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PurposeAccurate and timely cost prediction is critical to the success of construction projects which is still facing challenges especially at the early stage. In the context of rapid development of machine learning technology and the massive cost data from historical projects, this paper aims to propose a novel cost prediction model based on historical data with improved performance when only limited information about the new project is available.Design/methodology/approachThe proposed approach combines regression analysis (RA) and artificial neural network (ANN) to build a novel hybrid cost prediction model with the former as front-end prediction and the latter as back-end correction. Firstly, the main factors influencing the cost of building projects are identified through literature research and subsequently screened by principal component analysis (PCA). Secondly the optimal RA model is determined through multi-model comparison and used for front-end prediction. Finally, ANN is applied to construct the error correction model. The hybrid RA-ANN model was trained and tested with cost data from 128 completed construction projects in China.FindingsThe results show that the hybrid cost prediction model has the advantages of both RA and ANN whose prediction accuracy is higher than that of RA and ANN only with the information such as total floor area, height and number of floors.Originality/value(1) The most critical influencing factors of the buildings’ cost are found out by means of PCA on the historical data. (2) A novel hybrid RA-ANN model is proposed which proved to have the advantages of both RA and ANN with higher accuracy. (3) The comparison among different models has been carried out which is helpful to future model selection.

PurposeBuilding information modeling (BIM) is recognized as one of the technologies to upgrade the informatization level of the architecture engineering and construction (AEC) industry. However, the level of BIM implementation in the construction phase lags behind other phases of the project. Assessing the level of BIM implementation in the construction phase from a system dynamics (SD) perspective can comprehensively understand the interrelationship of factors in the BIM implementation system, thereby developing effective strategies to enhance BIM implementation during the construction phase. This study aims to develop a model to investigate the level of BIM implementation in the construction phase.Design/methodology/approachAn SD model which covered technical subsystem, organizational subsystem, economic subsystem and environmental subsystem was developed based on questionnaire survey data and literature review. Data from China were used for model validation and simulation.FindingsThe simulation results highlight that, in China, from 2021 to 2035, the ratio of BIM implementation in the construction phase will rise from 48.8% to 83.8%, BIM model quality will be improved from 27.6% to 77.2%. The values for variables “BIM platform”, “organizational structure of BIM” and “workflow of BIM” at 2035 will reach 65.6%, 72.9% and 72.8%, respectively. And the total benefits will reach 336.5 billion yuan in 2035. Furthermore, the findings reveal five factors to effectively promote the level of BIM implementation in the construction phase, including: policy support, number of BIM standards, owners demand for BIM, investment in BIM and strategic support for BIM.Originality/valueThis study provides beneficial insights to effectively enhance the implementation level of BIM in the construction phase. Meanwhile, the model developed in this study can be used to dynamically and quantitatively assess the changes in the level of BIM implementation caused by a measure.

Construction waste (CW) source reduction is a crucial strategy to address the sustainability issue of the construction industry. The economic benefit is a key factor affecting project decision-makers on whether to implement this strategy. However, limited studies analysed the cost–benefit of CW source reduction from a system dynamic perspective. Therefore, by considering the design and construction phase as a whole, this study constructed a system dynamics (SD) model based on the identification of the factors affecting the cost–benefit of CW source reduction to analyse the cost–benefit of CW source reduction. A residential building project in China’s Chengdu was used for the study case. The results show that the net benefit of CW source reduction is positive, and BIM implementation, design for standard material size, and building material storage are the three strategies to effectively improve the economic benefits of CW source reduction. Furthermore, the best investment level of CW source reduction was also determined. This study provides a model which can be used to simulate the cost–benefit under different implementation levels of reduction strategies and different investment levels in advance, thereby providing a reference for project decision-makers to plan CW source reduction.