Design of building construction safety prediction model based on optimized BP neural network algorithm
In order to solve the safety problem of the construction industry, the construction safety prediction model based on the optimized BP neural network algorithm is designed in this study. First, the characteristics of the construction industry were analyzed. As a labor-intensive industry, the construc...
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| Published in | Soft Computing Vol. 24; no. 11; pp. 7839 - 7850 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English Japanese |
| Published |
Berlin/Heidelberg
Springer Science and Business Media LLC
01.06.2020
Springer Berlin Heidelberg Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1432-7643 1433-7479 |
| DOI | 10.1007/s00500-019-03917-4 |
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| Abstract | In order to solve the safety problem of the construction industry, the construction safety prediction model based on the optimized BP neural network algorithm is designed in this study. First, the characteristics of the construction industry were analyzed. As a labor-intensive industry, the construction industry is characterized by numerous factors such as large investment, long construction period and complicated construction environment. Due to the increasingly serious security problem, widespread concern over such problem has been aroused in society. Second, the problem of building construction safety management was summarized, six influencing factors were explored and a building construction safety prediction model based on rough set-genetic-BP neural network was established. Finally, the model was validated by a combination of multiparty consultation, empirical analysis and model comparison. The results showed that the model accurately predicted the risk factors during the construction process and effectively reduced casualties. Therefore, the model is feasible, effective and accurate. |
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| AbstractList | In order to solve the safety problem of the construction industry, the construction safety prediction model based on the optimized BP neural network algorithm is designed in this study. First, the characteristics of the construction industry were analyzed. As a labor-intensive industry, the construction industry is characterized by numerous factors such as large investment, long construction period and complicated construction environment. Due to the increasingly serious security problem, widespread concern over such problem has been aroused in society. Second, the problem of building construction safety management was summarized, six influencing factors were explored and a building construction safety prediction model based on rough set-genetic-BP neural network was established. Finally, the model was validated by a combination of multiparty consultation, empirical analysis and model comparison. The results showed that the model accurately predicted the risk factors during the construction process and effectively reduced casualties. Therefore, the model is feasible, effective and accurate. |
| Author | Yukari Nagai Chan Gao Tao Shen |
| Author_xml | – sequence: 1 givenname: Tao surname: Shen fullname: Shen, Tao email: shentao@jaist.ac.jp organization: Knowledge Science, Japan Advanced Institute of Science and Technology – sequence: 2 givenname: Yukari surname: Nagai fullname: Nagai, Yukari organization: Knowledge Science, Japan Advanced Institute of Science and Technology – sequence: 3 givenname: Chan surname: Gao fullname: Gao, Chan organization: Architecture Department, Huzhou University |
| BackLink | https://cir.nii.ac.jp/crid/1871146593167722368$$DView record in CiNii |
