Identifying workers’construction activities or behaviors can enable managers to better monitor labor efficiency and construction progress.However,current activity analysis methods for construction workers rely solely...Identifying workers’construction activities or behaviors can enable managers to better monitor labor efficiency and construction progress.However,current activity analysis methods for construction workers rely solely on manual observations and recordings,which consumes considerable time and has high labor costs.Researchers have focused on monitoring on-site construction activities of workers.However,when multiple workers are working together,current research cannot accu rately and automatically identify the construction activity.This research proposes a deep learning framework for the automated analysis of the construction activities of multiple workers.In this framework,multiple deep neural network models are designed and used to complete worker key point extraction,worker tracking,and worker construction activity analysis.The designed framework was tested at an actual construction site,and activity recognition for multiple workers was performed,indicating the feasibility of the framework for the automated monitoring of work efficiency.展开更多
A large-span steel–concrete composite beam with precast hollow core slabs(CBHCSs)is a relatively new floor structure that can be applied to various long-span structures.However,human-induced vibrations may present se...A large-span steel–concrete composite beam with precast hollow core slabs(CBHCSs)is a relatively new floor structure that can be applied to various long-span structures.However,human-induced vibrations may present serviceability issues in such structures.To alleviate vibrations,both the walking forces excited by humans and the associated floor responses must be elucidated.In this study,150 load–time histories of walking,excited by 25 test participants,are obtained using a force measuring plate.The dynamic loading factors and phase angles in the Fourier series functions for one-step walking are determined.Subsequently,walking tests are performed on seven CBHCS specimens to capture the essential dynamic properties of mode shapes,natural frequencies,damping ratios,and acceleration time histories.The CBHCS floor system generally exhibits a high frequency(>10 Hz)and low damping(damping ratio<2%).Sensitivity studies using the finite element method are conducted to investigate the vibration performance of the CBHCS floor system,where the floor thickness,steel beam type,contact time,and human weight are considered.Finally,analytical expressions derived for the fundamental frequency and peak acceleration agree well with the experimental results and are hence proposed for practical use.展开更多
Steel structures are widely used;however,their traditional design method is a trial-and-error procedure which is neither efficient nor cost effective.Therefore,a multi-population particle swarm optimization(MPPSO)algo...Steel structures are widely used;however,their traditional design method is a trial-and-error procedure which is neither efficient nor cost effective.Therefore,a multi-population particle swarm optimization(MPPSO)algorithm is developed to optimize the weight of steel frames according to standard design codes.Modifications are made to improve the algorithm performances including the constraint-based strategy,piecewise mean learning strategy and multi-population cooperative strategy.The proposed method is tested against the representative frame taken from American standards and against other steel frames matching Chinese design codes.The related parameter influences on optimization results are discussed.For the representative frame,MPPSO can achieve greater efficiency through reduction of the number of analyses by more than 65% and can obtain frame with the weight for at least 2.4%lighter.A similar trend can also be observed in cases subjected to Chinese design codes.In addition,a migration interval of 1 and the number of populations as 5 are recommended to obtain better MPPSO results.The purpose of the study is to propose a method with high efficiency and robustness that is not confined to structural scales and design codes.It aims to provide a reference for automatic structural optimization design problems even with dimensional complexity.The proposed method can be easily generalized to the optimization problem of other structural systems.展开更多
基金supported by the National Natural Science Foundation of China(52130801,U20A20312,52178271,and 52077213)the National Key Research and Development Program of China(2021YFF0500903)。
文摘Identifying workers’construction activities or behaviors can enable managers to better monitor labor efficiency and construction progress.However,current activity analysis methods for construction workers rely solely on manual observations and recordings,which consumes considerable time and has high labor costs.Researchers have focused on monitoring on-site construction activities of workers.However,when multiple workers are working together,current research cannot accu rately and automatically identify the construction activity.This research proposes a deep learning framework for the automated analysis of the construction activities of multiple workers.In this framework,multiple deep neural network models are designed and used to complete worker key point extraction,worker tracking,and worker construction activity analysis.The designed framework was tested at an actual construction site,and activity recognition for multiple workers was performed,indicating the feasibility of the framework for the automated monitoring of work efficiency.
基金The authors acknowledge the financial support provided by the National Natural Science Foundation of China(51890902 and 51708058).
文摘A large-span steel–concrete composite beam with precast hollow core slabs(CBHCSs)is a relatively new floor structure that can be applied to various long-span structures.However,human-induced vibrations may present serviceability issues in such structures.To alleviate vibrations,both the walking forces excited by humans and the associated floor responses must be elucidated.In this study,150 load–time histories of walking,excited by 25 test participants,are obtained using a force measuring plate.The dynamic loading factors and phase angles in the Fourier series functions for one-step walking are determined.Subsequently,walking tests are performed on seven CBHCS specimens to capture the essential dynamic properties of mode shapes,natural frequencies,damping ratios,and acceleration time histories.The CBHCS floor system generally exhibits a high frequency(>10 Hz)and low damping(damping ratio<2%).Sensitivity studies using the finite element method are conducted to investigate the vibration performance of the CBHCS floor system,where the floor thickness,steel beam type,contact time,and human weight are considered.Finally,analytical expressions derived for the fundamental frequency and peak acceleration agree well with the experimental results and are hence proposed for practical use.
基金supported by National Natural Science Foundation of China(Grant Nos.52308142 and 52208185)Postdoctoral Fellowship Program of CPSF(No.GZC20233334)+1 种基金Special Support of Chongqing Postdoctoral Science Foundation(No.2021XM2039)National Key Research and Development Program of China(No.2022YFC3801700).
文摘Steel structures are widely used;however,their traditional design method is a trial-and-error procedure which is neither efficient nor cost effective.Therefore,a multi-population particle swarm optimization(MPPSO)algorithm is developed to optimize the weight of steel frames according to standard design codes.Modifications are made to improve the algorithm performances including the constraint-based strategy,piecewise mean learning strategy and multi-population cooperative strategy.The proposed method is tested against the representative frame taken from American standards and against other steel frames matching Chinese design codes.The related parameter influences on optimization results are discussed.For the representative frame,MPPSO can achieve greater efficiency through reduction of the number of analyses by more than 65% and can obtain frame with the weight for at least 2.4%lighter.A similar trend can also be observed in cases subjected to Chinese design codes.In addition,a migration interval of 1 and the number of populations as 5 are recommended to obtain better MPPSO results.The purpose of the study is to propose a method with high efficiency and robustness that is not confined to structural scales and design codes.It aims to provide a reference for automatic structural optimization design problems even with dimensional complexity.The proposed method can be easily generalized to the optimization problem of other structural systems.
