Intelligent planning of safe and economical construction sites: Theory and practice of hybrid multi objective decision making

Junwu WANG , Zhihao HUANG , Yinghui SONG

Front. Eng ›› 2025, Vol. 12 ›› Issue (3) : 487 -509.

PDF (4562KB)
Front. Eng ›› 2025, Vol. 12 ›› Issue (3) : 487 -509. DOI: 10.1007/s42524-024-4004-z
Construction Engineering and Intelligent Construction
RESEARCH ARTICLE

Intelligent planning of safe and economical construction sites: Theory and practice of hybrid multi objective decision making

Author information +
History +
PDF (4562KB)

Abstract

Construction site layout planning (CSLP) involves strategically placing various facilities to optimize a project. However, real construction sites are complex, making it challenging to consider all construction activities and facilities comprehensively. Addressing multi-objective layout optimization is crucial for CSLP. Previous optimization results often lacked precision, imposed stringent boundary constraints, and had limited applications in prefabricated construction. Traditional heuristic algorithms still require improvements in region search strategies and computational efficiency when tackling multi-objective optimization problems. This paper optimizes the prefabricated component construction site layout planning (PCCSLP) by treating construction efficiency and safety risk as objectives within a multi-objective CSLP model. A novel heuristic algorithm, the Hybrid Multi-Strategy Improvement Dung Beetle Optimizer (HMSIDBO), was applied to solve the model due to its balanced capabilities in global exploration and local development. The practicality and effectiveness of this approach were validated through a case study in prefabricated residential construction. The research findings indicate that the HMSIDBO-PCCSLP optimization scheme improved each objective by 18% to 75% compared to the original layout. Compared to Genetic Algorithm (GA), the HMSIDBO demonstrates significantly faster computational speed and higher resolution accuracy. Additionally, in comparison with the Dung Beetle Optimizer (DBO), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm (WOA), HMSIDBO exhibits superior iterative speed and an enhanced ability for global exploration. This paper completes the framework from data collection to multi-objective optimization in-site layout, laying the foundation for implementing intelligent construction site layout practices.

Graphical abstract

Keywords

prefabricated construction / prefabricated component construction site layout planning (PCCSLP) / construction efficiency / safety risk / hybrid multi-strategy improvement dung beetle optimizer (HMSIDBO)

Cite this article

Download citation ▾
Junwu WANG, Zhihao HUANG, Yinghui SONG. Intelligent planning of safe and economical construction sites: Theory and practice of hybrid multi objective decision making. Front. Eng, 2025, 12(3): 487-509 DOI:10.1007/s42524-024-4004-z

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Aydemir E, Yılmaz G, Oruc K O, (2020). A grey production planning model on a ready-mixed concrete plant. Engineering Optimization, 52( 5): 817–831

[2]

Cao X, Li X, Zhu Y, Zhang Z, (2015). A comparative study of environmental performance between prefabricated and traditional residential buildings in China. Journal of Cleaner Production, 109: 131–143

[3]

Choi C W, Harris F C, (1991). A model for determining optimum crane position. Proceedings—Institution of Civil Engineers, 90( 3): 627–634

[4]

Cui Y, Shi RH, Dong J, (2022). CLTSA: A novel tunicate swarm algorithm based on chaotic-levy flight strategy for solving optimization problems. Mathematics, 10( 18): 1–1

[5]

De Santis M, Grani G, Palagi L, (2020). Branching with hyperplanes in the criterion space: The frontier partitioner algorithm for biobjective integer programming. European Journal of Operational Research, 283( 1): 57–69

[6]

El-Rayes K, Khalafallah A, (2005). Trade-off between safety and cost in planning construction site layouts. Journal of Construction Engineering and Management, 131( 11): 1186–1195

[7]

Fard M M, Terouhid S A, Kibert C J, Hakim H, (2017). Safety concerns related to modular/prefabricated building construction. International Journal of Injury Control and Safety Promotion, 24( 1): 10–23

[8]

