Early Warning for the Construction Safety Risk of Bridge Projects Using a RS-SSA-LSSVM Model

Bridge engineering is an important component of the transportation system, and early warnings of construction safety risks are crucial for bridge engineering construction safety. To solve the challenges faced by early warnings risk and the low early warning accuracy in bridge construction safety, th...

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Autores principales:	Gang Li, Ruijiang Ran, Jun Fang, Hao Peng, Shengmin Wang
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/ddbbe96643944d9d9dbc9c66523fc641
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spelling	oai:doaj.org-article:ddbbe96643944d9d9dbc9c66523fc6412021-11-15T01:19:19ZEarly Warning for the Construction Safety Risk of Bridge Projects Using a RS-SSA-LSSVM Model1687-809410.1155/2021/4449451https://doaj.org/article/ddbbe96643944d9d9dbc9c66523fc6412021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/4449451https://doaj.org/toc/1687-8094Bridge engineering is an important component of the transportation system, and early warnings of construction safety risks are crucial for bridge engineering construction safety. To solve the challenges faced by early warnings risk and the low early warning accuracy in bridge construction safety, this study proposed a new early-warning model for bridge construction safety risk. The proposed model integrates a rough set (RS), the sparrow search algorithm (SSA), and the least squares support vector machine (LSSVM). In particular, the initial early warning factors of bridge construction safety risk from five factors (men, machines, methods, materials, and environment) were selected, and the RS was used to reduce the attributes of 20 initial early warning factors to obtain the optimized early warning factor set. This overcame the problem of multiple early warning factors and reduced the complexity of the subsequent prediction model. Then, the LSSVM with the strongest nonlinear modelling ability was selected to build the bridge construction early-warning model and adopted the SSA to optimize the LSSVM parameter combination, improving the early warning accuracy. The Longlingshan Project in Wuhan and the Shihe Bridge Project in Xinyang, China, were then selected as case studies for empirical research. Results demonstrated a significant improvement in the performance of the early-warning model following the removal of redundancy or interference factors via the RS. Compared with the standard LSSVM, Back Propagation Neural Network and other traditional early-warning models, the proposed model exhibited higher computational efficiency and a better early warning performance. The research presented in this article has important theoretical and practical significance for the improvement of the early warning management of bridge construction safety risks.Gang LiRuijiang RanJun FangHao PengShengmin WangHindawi LimitedarticleEngineering (General). Civil engineering (General)TA1-2040ENAdvances in Civil Engineering, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Engineering (General). Civil engineering (General) TA1-2040 Gang Li Ruijiang Ran Jun Fang Hao Peng Shengmin Wang Early Warning for the Construction Safety Risk of Bridge Projects Using a RS-SSA-LSSVM Model
description	Bridge engineering is an important component of the transportation system, and early warnings of construction safety risks are crucial for bridge engineering construction safety. To solve the challenges faced by early warnings risk and the low early warning accuracy in bridge construction safety, this study proposed a new early-warning model for bridge construction safety risk. The proposed model integrates a rough set (RS), the sparrow search algorithm (SSA), and the least squares support vector machine (LSSVM). In particular, the initial early warning factors of bridge construction safety risk from five factors (men, machines, methods, materials, and environment) were selected, and the RS was used to reduce the attributes of 20 initial early warning factors to obtain the optimized early warning factor set. This overcame the problem of multiple early warning factors and reduced the complexity of the subsequent prediction model. Then, the LSSVM with the strongest nonlinear modelling ability was selected to build the bridge construction early-warning model and adopted the SSA to optimize the LSSVM parameter combination, improving the early warning accuracy. The Longlingshan Project in Wuhan and the Shihe Bridge Project in Xinyang, China, were then selected as case studies for empirical research. Results demonstrated a significant improvement in the performance of the early-warning model following the removal of redundancy or interference factors via the RS. Compared with the standard LSSVM, Back Propagation Neural Network and other traditional early-warning models, the proposed model exhibited higher computational efficiency and a better early warning performance. The research presented in this article has important theoretical and practical significance for the improvement of the early warning management of bridge construction safety risks.
format	article
author	Gang Li Ruijiang Ran Jun Fang Hao Peng Shengmin Wang
author_facet	Gang Li Ruijiang Ran Jun Fang Hao Peng Shengmin Wang
author_sort	Gang Li
title	Early Warning for the Construction Safety Risk of Bridge Projects Using a RS-SSA-LSSVM Model
title_short	Early Warning for the Construction Safety Risk of Bridge Projects Using a RS-SSA-LSSVM Model
title_full	Early Warning for the Construction Safety Risk of Bridge Projects Using a RS-SSA-LSSVM Model
title_fullStr	Early Warning for the Construction Safety Risk of Bridge Projects Using a RS-SSA-LSSVM Model
title_full_unstemmed	Early Warning for the Construction Safety Risk of Bridge Projects Using a RS-SSA-LSSVM Model
title_sort	early warning for the construction safety risk of bridge projects using a rs-ssa-lssvm model
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/ddbbe96643944d9d9dbc9c66523fc641
work_keys_str_mv	AT gangli earlywarningfortheconstructionsafetyriskofbridgeprojectsusingarsssalssvmmodel AT ruijiangran earlywarningfortheconstructionsafetyriskofbridgeprojectsusingarsssalssvmmodel AT junfang earlywarningfortheconstructionsafetyriskofbridgeprojectsusingarsssalssvmmodel AT haopeng earlywarningfortheconstructionsafetyriskofbridgeprojectsusingarsssalssvmmodel AT shengminwang earlywarningfortheconstructionsafetyriskofbridgeprojectsusingarsssalssvmmodel
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Early Warning for the Construction Safety Risk of Bridge Projects Using a RS-SSA-LSSVM Model

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