construction engineering.To this end,this paper starts with a brief review of the principles of causality and then adopts four causal discovery algorithms,namely,PC(Peter-Clark),FCI(fast causal inference),GES(greedy equivalence search),and GRa SP(greedy relaxation of the sparsest permutation),have been used to examine four phenomena,including predicting the load-bearing capacity of axially loaded members,fire resistance of structural members,shear strength of beams,and resistance of walls against impulsive(blast)loading.Findings from this study reveal the possibility and merit of discovering complete and partial causal models.Finally,this study also proposes two simple metrics that can help assess the performance of causal discovery algorithms.
基金This study was supported by the National Science Foundation(No.CMMI-1068621).
文摘This paper reviews the fire problem in critical transportation infrastructures such as bridges and tunnels.The magnitude of the fire problem is illustrated,and the recent increase in fire problems in bridges and tunnels is highlighted.Recent research undertaken to address fire problems in transportation structures is reviewed,as well as critical factors governing the performance of those structures.Furthermore,key strategies recommended for mitigating fire hazards in bridges and tunnels are presented,and their applicability to practical situations is demonstrated through a practical case study.Furthermore,research needs and emerging trends for enhancing the“state-of-the-art”in this area are discussed.
文摘This paper investigates the effect of differential support settlement on shear strength and behavior of continuous reinforced concrete(RC)deep beams.A total of twenty three-dimensional nonlinear finite element models were developed taking into account various constitutive laws for concrete material in compression(crushing)and tension(cracking),steel plasticity(i.e.,yielding and strain hardening),bond-slip at the concrete and steel reinforcement interface as well as unique behavior of spring-like support elements.These models are first validated by comparing numerical predictions in terms of load-deflection response,crack propagation,reaction distribution,and failure mode against that of measured experimental data reported in literature.Once the developed models were successfully validated,a parametric study was designed and performed.This parametric study examined number of critical parameters such as ratio and spacing of the longitudinal and vertical reinforcement,compressive and tensile strength of concrete,as well as degree(stiffness)and location of support stiffness to induce varying levels of differential settlement.This study also aims at presenting a numerical approach using finite element simulation,supplemented with coherent assumptions,such that engineers,practitioners,and researchers can carry out simple,but yet effective and realistic analysis of RC structural members undergoing differential settlements due to variety of load actions.
文摘This paper introduces a machine learning approach to address the challenge of limited data resulting from costly and time-consuming fire experiments by enlarging small fire test data sets and predicting the fire resistance of reinforced concrete columns.Our approach begins by creating deep learning models,namely generative adversarial networks and variational autoencoders,to learn the spatial distribution of real fire tests.We then use these models to generate synthetic tabular samples that closely resemble realistic fire resistance values for reinforced concrete columns.The generated data are employed to train state-of-the-art machine learning techniques,including Extreme Gradient Boost,Light Gradient Boosting Machine,Categorical Boosting Algorithm,Support Vector Regression,Random Forest,Decision Tree,Multiple Linear Regression,Polynomial Regression,Support Vector Machine,Kernel Support Vector Machine,Naive Bayes,and K-Nearest Neighbors,which can predict the fire resistance of the columns through regression and classification.Machine learning analyses achieved highly accurate predictions of fire resistance values,outperforming traditional models that relied solely on limited experimental data.Our study highlights the potential for using machine learning and deep learning analyses to revolutionize the field of structural engineering by improving the accuracy and efficiency of fire resistance evaluations while reducing the reliance on costly and time-consuming experiments.
文摘Causality is the science of cause and effect.It is through causality that explanations can be derived,theories can be formed,and new knowledge can be discovered.This paper presents a modern look into establishing causality within structural engineering systems.In this pursuit,this paper starts with a gentle introduction to causality.Then,this paper pivots to contrast commonly adopted methods for inferring causes and effects,i.e.,induction(empiricism)and deduc-tion(rationalism),and outlines how these methods continue to shape our structural engineering philosophy and,by extension,our domain.The bulk of this paper is dedicated to establishing an approach and criteria to tie principles of induction and deduction to derive causal laws(i.e.,mapping functions)through explainable artificial intelligence(XAI)capable of describing new knowledge pertaining to structural engineering phenomena.The proposed approach and criteria are then examined via a case study.