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| Cites_doi | 10.1016/j.autcon.2016.09.003 10.1007/s00521-016-2190-2 10.1061/(ASCE)EI.1943-5541.0000278 10.1016/j.advengsoft.2014.11.003 10.1007/s00366-015-0400-7 10.1016/j.resourpol.2014.10.011 10.1016/j.neucom.2016.09.027 10.1016/j.media.2014.10.007 10.1016/j.autcon.2015.11.003 10.1007/s00366-015-0415-0 10.1016/j.knosys.2015.05.017 10.1016/j.enconman.2016.02.013 10.1007/s12665-015-4274-1 10.1016/j.enconman.2014.12.053 10.1016/j.resconrec.2017.10.020 10.1016/j.tourman.2015.10.001 10.1007/s10064-014-0657-x 10.1111/coin.12061 10.1016/j.enbuild.2014.11.063 10.1007/s13278-017-0484-8 10.1007/s10346-015-0557-6 10.1007/s11042-016-4319-9 10.1504/IJCSYSE.2016.079000 |
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| References | Chen, Tsai (CR3) 2016; 53 Yu, Li, Jia (CR25) 2015; 88 Wang, Kim, Shi (CR21) 2015; 20 Dutta, Ghatak, Dey, Das, Ghosh (CR4) 2018; 8 Hajihassani, Armaghani, Marto (CR7) 2015; 74 Liu, Huo, Liang, Sun, Hu (CR17) 2016; 142 Gordan, Armaghani, Hajihassani (CR6) 2016; 32 Leu, Liu (CR13) 2016; 24 Meng, Ge, Yin (CR18) 2016; 114 Jia, Shang, Zhou, Yao (CR10) 2016; 91 Li, Ruan, Shen, Hermans, Wets (CR14) 2016; 32 Kusi-Sarpong, Bai, Sarkis, Wang (CR12) 2015; 46 Hu, Wen, Zeng, Huang (CR9) 2017; 221 Yi, Chan, Wang (CR24) 2016; 62 Liu, Tian, Li (CR16) 2015; 92 Hajihassani, Armaghani, Monjezi (CR8) 2015; 74 Roy, Viswanatham, Krishna (CR19) 2016; 2 Bui, Tuan, Klempe (CR1) 2016; 13 Liou, Chuang, Hsu (CR15) 2016; 33 Zhang, Wu, Zhu, AbouRizk (CR26) 2017; 73 Chatterjee, Sarkar, Hore, Dey, Ashour, Balas (CR2) 2017; 28 Kuang, Singh, Singh, Singh (CR11) 2017; 76 Saghatforoush, Monjezi, Faradonbeh (CR20) 2016; 32 Gholizadeh (CR5) 2015; 81 Waziri, Bala, Bustani (CR22) 2017; 6 Ye, Ren, Hu, Lin, Shi, Zhang, Li (CR23) 2018; 129 Q Wang (3917_CR21) 2015; 20 SS Roy (3917_CR19) 2016; 2 M Hajihassani (3917_CR7) 2015; 74 X Jia (3917_CR10) 2016; 91 W Yu (3917_CR25) 2015; 88 Y Kuang (3917_CR11) 2017; 76 A Meng (3917_CR18) 2016; 114 L Zhang (3917_CR26) 2017; 73 S Dutta (3917_CR4) 2018; 8 S Kusi-Sarpong (3917_CR12) 2015; 46 S Gholizadeh (3917_CR5) 2015; 81 H Liu (3917_CR16) 2015; 92 W Yi (3917_CR24) 2016; 62 H Ye (3917_CR23) 2018; 129 B Liu (3917_CR17) 2016; 142 BS Waziri (3917_CR22) 2017; 6 SS Leu (3917_CR13) 2016; 24 M Hajihassani (3917_CR8) 2015; 74 T Li (3917_CR14) 2016; 32 S Chatterjee (3917_CR2) 2017; 28 A Saghatforoush (3917_CR20) 2016; 32 B Gordan (3917_CR6) 2016; 32 JJ Liou (3917_CR15) 2016; 33 DT Bui (3917_CR1) 2016; 13 LF Chen (3917_CR3) 2016; 53 R Hu (3917_CR9) 2017; 221 |
| References_xml | – volume: 73 start-page: 88 year: 2017 end-page: 101 ident: CR26 article-title: Perceiving safety risk of buildings adjacent to tunneling excavation: an information fusion approach publication-title: Autom Constr doi: 10.1016/j.autcon.2016.09.003 – volume: 28 start-page: 2005 issue: 8 year: 2017 end-page: 2016 ident: CR2 article-title: Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings publication-title: Neural Comput Appl doi: 10.1007/s00521-016-2190-2 – volume: 142 start-page: 05016003 issue: 4 year: 2016 ident: CR17 article-title: Key factors of project characteristics affecting project delivery system decision making in the Chinese construction industry: case study using Chinese data based on rough set theory publication-title: J Prof Issues Eng Educ Pract doi: 10.1061/(ASCE)EI.1943-5541.0000278 – volume: 81 start-page: 50 year: 2015 end-page: 65 ident: CR5 article-title: Performance-based optimum seismic design of steel structures by a modified firefly algorithm and a new neural network publication-title: Adv Eng Softw doi: 10.1016/j.advengsoft.2014.11.003 – volume: 32 start-page: 85 issue: 1 year: 2016 end-page: 97 ident: CR6 article-title: Prediction of seismic slope stability through combination of particle swarm optimization and neural network publication-title: Eng Comput doi: 10.1007/s00366-015-0400-7 – volume: 46 start-page: 86 year: 2015 end-page: 100 ident: CR12 article-title: Green supply chain practices evaluation in the mining industry using a joint rough sets and fuzzy TOPSIS methodology publication-title: Resour Policy doi: 10.1016/j.resourpol.2014.10.011 – volume: 221 start-page: 24 year: 2017 end-page: 31 ident: CR9 article-title: A short-term power load forecasting model based on the generalized regression neural network with decreasing step fruit fly optimization algorithm publication-title: Neurocomputing doi: 10.1016/j.neucom.2016.09.027 – volume: 24 start-page: 82 issue: 2 year: 2016 end-page: 90 ident: CR13 article-title: Using principal component analysis with a back-propagation neural network to predict industrial building construction duration publication-title: J Mar Sci Technol – volume: 20 start-page: 61 issue: 1 year: 2015 end-page: 75 ident: CR21 article-title: Predict brain MR image registration via sparse learning of appearance and transformation publication-title: Med Image Anal doi: 10.1016/j.media.2014.10.007 – volume: 62 start-page: 101 year: 2016 end-page: 113 ident: CR24 article-title: Development of an early-warning system for site work in hot and humid environments: a case study publication-title: Autom Constr doi: 10.1016/j.autcon.2015.11.003 – volume: 32 start-page: 255 issue: 2 year: 2016 end-page: 266 ident: CR20 article-title: Combination of neural network and ant colony optimization algorithms for prediction and optimization of flyrock and back-break induced by blasting publication-title: Eng Comput doi: 10.1007/s00366-015-0415-0 – volume: 91 start-page: 204 year: 2016 end-page: 218 ident: CR10 article-title: Generalized attribute reduct in rough set theory publication-title: Knowl Based Syst doi: 10.1016/j.knosys.2015.05.017 – volume: 114 start-page: 75 year: 2016 end-page: 88 ident: CR18 article-title: Wind speed forecasting based on wavelet packet decomposition and artificial neural networks trained by crisscross optimization algorithm publication-title: Energy Convers Manag doi: 10.1016/j.enconman.2016.02.013 – volume: 74 start-page: 2799 issue: 4 year: 2015 end-page: 2817 ident: CR8 article-title: Blast-induced air and ground vibration prediction: a particle swarm optimization-based artificial neural network approach publication-title: Environ Earth Sci doi: 10.1007/s12665-015-4274-1 – volume: 92 start-page: 67 year: 2015 end-page: 81 ident: CR16 article-title: Comparison of four Adaboost algorithm based artificial neural networks in wind speed predictions publication-title: Energy Convers Manag doi: 10.1016/j.enconman.2014.12.053 – volume: 129 start-page: 168 year: 2018 end-page: 174 ident: CR23 article-title: Modeling energy-related CO 2 emissions from office buildings using general regression neural network publication-title: Resour Conserv Recycl doi: 10.1016/j.resconrec.2017.10.020 – volume: 53 start-page: 197 year: 2016 end-page: 206 ident: CR3 article-title: Data mining framework based on rough set theory to improve location selection decisions: a case study of a restaurant chain publication-title: Tour Manag doi: 10.1016/j.tourman.2015.10.001 – volume: 74 start-page: 873 issue: 3 year: 2015 end-page: 886 ident: CR7 article-title: Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm publication-title: Bull Eng Geol Env doi: 10.1007/s10064-014-0657-x – volume: 32 start-page: 517 issue: 4 year: 2016 end-page: 534 ident: CR14 article-title: A new weighting approach based on rough set theory and granular computing for road safety indicator analysis publication-title: Comput Intell doi: 10.1111/coin.12061 – volume: 88 start-page: 135 year: 2015 end-page: 143 ident: CR25 article-title: Application of multi-objective genetic algorithm to optimize energy efficiency and thermal comfort in building design publication-title: Energy Build doi: 10.1016/j.enbuild.2014.11.063 – volume: 8 start-page: 7 issue: 1 year: 2018 ident: CR4 article-title: Attribute selection for improving spam classification in online social networks: a rough set theory-based approach publication-title: Soc Netw Anal Min doi: 10.1007/s13278-017-0484-8 – volume: 6 start-page: 50 issue: 1 year: 2017 end-page: 60 ident: CR22 article-title: Artificial neural networks in construction engineering and management publication-title: Int J Arch Eng Constr – volume: 33 start-page: 123 issue: 2 year: 2016 end-page: 133 ident: CR15 article-title: Improving airline service quality based on rough set theory and flow graphs publication-title: J Ind Prod Eng – volume: 13 start-page: 361 issue: 2 year: 2016 end-page: 378 ident: CR1 article-title: Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree