Design of building construction safety prediction model based on optimized BP neural network algorithm

  • Tao ShenEmail author
  • Yukari Nagai
  • Chan Gao


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.


Risk factors Genetic algorithm BP network Safety prediction 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Human and animal rights

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.


  1. Bui DT, Tuan TA, Klempe H et al (2016) 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. Landslides 13(2):361–378CrossRefGoogle Scholar
  2. Chatterjee S, Sarkar S, Hore S, Dey N, Ashour AS, Balas VE (2017) Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings. Neural Comput Appl 28(8):2005–2016CrossRefGoogle Scholar
  3. Chen LF, Tsai CT (2016) Data mining framework based on rough set theory to improve location selection decisions: a case study of a restaurant chain. Tour Manag 53:197–206CrossRefGoogle Scholar
  4. Dutta S, Ghatak S, Dey R, Das AK, Ghosh S (2018) Attribute selection for improving spam classification in online social networks: a rough set theory-based approach. Soc Netw Anal Min 8(1):7CrossRefGoogle Scholar
  5. Gholizadeh S (2015) Performance-based optimum seismic design of steel structures by a modified firefly algorithm and a new neural network. Adv Eng Softw 81:50–65CrossRefGoogle Scholar
  6. Gordan B, Armaghani DJ, Hajihassani M et al (2016) Prediction of seismic slope stability through combination of particle swarm optimization and neural network. Eng Comput 32(1):85–97CrossRefGoogle Scholar
  7. Hajihassani M, Armaghani DJ, Marto A et al (2015a) Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm. Bull Eng Geol Env 74(3):873–886CrossRefGoogle Scholar
  8. Hajihassani M, Armaghani DJ, Monjezi M et al (2015b) Blast-induced air and ground vibration prediction: a particle swarm optimization-based artificial neural network approach. Environ Earth Sci 74(4):2799–2817CrossRefGoogle Scholar
  9. Hu R, Wen S, Zeng Z, Huang T (2017) A short-term power load forecasting model based on the generalized regression neural network with decreasing step fruit fly optimization algorithm. Neurocomputing 221:24–31CrossRefGoogle Scholar
  10. Jia X, Shang L, Zhou B, Yao Y (2016) Generalized attribute reduct in rough set theory. Knowl Based Syst 91:204–218CrossRefGoogle Scholar
  11. Kuang Y, Singh R, Singh S, Singh SP (2017) A novel macroeconomic forecasting model based on revised multimedia assisted BP neural network model and ant Colony algorithm. Multimedia Tools Appl 76(18):18749–18770CrossRefGoogle Scholar
  12. Kusi-Sarpong S, Bai C, Sarkis J, Wang X (2015) Green supply chain practices evaluation in the mining industry using a joint rough sets and fuzzy TOPSIS methodology. Resour Policy 46:86–100CrossRefGoogle Scholar
  13. Leu SS, Liu CM (2016) Using principal component analysis with a back-propagation neural network to predict industrial building construction duration. J Mar Sci Technol 24(2):82–90Google Scholar
  14. Li T, Ruan D, Shen Y, Hermans E, Wets G (2016) A new weighting approach based on rough set theory and granular computing for road safety indicator analysis. Comput Intell 32(4):517–534MathSciNetCrossRefGoogle Scholar
  15. Liou JJ, Chuang YC, Hsu CC (2016) Improving airline service quality based on rough set theory and flow graphs. J Ind Prod Eng 33(2):123–133Google Scholar
  16. Liu H, Tian H, Li Y et al (2015) Comparison of four Adaboost algorithm based artificial neural networks in wind speed predictions. Energy Convers Manag 92:67–81CrossRefGoogle Scholar
  17. Liu B, Huo T, Liang Y, Sun Y, Hu X (2016) 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. J Prof Issues Eng Educ Pract 142(4):05016003CrossRefGoogle Scholar
  18. Meng A, Ge J, Yin H et al (2016) Wind speed forecasting based on wavelet packet decomposition and artificial neural networks trained by crisscross optimization algorithm. Energy Convers Manag 114:75–88CrossRefGoogle Scholar
  19. Roy SS, Viswanatham VM, Krishna PV (2016) Spam detection using hybrid model of rough set and decorate ensemble. Int J Comput Syst Eng 2(3):139–147CrossRefGoogle Scholar
  20. Saghatforoush A, Monjezi M, Faradonbeh RS et al (2016) Combination of neural network and ant colony optimization algorithms for prediction and optimization of flyrock and back-break induced by blasting. Eng Comput 32(2):255–266CrossRefGoogle Scholar
  21. Wang Q, Kim M, Shi Y et al (2015) Predict brain MR image registration via sparse learning of appearance and transformation. Med Image Anal 20(1):61–75CrossRefGoogle Scholar
  22. Waziri BS, Bala K, Bustani SA (2017) Artificial neural networks in construction engineering and management. Int J Arch Eng Constr 6(1):50–60Google Scholar
  23. Ye H, Ren Q, Hu X, Lin T, Shi L, Zhang G, Li X (2018) Modeling energy-related CO 2 emissions from office buildings using general regression neural network. Resour Conserv Recycl 129:168–174CrossRefGoogle Scholar
  24. Yi W, Chan APC, Wang X et al (2016) Development of an early-warning system for site work in hot and humid environments: a case study. Autom Constr 62:101–113CrossRefGoogle Scholar
  25. Yu W, Li B, Jia H et al (2015) Application of multi-objective genetic algorithm to optimize energy efficiency and thermal comfort in building design. Energy Build 88:135–143CrossRefGoogle Scholar
  26. Zhang L, Wu X, Zhu H, AbouRizk SM (2017) Perceiving safety risk of buildings adjacent to tunneling excavation: an information fusion approach. Autom Constr 73:88–101CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Knowledge ScienceJapan Advanced Institute of Science and TechnologyNomi CityJapan
  2. 2.Architecture DepartmentHuzhou UniversityHuzhouChina

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