ARCOM

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Aljassmi, H, Han, S and Davis, S (2014) Project Pathogens Network: New Approach to Analyzing Construction-Defects-Generation Mechanisms. Journal of Construction Engineering and Management, 140(01).

Casanovas, M d M, Armengou, J and Ramos, G (2014) Occupational Risk Index for Assessment of Risk in Construction Work by Activity. Journal of Construction Engineering and Management, 140(01).

Chang, C (2014) Principal-Agent Model of Risk Allocation in Construction Contracts and Its Critique. Journal of Construction Engineering and Management, 140(01).

Choi, S, Kim, D Y, Han, S H and Kwak, Y H (2014) Conceptual Cost-Prediction Model for Public Road Planning via Rough Set Theory and Case-Based Reasoning. Journal of Construction Engineering and Management, 140(01).

Type: Journal Article
Keywords: Costs; Predictions; Highways and roads; Policies; Conceptual cost prediction; Policy making; Rough set theory; Case-based reasoning; Road construction cost; Project planning and design;
ISBN/ISSN: 0733-9364
URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0000743
Abstract:
Long-term transportation policies require government officials to predict the cost of public road construction during the conceptual planning phase. However, early cost prediction is often inaccurate because public officials are not familiar with cost engineering practices, and moreover, have limited time and insufficient information for estimating the possible range of the cost distribution. This study develops a conceptual cost prediction model by combining rough set theory, case-based reasoning, and genetic algorithms to better predict costs in the conceptual planning phase. Rough set theory and qualitative in-depth interviews are integrated to select the proper input attributes for the cost prediction model. Case-based reasoning is then applied to predict road construction costs by considering users’ difficulties in the conceptual policy planning phase. A genetic algorithm is also used to assist the rough set model and case-based reasoning model to obtain optimal solutions. The result of the analysis shows that the proposed conceptual cost prediction model is reliable and robust compared to the existing cost prediction model.

González, P, González, V, Molenaar, K and Orozco, F (2014) Analysis of Causes of Delay and Time Performance in Construction Projects. Journal of Construction Engineering and Management, 140(01).

Laryea, S and Lubbock, A (2014) Tender Pricing Environment of Subcontractors in the United Kingdom. Journal of Construction Engineering and Management, 140(01).

Ling, F Y Y, Ke, Y, Kumaraswamy, M M and Wang, S (2014) Key Relational Contracting Practices Affecting Performance of Public Construction Projects in China. Journal of Construction Engineering and Management, 140(01).

Narbaev, T and De Marco, A (2014) Combination of Growth Model and Earned Schedule to Forecast Project Cost at Completion. Journal of Construction Engineering and Management, 140(01).

Ning, Y and Ling, F Y Y (2014) Boosting Public Construction Project Outcomes through Relational Transactions. Journal of Construction Engineering and Management, 140(01).

Rosenfeld, Y (2014) Root-Cause Analysis of Construction-Cost Overruns. Journal of Construction Engineering and Management, 140(01).

Syal, M, Duah, D, Samuel, S, Mazor, M, Mo, Y and Cyr, T (2014) Information Framework for Intelligent Decision Support System for Home Energy Retrofits. Journal of Construction Engineering and Management, 140(01).

Zhang, S, Du, C, Sa, W, Wang, C and Wang, G (2014) Bayesian-Based Hybrid Simulation Approach to Project Completion Forecasting for Underground Construction. Journal of Construction Engineering and Management, 140(01).