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Abdeen, F N, Gunatilaka, R N, Sepasgozar, S M E and Edwards, D J (2024) The usability of a novel mobile augmented reality application for excavation process considering safety and productivity in construction. Construction Innovation, 24(04), 892-911.
AbdulLateef, O, Sanmargaraja, S, Oni, O, Anavhe, P and Mewomo, C M (2024) An association rule mining model for the application of construction technologies during COVID-19. International Journal of Construction Management, 24(04), 443–53.
Ahmadian, F F A, Rashidi, T H, Akbarnezhad, A and Waller, S T (2017) BIM-enabled sustainability assessment of material supply decisions. Engineering, Construction and Architectural Management, 24(04), 668-95.
Al-Hammad, A M, Al-Mubaiyadh, S and Mahmoud, T (1996) Public versus private sector's assessment of problems facing the building maintenance industry in Saudi Arabia. Building Research & Information, 24(04), 245–54.
Alzraiee, H (2024) Quantifying labour impact cost due to change orders. International Journal of Construction Management, 24(04), 467–75.
Arogundade, S, Dulaimi, M and Ajayi, S (2024) Exploring the challenges impeding construction process carbon reduction in the UK. International Journal of Construction Management, 24(04), 422–31.
Bajpai, A and Misra, S C (2024) Evaluation of success factors to implement digitalization in the construction industry. Construction Innovation, 24(04), 865-91.
Best, R and Langston, C (2006) Evaluation of construction contractor performance: a critical analysis of some recent research. Construction Management and Economics, 24(04), 439-45.
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Cheung, F K T and Skitmore, M (2006) A modified storey enclosure model. Construction Management and Economics, 24(04), 391-405.
Daniel, E I, Oshodi, O, Dabara, D and Dimka, N (2024) Towards closing the housing gap in the UK: exploration of the influencing factors and the way forward. Construction Innovation, 24(04), 965-85.
Das, D K (2024) Conceptualising environmental justice in the construction of road transport infrastructure in the global South: An Indian context. International Journal of Construction Management, 24(04), 400–10.
Do, S T, Nguyen, V T and Banlasan, D (2024) Social media sensing framework for urban infrastructure management: a Philippine case study. Construction Innovation, 24(04), 1117-36.
Ejohwomu, O A, Oshodi, O S and Lam, K C (2017) Nigeria’s construction industry: Barriers to effective communication. Engineering, Construction and Architectural Management, 24(04), 652-67.
Ekanayake, B, Ahmadian Fard Fini, A, Wong, J K W and Smith, P (2024) A deep learning-based approach to facilitate the as-built state recognition of indoor construction works. Construction Innovation, 24(04), 933-49.
Enshassi, A (1996) Training for Palestinian engineers to face the challenges of multinational enterprises in the Gaza Strip. Building Research & Information, 24(04), 222–7.
GhaffarianHoseini, A, Tien, D D, Naismith, N, Tookey, J and GhaffarianHoseini, A (2017) Amplifying the practicality of contemporary building information modelling implementations for New Zealand green building certification (green star). Engineering, Construction and Architectural Management, 24(04), 696-714.
Golabchi, H and Hammad, A (2024) Estimating labor resource requirements in construction projects using machine learning. Construction Innovation, 24(04), 1048-65.
- Type: Journal Article
- Keywords: construction management; construction scheduling; labor estimation; labor resource management; recurrent neural network; time series forecasting
- ISBN/ISSN:
- URL: https://doi.org/10.1108/CI-11-2021-0211
- Abstract:
Purpose: Existing labor estimation models typically consider only certain construction project types or specific influencing factors. These models are focused on quantifying the total labor hours required, while the utilization rate of the labor during the project is not usually accounted for. This study aims to develop a novel machine learning model to predict the time series of labor resource utilization rate at the work package level. Design/methodology/approach: More than 250 construction work packages collected over a two-year period are used to identify the main contributing factors affecting labor resource requirements. Also, a novel machine learning algorithm – Recurrent Neural Network (RNN) – is adopted to develop a forecasting model that can predict the utilization of labor resources over time. Findings: This paper presents a robust machine learning approach for predicting labor resources’ utilization rates in construction projects based on the identified contributing factors. The machine learning approach is found to result in a reliable time series forecasting model that uses the RNN algorithm. The proposed model indicates the capability of machine learning algorithms in facilitating the traditional challenges in construction industry. Originality/value: The findings point to the suitability of state-of-the-art machine learning techniques for developing predictive models to forecast the utilization rate of labor resources in construction projects, as well as for supporting project managers by providing forecasting tool for labor estimations at the work package level before detailed activity schedules have been generated. Accordingly, the proposed approach facilitates resource allocation and enables prioritization of available resources to enhance the overall performance of projects. ©, Emerald Publishing Limited.
