Abstracts – Browse Results
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Adaku, E, Osei-Poku, V, Ottou, J A and Yirenkyi-Fianko, A (2024) Contractor payment delays: a systematic review of current trends and future directions. Construction Innovation, 24(05), 1205–27.
Almasabha, G, Shehadeh, A, Alshboul, O and Al Hattamleh, O (2024) Structural performance of buried reinforced concrete pipelines under deep embankment soil. Construction Innovation, 24(05), 1280-96.
Hilu, K A and Hiyassat, M A (2024) Qualitative assessment of resilience in construction projects. Construction Innovation, 24(05), 1297-319.
Jayamaha, B H V H, Perera, B A K S, Gimhani, K D M and Rodrigo, M N N (2024) Adaptability of enterprise resource planning (ERP) systems for cost management of building construction projects in Sri Lanka. Construction Innovation, 24(05), 1255-79.
Kedir, F, Hall, D M, Brantvall, S, Lessing, J, Hollberg, A and Soman, R K (2024) Circular information flows in industrialized housing construction: the case of a multi-family housing product platform in Sweden. Construction Innovation, 24(05), 1354-79.
Kineber, A F, Othman, I, Oke, A E, Chileshe, N and Zayed, T (2024) Modeling the relationship between value management implementation phases, critical success factors and overall project success. Construction Innovation, 24(05), 1380-400.
Nguyen Ngoc, H, Mohammed Abdelkader, E, Al-Sakkaf, A, Alfalah, G and Zayed, T (2024) A hybrid AHP-maut model for assessing competitiveness of construction companies: A case study of construction companies in Vietnam and Canada. Construction Innovation, 24(05), 1320-53.
Sammour, F, Alkailani, H, Sweis, G J, Sweis, R J, Maaitah, W and Alashkar, A (2024) Forecasting demand in the residential construction industry using machine learning algorithms in Jordan. Construction Innovation, 24(05), 1228-54.
- Type: Journal Article
- Keywords: construction management; demand forecast; economic indicators; machine learning; residential housing
- ISBN/ISSN: 14714175
- URL: https://doi.org/10.1108/CI-10-2022-0279
- Abstract:
Purpose: Demand forecasts are a key component of planning efforts and are crucial for managing core operations. This study aims to evaluate the use of several machine learning (ML) algorithms to forecast demand for residential construction in Jordan. Design/methodology/approach: The identification and selection of variables and ML algorithms that are related to the demand for residential construction are indicated using a literature review. Feature selection was done by using a stepwise backward elimination. The developed algorithm’s accuracy has been demonstrated by comparing the ML predictions with real residual values and compared based on the coefficient of determination. Findings: Nine economic indicators were selected to develop the demand models. Elastic-Net showed the highest accuracy of (0.838) versus artificial neural networkwith an accuracy of (0.727), followed by Eureqa with an accuracy of (0.715) and the Extra Trees with an accuracy of (0.703). According to the results of the best-performing model forecast, Jordan’s 2023 first-quarter demand for residential construction is anticipated to rise by 11.5% from the same quarter of the year 2022. Originality/value: The results of this study extend to the existing body of knowledge through the identification of the most influential variables in the Jordanian residential construction industry. In addition, the models developed will enable users in the fields of construction engineering to make reliable demand forecasts while also assisting in effective financial decision-making.
Shang, G, Pheng, L S and Zhong Xia, R L (2024) Adoption of smart contracts in the construction industry: an institutional analysis of drivers and barriers. Construction Innovation, 24(05), 1401-21.
Zabidin, N S, Belayutham, S and Che Ibrahim, C K I (2024) The knowledge, attitude and practices (kap) of industry 4.0 between construction practitioners and academicians in Malaysia: A comparative study. Construction Innovation, 24(05), 1185-204.