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Abediniangerabi, B, Shahandashti, S M, Ahmadi, N and Ashuri, B (2017) Empirical Investigation of Temporal Association between Architecture Billings Index and Construction Spending Using Time-Series Methods. Journal of Construction Engineering and Management, 143(10).
Barbachyn, S M, Devine, R D, Thrall, A P and Kurama, Y C (2017) Economic Evaluation of High-Strength Materials in Stocky Reinforced Concrete Shear Walls. Journal of Construction Engineering and Management, 143(10).
D’Onofrio, R M (2017) CPM Scheduling: A 60-Year History. Journal of Construction Engineering and Management, 143(10).
Gurmu, A T and Aibinu, A A (2017) Construction Equipment Management Practices for Improving Labor Productivity in Multistory Building Construction Projects. Journal of Construction Engineering and Management, 143(10).
Hanna, A S and Iskandar, K A (2017) Quantifying and Modeling the Cumulative Impact of Change Orders. Journal of Construction Engineering and Management, 143(10).
Hasanzadeh, S, Esmaeili, B and Dodd, M D (2017) Impact of Construction Workers’ Hazard Identification Skills on Their Visual Attention. Journal of Construction Engineering and Management, 143(10).
Hyari, K H, Shatarat, N and Khalafallah, A (2017) Handling Risks of Quantity Variations in Unit-Price Contracts. Journal of Construction Engineering and Management, 143(10).
Kamardeen, I and Sunindijo, R Y (2017) Personal Characteristics Moderate Work Stress in Construction Professionals. Journal of Construction Engineering and Management, 143(10).
Mani, N, Kisi, K P, Rojas, E M and Foster, E T (2017) Estimating Construction Labor Productivity Frontier: Pilot Study. Journal of Construction Engineering and Management, 143(10).
Park, J, Cai, H, Dunston, P S and Ghasemkhani, H (2017) Database-Supported and Web-Based Visualization for Daily 4D BIM. Journal of Construction Engineering and Management, 143(10).
Sunindijo, R Y and Kamardeen, I (2017) Work Stress Is a Threat to Gender Diversity in the Construction Industry. Journal of Construction Engineering and Management, 143(10).
Tuchman, J L (2017) Why Journalism Matters. Journal of Construction Engineering and Management, 143(10).
Umer, W, Li, H, Szeto, G P Y and Wong, A Y L (2017) Low-Cost Ergonomic Intervention for Mitigating Physical and Subjective Discomfort during Manual Rebar Tying. Journal of Construction Engineering and Management, 143(10).
Zhang, Y, Minchin, R E and Agdas, D (2017) Forecasting Completed Cost of Highway Construction Projects Using LASSO Regularized Regression. Journal of Construction Engineering and Management, 143(10).
- Type: Journal Article
- Keywords: Highway construction cost; Least absolute shrinkage and selection operator (LASSO); Completed cost; Ordinary least square; Parametric cost estimation; Cost and schedule;
- ISBN/ISSN: 0733-9364
- URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001378
- Abstract:
Finishing highway projects within budget is critical for state highway agencies (SHAs) because budget overruns can result in severe damage to their reputation and credibility. Cost overruns in highway projects have plagued public agencies globally. Hence, this research aims to develop a parametric cost estimation model for SHAs to forecast the completed project cost prior to project execution to take necessary measures to prevent cost escalation. Ordinary least-square (OLS) regression has been a commonly used parametric estimation method in the literature. However, OLS regression has certain limitations. It, for instance, requires strict statistical assumptions. This paper proposes an alternative approach—least absolute shrinkage and selection operator (LASSO)—that has proved in other fields of research to be significantly better than the OLS method in many respects, including automatic feature selection, the ability to handle highly correlated data, ease of interpretability, and numerical stability of the model predictions. Another contribution to the body of knowledge is that this study simultaneously explores project-related variables with some economic factors that have not been used in previous research, but economic conditions are widely considered to be influential on highway construction costs. The data were separated into two groups: one for training the model and the other for validation purposes. Using the same data set, both LASSO and OLS were used to build models, and then their performance was evaluated based on the mean absolute error, mean absolute percentage error, and root-mean-square error. The results showed that the LASSO regression model outperformed the OLS regression model based on the criteria.