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Ünsal Altuncan, & and Tanyer, A M (2018) Context-Dependent Construction Conflict Management Performance Analysis Based on Competency Theory. Journal of Construction Engineering and Management, 144(12).

Alomari, K A, Gambatese, J A and Tymvios, N (2018) Risk Perception Comparison among Construction Safety Professionals: Delphi Perspective. Journal of Construction Engineering and Management, 144(12).

Chen, H, Hu, H, Tang, M, Yang, X and Zhu, J (2018) Hybrid Bored Prestressed Concrete Cased Piles: Equipment and Construction Procedures. Journal of Construction Engineering and Management, 144(12).

Leandro, R, O’Connor, J T and Khwaja, N (2018) Development and Application of a Production-Rate Resource for Contract Time Determination. Journal of Construction Engineering and Management, 144(12).

Lee, J I, Lee, H and Park, M (2018) Contractor Liquidity Evaluation Model for Successful Public Housing Projects. Journal of Construction Engineering and Management, 144(12).

Mazher, K M, Chan, A P C, Zahoor, H, Khan, M I and Ameyaw, E E (2018) Fuzzy Integral–Based Risk-Assessment Approach for Public–Private Partnership Infrastructure Projects. Journal of Construction Engineering and Management, 144(12).

Moon, S, Yang, B and Choi, E (2018) Safety Guideline for Safe Concrete Placement Utilizing the Information on the Structural Behavior of Formwork. Journal of Construction Engineering and Management, 144(12).

Nguyen, A, Mollik, A and Chih, Y (2018) Managing Critical Risks Affecting the Financial Viability of Public–Private Partnership Projects: Case Study of Toll Road Projects in Vietnam. Journal of Construction Engineering and Management, 144(12).

Rafiei, M H and Adeli, H (2018) Novel Machine-Learning Model for Estimating Construction Costs Considering Economic Variables and Indexes. Journal of Construction Engineering and Management, 144(12).

  • Type: Journal Article
  • Keywords: Construction cost estimation; Residential buildings; Deep learning; Deep Boltzmann machine; Softmax; Back-propagation neural networks; Support vector machine;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001570
  • Abstract:
    In addition to materials, labor, equipment, and method, construction cost depends on many other factors such as the project locality, type, construction duration, scheduling, and the extent of use of recycled materials. Further, the fluctuation of economic variables and indexes (EV&Is), such as liquidity, wholesale price index, and building services index, causes variation in costs. These changes may increase or reduce the construction cost, are hard to predict, and are normally ignored in the traditional cost estimation computation. This paper presents an innovative construction cost estimation model using advanced machine-learning concepts and taking into account the EV&Is. A data structure is proposed that incorporates a set of physical and financial (P&F) variables of the real estate units as well as a set of EV&Is variables affecting the construction costs. The model includes an unsupervised deep Boltzmann machine (DBM) learning approach along with a softmax layer (DBM-SoftMax), and a three-layer back-propagation neural network (BPNN) or another regression model, support vector machine (SVM). The role of DBM-SoftMax is to extract relevant features from the input data. The role of the BPNN or SVM is to turn the trained unsupervised DBM into a supervised regression network. This combination improves the effectiveness and accuracy of both conventional BPNN and SVM. A sensitivity analysis was performed within the algorithm in order to achieve the best results taking into account the impact of the EV&I factors in different times (time lags). The model was verified using the construction cost data for 372 low- and midrise buildings in the range of three to nine stories. Cost estimation errors of the proposed model were much less than those of both the BPNN-only and SVM-only models, thus demonstrating the effectiveness of the strategies employed in this research and the superiority of the proposed model.

Sepasgozar, S M E, Forsythe, P and Shirowzhan, S (2018) Evaluation of Terrestrial and Mobile Scanner Technologies for Part-Built Information Modeling. Journal of Construction Engineering and Management, 144(12).