HebibaA (2020). Wind-wise automated decision support tool for tower crane type selection and location. Dissertation for the Master Degree. Concordia University (in Canadian)

[9]

Hong W K, Lee G, Lee S, Kim S, (2014). Algorithms for in-situ production layout of composite precast concrete members. Automation in Construction, 41: 50–59

[10]

Hu S, Fang Y, Moehler R, (2023). Estimating and visualizing the exposure to tower crane operation hazards on construction sites. Safety Science, 160: 106044

[11]

Huang C, Li W, Lu W, Xue F, Liu M, Liu Z, (2021). Optimization of multiple-crane service schedules in overlapping areas through consideration of transportation efficiency and operational safety. Automation in Construction, 127: 103716

[12]

Huang C, Wong C K, (2015). Optimisation of site layout planning for multiple construction stages with safety considerations and requirements. Automation in Construction, 53: 58–68

[13]

Hwang S, (2012). Ultra-wide band technology experiments for real-time prevention of tower crane collisions. Automation in Construction, 22: 545–553

[14]

Ji Y, Leite F, (2020). Optimized planning approach for multiple tower cranes and material supply points using mixed-integer programming. Journal of Construction Engineering and Management, 146( 3): 04020007

[15]

Jiang H, Jiang X, (2023). Fatigue life prediction for tower cranes under moving load. Journal of Mechanical Science and Technology, 37( 12): 6461–6466

[16]

Kaveh A, Khanzadi M, Moghaddam M R, Rezazadeh M, (2018). Charged system search and magnetic charged system search algorithms for construction site layout planning optimization. periodica polytechnica. Civil Engineering, 62( 4): 841–850

[17]

Li R Y, Chi H L, Peng Z Y, Li X, Chan A P C, (2023). Automatic tower crane layout planning system for high-rise building construction using generative adversarial network. Advanced Engineering Informatics, 58: 102202

[18]

LinJFuY LiRLaiW (2020), An algorithm for optimizing the location and type selection of attached tower cranes based on value engineering. In: 2020 International Conference on Construction and Real Estate Management: Intelligent Construction and Sustainable Buildings, 106–117

[19]

LiuYCuiJ (2020). Identification of hazard sources in prefabricated building construction by entropy weight method. In: 2020 4th International Conference on Water Conservancy, Hydropower and Building Engineering, 560(1)

[20]

Lu Y, Zhu Y, (2021). Integrating hoisting efficiency into construction site layout plan model for prefabricated construction. Journal of Construction Engineering and Management, 147( 10): 04021130

[21]

Monahan J, Powell J C, (2011). An embodied carbon and energy analysis of modern methods of construction in housing: A case study using a lifecycle assessment framework. Energy and Building, 43( 1): 179–188

[22]

NingXLiu W H (2011). Max-min Ant system approach for solving construction site layout. In: International Conference on Mechatronics and Materials Processing. Advanced Materials Research, 328–330

[23]

Ning X, Qi J, Wu C, (2018a). A quantitative safety risk assessment model for construction site layout planning. Safety Science, 104: 246–259

[24]

Ning X, Qi J, Wu C, Wang W, (2018b). A tri-objective ant colony optimization based model for planning safe construction site layout. Automation in Construction, 89: 1–12

[25]

Ning X, Qi J, Wu C, Wang W, (2019c). Reducing noise pollution by planning construction site layout via a multi-objective optimization model. Journal of Cleaner Production, 222: 218–230

[26]

Riga K, Jahr K, Thielen C, Borrmann A, (2020). Mixed integer programming for dynamic tower crane and storage area optimization on construction sites. Automation in Construction, 120: 103259

[27]

Said H, El-Rayes K, (2013). Performance of global optimization models for dynamic site layout planning of construction projects. Automation in Construction, 36: 71–78

[28]

Saito A, Yamaguchi A, (2016). Pseudorandom number generation using chaotic true orbits of the Bernoulli map. Chaos, 26( 6): 063122

[29]

