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Ahmed, K, Leung, M and Ojo, L D (2022) An Exploratory Study to Identify Key Stressors of Ethnic Minority Workers in the Construction Industry. Journal of Construction Engineering and Management, 148(05).

Atasoy, G, Ertaymaz, U, Dikmen, I and Talat Birgonul, M (2022) Empowering Risk Communication: Use of Visualizations to Describe Project Risks. Journal of Construction Engineering and Management, 148(05).

Babaeian Jelodar, M, Yiu, T W and Wilkinson, S (2022) Empirical Modeling for Conflict Causes and Contractual Relationships in Construction Projects. Journal of Construction Engineering and Management, 148(05).

Hosseinian, S M, Younesi, S, Razini, S and Carmichael, D G (2022) Intelligent Stochastic Agent-Based Model for Predicting Truck Production in Construction Sites by Considering Learning Effect. Journal of Construction Engineering and Management, 148(05).

  • Type: Journal Article
  • Keywords: Truck production; Agent-based stochastic modeling; Learning; Construction sites;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0002264
  • Abstract:
    Predicting truck production in construction projects is one of the basic tasks within project planning and control. This paper presents an original and novel intelligent stochastic agent-based model to maximize truck production at construction sites by considering the impact of learning. The proposed model was developed to overcome limitations of existing models, including a lack of the inclusion of a training mechanism and a reward/penalty framework for truck performance. Ideas of reinforcement learning theory were used. A reward/penalty function was designed based on minimum travel time. Traffic and fuel volume were treated as stochastic variables. A worked example and a real case study are presented to show the applicability and efficiency of the proposed model. The paper shows that the results of the proposed model accurately predict truck production. The paper also shows that the proposed model demonstrates a shorter truck travel time and, thus, higher production compared to the Monte Carlo simulation logic. The method proposed here offers an original contribution to the analysis of truck production and will be of use to practitioners engaged in project planning and control, especially in large earth-moving operations.

Kaminsky, J A (2022) Improving Public–Private Partnerships for Renewable Electricity Infrastructure in Lower- and Middle-Income Countries. Journal of Construction Engineering and Management, 148(05).

Keskin, B, Salman, B and Koseoglu, O (2022) Architecting a BIM-Based Digital Twin Platform for Airport Asset Management: A Model-Based System Engineering with SysML Approach. Journal of Construction Engineering and Management, 148(05).

Mrazovic, N and Fischer, M (2022) Assessment Framework for Additive Manufacturing in the AEC Industry. Journal of Construction Engineering and Management, 148(05).

Nasirian, A, Abbasi, B, Cheng, T C E and Arashpour, M (2022) Multiskilled Workforce Planning: A Case from the Construction Industry. Journal of Construction Engineering and Management, 148(05).

Nickdoost, N, Choi, J, AbdelRazig, Y and Sobanjo, J (2022) A Project Life-Cycle Approach to Managing Procrastination in Construction Projects: State-of-the-Art Review. Journal of Construction Engineering and Management, 148(05).

Salhab, D, Møller, D E, Lindhard, S M, Hamzeh, F, Randrup, M and Pilgaard, A (2022) Accounting for Variability: Identifying Critical Activities as a Supplement to the Critical Path. Journal of Construction Engineering and Management, 148(05).

Yuan, Z, Fang, Y, Hong, J, Zhang, Q, Zhang, Z and Ni, G (2022) Coupling Relationship between Capabilities and Benefits of Lean Construction for Precast Buildings from a Multivariable Moderation Perspective. Journal of Construction Engineering and Management, 148(05).

Zarghami, S A (2022) Forecasting Project Duration in the Face of Disruptive Events: A Resource-Based Approach. Journal of Construction Engineering and Management, 148(05).