Guerra, B C, Koo, H J, Caldas, C and Leite, F (2024) Prediction of waste diversion and identification of trends in construction and demolition waste data using data mining. International Journal of Construction Management, 24(04), 374–83.
Gunning, J G and Cooke, E (1996) The influence of occupational stress on construction professionals. Building Research & Information, 24(04), 213–22.
Gupta, S, Jha, K N and Vyas, G (2024) Construction and demolition waste causative factors in building projects: Survey of the Indian construction industry experiences. International Journal of Construction Management, 24(04), 432–42.
Hassanein, A A G (1996) Factors affecting the bidding behaviour of contractors in Egypt. Building Research & Information, 24(04), 228–36.
Hassanein, A A G and Hakam, Z H R (1996) A bidding decision index for construction contractors. Building Research & Information, 24(04), 237–44.
Hu, X and Liu, C (2017) Total factor productivity measurement with carbon reduction. Engineering, Construction and Architectural Management, 24(04), 575-92.
Huang, Y-L and Chou, S-P (2006) Valuation of the minimum revenue guarantee and the option to abandon in BOT infrastructure projects. Construction Management and Economics, 24(04), 379-89.
Jiang, X, Zhou, H, Li, M, Lu, K, Lyu, S, Omrani, S and Skitmore, M (2024) Sustainable construction projects management in the AEC industry: Analysis and visualization. International Journal of Construction Management, 24(04), 384–99.
Johnson, C, Lizarralde, G and Davidson, C H (2006) A systems view of temporary housing projects in post-disaster reconstruction. Construction Management and Economics, 24(04), 367-78.
Kazemi, R M, Riley, D, Asadi, S and Delgoshaei, P (2017) Improving the performance profile of energy conservation measures at the Penn State University Park campus. Engineering, Construction and Architectural Management, 24(04), 610-28.
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Malla, V (2024) Structuration of lean-agile integrated factors for construction projects. Construction Innovation, 24(04), 986-1004.
Nabawy, M and Gouda Mohamed, A (2024) Risks assessment in the construction of infrastructure projects using artificial neural networks. International Journal of Construction Management, 24(04), 361–73.
Oke, A E, Aliu, J, Onajite, S and Simeon, M (2024) Success factors of digital technologies (DT) tools adoption for sustainable construction in a developing economy. Construction Innovation, 24(04), 950-64.
Oke, A E, Aliu, J, Tunji-Olayeni, P and Abayomi, T (2024) Application of gamification for sustainable construction: an evaluation of the challenges. Construction Innovation, 24(04), 1066-84.
Olugboyega, O, Ilesanmi, K E, Oseghale, G E and Aigbavboa, C (2024) The link between construction apps’ acceptance and digital attributes of construction professionals: perspectives from digital competence model. Construction Innovation, 24(04), 912-32.
Owojori, O M, Okoro, C and Chileshe, N (2024) Actualising social sustainability through adaptive reuse innovations within the context of sustainable development. International Journal of Construction Management, 24(04), 411–21.
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Rahmani, F, Maqsood, T and Khalfan, M (2017) An overview of construction procurement methods in Australia. Engineering, Construction and Architectural Management, 24(04), 593-609.
Silveira, B F and Costa, D B (2024) Method for automating the processes of generating and using 4D BIM models integrated with location-based planning and Last Planner® System. Construction Innovation, 24(04), 1005-25.
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Tang, C M, Wong, C W Y, Leung, A Y T and Lam, K C (2006) Selection of funding schemes by a borrowing decision model: a Hong Kong case study. Construction Management and Economics, 24(04), 349-65.
Thomas, A V, Kalidindi, S N and Ganesh, L S (2006) Modelling and assessment of critical risks in BOT road projects. Construction Management and Economics, 24(04), 407-24.
Uzun, C and Cangür, R E (2024) An ontological assessment proposal for architectural outputs of generative adversarial network. Construction Innovation, 24(04), 1165-84.
Wang, K, Guo, F, Zhou, R and Qian, L (2024) Implementation of augmented reality in BIM-enabled construction projects: a bibliometric literature review and a case study from China. Construction Innovation, 24(04), 1085-116.
Wilson, J G and Gupta, N K (1996) Assessment of structure formation in fresh concrete by measurement of its electrical resistance. Building Research & Information, 24(04), 209–12.
Wong, J T Y and Hui, E C M (2006) Construction project risks: further considerations for constructors' pricing in Hong Kong. Construction Management and Economics, 24(04), 425-38.
Wuni, I Y and Mazher, K M (2024) Ending the suitability quantification dilemma: intelligent decision support system for modular integrated construction in a high-density metropolis. Construction Innovation, 24(04), 1026-47.
Zoleykani, M J, Abbasianjahromi, H, Banihashemi, S, Tabadkani, S A and Hajirasouli, A (2024) Extended reality (XR) technologies in the construction safety: systematic review and analysis. Construction Innovation, 24(04), 1137-64.