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Aladağ, H and Işık, Z (2019) Design and construction risks in BOT type mega transportation projects. Engineering, Construction and Architectural Management, 26(10), 2223–42.

Almarri, K, Aljarman, M and Boussabaine, H (2019) Emerging contractual and legal risks from the application of building information modelling. Engineering, Construction and Architectural Management, 26(10), 2307–25.

Cajzek, R and KlanÅ¡ek, U (2019) Cost optimization of project schedules under constrained resources and alternative production processes by mixed-integer nonlinear programming. Engineering, Construction and Architectural Management, 26(10), 2474–508.

Derakhshanfar, H, Ochoa, J J, Kirytopoulos, K, Mayer, W and Tam, V W (2019) Construction delay risk taxonomy, associations and regional contexts. Engineering, Construction and Architectural Management, 26(10), 2364–88.

Fang, Y and Ng, S T (2019) Genetic algorithm for determining the construction logistics of precast components. Engineering, Construction and Architectural Management, 26(10), 2289–306.

Jin, H, Liu, S, Liu, C and Udawatta, N (2019) Optimizing the concession period of PPP projects for fair allocation of financial risk. Engineering, Construction and Architectural Management, 26(10), 2347–63.

Kumar Singla, H (2019) A comparative analysis of long-term performance of construction and non-construction IPOs in India. Engineering, Construction and Architectural Management, 26(10), 2447–73.

Kunieda, Y, Codinhoto, R and Emmitt, S (2019) Increasing the efficiency and efficacy of demolition through computerised 4D simulation. Engineering, Construction and Architectural Management, 26(10), 2186–205.

Kwofie, T E, Aigbavboa, C O and Machethe, S O (2019) Nature of communication performance in non-traditional procurements in South Africa. Engineering, Construction and Architectural Management, 26(10), 2264–88.

Lau, C H, Mesthrige, J W, Lam, P T and Javed, A A (2019) The challenges of adopting new engineering contract: a Hong Kong study. Engineering, Construction and Architectural Management, 26(10), 2389–409.

Loosemore, M, Sunindijo, R Y, Lestari, F, Kusminanti, Y and Widanarko, B (2019) Comparing the safety climate of the Indonesian and Australian construction industries. Engineering, Construction and Architectural Management, 26(10), 2206–22.

Oyewobi, L O, Oke, A E, Adeneye, T D and Jimoh, R A (2019) Influence of organizational commitment on work–life balance and organizational performance of female construction professionals. Engineering, Construction and Architectural Management, 26(10), 2243–63.

Qayoom, A and H.W. Hadikusumo, B (2019) Multilevel safety culture affecting organization safety performance: a system dynamic approach. Engineering, Construction and Architectural Management, 26(10), 2326–46.

Sinesilassie, E G, Tripathi, K K, Tabish, S Z S and Jha, K N (2019) Modeling success factors for public construction projects with the SEM approach: engineer’s perspective. Engineering, Construction and Architectural Management, 26(10), 2410–31.

Whang, S, Park, K S and Kim, S (2019) Critical success factors for implementing integrated construction project delivery. Engineering, Construction and Architectural Management, 26(10), 2432–46.

Xiong, B, Newton, S, Li, V, Skitmore, M and Xia, B (2019) Hybrid approach to reducing estimating overfitting and collinearity. Engineering, Construction and Architectural Management, 26(10), 2170–85.

• Type: Journal Article
• Keywords: Estimating; Novel model; Approach;
• ISBN/ISSN: 0969-9988
• URL: https://doi.org/10.1108/ECAM-08-2018-0353
• Abstract:
  The purpose of this paper is to present an approach to address the overfitting and collinearity problems that frequently occur in predictive cost estimating models for construction practice. A case study, modeling the cost of preliminaries is proposed to test the robustness of this approach. A hybrid approach is developed based on the Akaike information criterion (AIC) and principal component regression (PCR). Cost information for a sample of 204 UK school building projects is collected involving elemental items, contingencies (risk) and the contractors’ preliminaries. An application to estimate the cost of preliminaries for construction projects demonstrates the method and tests its effectiveness in comparison with such competing models as: alternative regression models, three artificial neural network data mining techniques, case-based reasoning and support vector machines. The experimental results show that the AIC–PCR approach provides a good predictive accuracy compared with the alternatives used, and is a promising alternative to avoid overfitting and collinearity. This is the first time an approach integrating the AIC and PCR has been developed to offer an improvement on existing methods for estimating construction project Preliminaries. The hybrid approach not only reduces the risk of overfitting and collinearity, but also results in better predictability compared with the commonly used stepwise regression.