Sanad H M, Ammar M A, Ibrahim M E, (2008). Optimal construction site layout considering safety and environmental aspects. Journal of Construction Engineering and Management, 134( 7): 536–544

[30]

Tam V W Y, Tam C M, Zeng S X, Ng W C Y, (2007). Towards adoption of prefabrication in construction. Building and Environment, 42( 10): 3642–3654

[31]

Tatari A, (2023). Simulating Cost risks for prefabricated construction in developing countries using bayesian networks. Journal of Construction Engineering and Management, 149( 6): 04023037

[32]

TheState Council (2014). China’s National New Urbanization Plan 2014–2020, The State Council, Beijing. Available at: https://www.gov.cn/zhengce/2014-03/16/content_2640075.htm, 2023-12-21

[33]

Tommelein I D, Levitt R E, Hayes-Roth B, (1992). Site layout modeling: how can artificial intelligence help. Journal of Construction Engineering and Management, 118( 3): 594–611

[34]

Wang J, Zhang X, Shou W, Wang X, Xu B, Kim M J, Wu P, (2015). A BIM-based approach for automated tower crane layout planning. Automation in Construction, 59: 168–178

[35]

Wang Z, Hu H, Gong J, Ma X, Xiong W, (2019). Precast supply chain management in off-site construction: A critical literature review. Journal of Cleaner Production, 232: 1204–1217

[36]

Xu J, Li Z, (2012). Multi-objective dynamic construction site layout planning in fuzzy random environment. Automation in Construction, 27: 155–169

[37]

Xue J, Shen B, (2023). Dung beetle optimizer: A new meta-heuristic algorithm for global optimization. Journal of Supercomputing, 79( 7): 7305–7336

[38]

Yahya M, Saka M P, (2014). Construction site layout planning using multi-objective artificial bee colony algorithm with Levy flights. Automation in Construction, 38: 14–29

[39]

Yang B, Fang T, Luo X, Liu B, Dong M, (2022). A BIM-based approach to automated prefabricated building construction site layout planning. KSCE Journal of Civil Engineering, 26( 4): 1535–1552

[40]

Yang B, Liu B, Xiao J, Zhang B, Wang Z, Dong M, (2021a). A novel construction scheduling framework for a mixed construction process of precast components and cast-in-place parts in prefabricated buildings. Journal of Building Engineering, 43: 103181

[41]

Yang X, Liu J, Liu Y, Xu P, Yu L, Zhu L, Chen H, Deng W, (2021b). A novel adaptive sparrow search algorithm based on chaotic mapping and T-Distribution mutation. Applied Sciences, 11( 23): 11192

[42]

Yao G, Li R, Yang Y, (2023). An improved multi-objective optimization and decision-making method on construction sites layout of prefabricated buildings. Sustainability, 15( 7): 6279

[43]

Yi W, Chi H L, Wang S, (2018). Mathematical programming models for construction site layout problems. Automation in Construction, 85: 241–248

[44]

Zavari M, Shahhosseini V, Ardeshir A, Sebt M H, (2022). Multi-objective optimization of dynamic construction site layout using BIM and GIS. Journal of Building Engineering, 52: 104518

[45]

Zhang C, Hammad A, (2012). Improving lifting motion planning and re-planning of cranes with consideration for safety and efficiency. Advanced Engineering Informatics, 26( 2): 396–410

[46]

Zhang H, Yu L, (2021). Site layout planning for prefabricated components subject to dynamic and interactive constraints. Automation in Construction, 126: 103693

[47]

Zhang R, Zhu Y, (2023). Predicting the mechanical properties of heat-treated woods using optimization-algorithm-based BPNN. Forests, 14( 5–34): 935

[48]

Zhang W, Zhang H, Yu L, (2023). Collaborative planning for stacking and installation of prefabricated building components regarding crane-collision avoidance. Journal of Construction Engineering and Management, 149( 6): 04023029

RIGHTS & PERMISSIONS

Higher Education Press

AI Summary AI Mindmap
PDF (4562KB)

1632

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/