publication-title: Landslides doi: 10.1007/s10346-015-0557-6 – volume: 76 start-page: 18749 issue: 18 year: 2017 end-page: 18770 ident: CR11 article-title: A novel macroeconomic forecasting model based on revised multimedia assisted BP neural network model and ant Colony algorithm publication-title: Multimedia Tools Appl doi: 10.1007/s11042-016-4319-9 – volume: 2 start-page: 139 issue: 3 year: 2016 end-page: 147 ident: CR19 article-title: Spam detection using hybrid model of rough set and decorate ensemble publication-title: Int J Comput Syst Eng doi: 10.1504/IJCSYSE.2016.079000 – volume: 74 start-page: 2799 issue: 4 year: 2015 ident: 3917_CR8 publication-title: Environ Earth Sci doi: 10.1007/s12665-015-4274-1 – volume: 88 start-page: 135 year: 2015 ident: 3917_CR25 publication-title: Energy Build doi: 10.1016/j.enbuild.2014.11.063 – volume: 91 start-page: 204 year: 2016 ident: 3917_CR10 publication-title: Knowl Based Syst doi: 10.1016/j.knosys.2015.05.017 – volume: 62 start-page: 101 year: 2016 ident: 3917_CR24 publication-title: Autom Constr doi: 10.1016/j.autcon.2015.11.003 – volume: 76 start-page: 18749 issue: 18 year: 2017 ident: 3917_CR11 publication-title: Multimedia Tools Appl doi: 10.1007/s11042-016-4319-9 – volume: 28 start-page: 2005 issue: 8 year: 2017 ident: 3917_CR2 publication-title: Neural Comput Appl doi: 10.1007/s00521-016-2190-2 – volume: 8 start-page: 7 issue: 1 year: 2018 ident: 3917_CR4 publication-title: Soc Netw Anal Min doi: 10.1007/s13278-017-0484-8 – volume: 24 start-page: 82 issue: 2 year: 2016 ident: 3917_CR13 publication-title: J Mar Sci Technol – volume: 53 start-page: 197 year: 2016 ident: 3917_CR3 publication-title: Tour Manag doi: 10.1016/j.tourman.2015.10.001 – volume: 32 start-page: 517 issue: 4 year: 2016 ident: 3917_CR14 publication-title: Comput Intell doi: 10.1111/coin.12061 – volume: 129 start-page: 168 year: 2018 ident: 3917_CR23 publication-title: Resour Conserv Recycl doi: 10.1016/j.resconrec.2017.10.020 – volume: 13 start-page: 361 issue: 2 year: 2016 ident: 3917_CR1 publication-title: Landslides doi: 10.1007/s10346-015-0557-6 – volume: 32 start-page: 85 issue: 1 year: 2016 ident: 3917_CR6 publication-title: Eng Comput doi: 10.1007/s00366-015-0400-7 – volume: 73 start-page: 88 year: 2017 ident: 3917_CR26 publication-title: Autom Constr doi: 10.1016/j.autcon.2016.09.003 – volume: 20 start-page: 61 issue: 1 year: 2015 ident: 3917_CR21 publication-title: Med Image Anal doi: 10.1016/j.media.2014.10.007 – volume: 74 start-page: 873 issue: 3 year: 2015 ident: 3917_CR7 publication-title: Bull Eng Geol Env doi: 10.1007/s10064-014-0657-x – volume: 142 start-page: 05016003 issue: 4 year: 2016 ident: 3917_CR17 publication-title: J Prof Issues Eng Educ Pract doi: 10.1061/(ASCE)EI.1943-5541.0000278 – volume: 114 start-page: 75 year: 2016 ident: 3917_CR18 publication-title: Energy Convers Manag doi: 10.1016/j.enconman.2016.02.013 – volume: 221 start-page: 24 year: 2017 ident: 3917_CR9 publication-title: Neurocomputing doi: 10.1016/j.neucom.2016.09.027 – volume: 2 start-page: 139 issue: 3 year: 2016 ident: 3917_CR19 publication-title: Int J Comput Syst Eng doi: 10.1504/IJCSYSE.2016.079000 – volume: 46 start-page: 86 year: 2015 ident: 3917_CR12 publication-title: Resour Policy doi: 10.1016/j.resourpol.2014.10.011 – volume: 81 start-page: 50 year: 2015 ident: 3917_CR5 publication-title: Adv Eng Softw doi: 10.1016/j.advengsoft.2014.11.003 – volume: 33 start-page: 123 issue: 2 year: 2016 ident: 3917_CR15 publication-title: J Ind Prod Eng – volume: 6 start-page: 50 issue: 1 year: 2017 ident: 3917_CR22 publication-title: Int J Arch Eng Constr – volume: 92 start-page: 67 year: 2015 ident: 3917_CR16 publication-title: Energy Convers Manag doi: 10.1016/j.enconman.2014.12.053 – volume: 32 start-page: 255 issue: 2 year: 2016 ident: 3917_CR20 publication-title: Eng Comput doi: 10.1007/s00366-015-0415-0 |
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| Title | Design of building construction safety prediction model based on optimized BP neural network algorithm |
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