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Asgari, S, Afshar, A and Madani, K (2014) Cooperative Game Theoretic Framework for Joint Resource Management in Construction. Journal of Construction Engineering and Management, 140(03).

Baccarini, D and Love, P E D (2014) Statistical Characteristics of Cost Contingency in Water Infrastructure Projects. Journal of Construction Engineering and Management, 140(03).

Blomberg, D, Cotellesso, P, Sitzabee, W and Thal, A E (2014) Discovery of Internal and External Factors Causing Military Construction Cost Premiums. Journal of Construction Engineering and Management, 140(03).

Bowen, P, Edwards, P, Lingard, H and Cattell, K (2014) Predictive Modeling of Workplace Stress among Construction Professionals. Journal of Construction Engineering and Management, 140(03).

Bowen, P, Edwards, P, Lingard, H and Cattell, K (2014) Workplace Stress, Stress Effects, and Coping Mechanisms in the Construction Industry. Journal of Construction Engineering and Management, 140(03).

Chai, C, de Brito, J, Gaspar, P L and Silva, A (2014) Predicting the Service Life of Exterior Wall Painting: Techno-Economic Analysis of Alternative Maintenance Strategies. Journal of Construction Engineering and Management, 140(03).

Chiang, Y H, Zhou, L, Li, J, Lam, P T I and Wong, K W (2014) Achieving Sustainable Building Maintenance through Optimizing Life-Cycle Carbon, Cost, and Labor: Case in Hong Kong. Journal of Construction Engineering and Management, 140(03).

Cirilovic, J, Vajdic, N, Mladenovic, G and Queiroz, C (2014) Developing Cost Estimation Models for Road Rehabilitation and Reconstruction: Case Study of Projects in Europe and Central Asia. Journal of Construction Engineering and Management, 140(03).

Fan, S (2014) Intellectual Property Rights in Building Information Modeling Application in Taiwan. Journal of Construction Engineering and Management, 140(03).

Gao, T, Ergan, S, Akinci, B and Garrett, J H (2014) Proactive Productivity Management at Job Sites: Understanding Characteristics of Assumptions Made for Construction Processes during Planning Based on Case Studies and Interviews. Journal of Construction Engineering and Management, 140(03).

Jafarzadeh, R, Wilkinson, S, González, V, Ingham, J M and Amiri, G G (2014) Predicting Seismic Retrofit Construction Cost for Buildings with Framed Structures Using Multilinear Regression Analysis. Journal of Construction Engineering and Management, 140(03).

  • Type: Journal Article
  • Keywords: Construction costs; Seismic effects; Rehabilitation; Frames; Regression analysis; Regression models; Construction-cost estimation; Seismic retrofit projects; Cost modeling; Regression analysis; Regression models; Cost and Schedule;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0000750
  • Abstract:
    Attempts to predict construction cost represent a problem of continual concern and interest to both practitioners and researchers. Such an attempt is presented here for the specific challenge of cost prediction when undertaking seismic retrofitting of existing structures. Using multilinear regression analysis, 14 independent variables were analyzed to develop parametric models for predicting the retrofit net construction cost (RNCC). Half of these variables have never previously been studied in the literature. The required data for this study were collected from 158 earthquake-prone public schools in Iran, each having a framed structure. The backward elimination (BE) regression technique was used to identify any variables that made a statistically significant contribution to the RNCC. The suitability of the BE technique for this identification was examined and demonstrated using a number of model-selection criteria. Rather surprisingly, building age and compliance with the earliest practiced seismic design code were found to be insignificant predictors of the RNCC. As reflected by the BE technique, the significant predictors were building total plan area, number of stories, structural type, seismicity, soil type, weight, and plan irregularity. The causal analysis performed between the RNCC and these variables showed that the first two variables have the greatest influence on the determination of the RNCC. The primary contribution to the construction industry is the introduction of a simple double-log cost-area model for predicting seismic retrofit construction cost. The introduced model enables engineering consultants, managers, and policy makers to simply predict this cost at the early planning and budgeting stage of seismic retrofit projects.

Karan, E P, Sivakumar, R, Irizarry, J and Guhathakurta, S (2014) Digital Modeling of Construction Site Terrain Using Remotely Sensed Data and Geographic Information Systems Analyses. Journal of Construction Engineering and Management, 140(03).

Leung, M, Yu, J and Chan, Y S (2014) Focus Group Study to Explore Critical Factors of Public Engagement Process for Mega Development Projects. Journal of Construction Engineering and Management, 140(03).

Malone, E K and Issa, R R (2014) Predictive Models for Work-Life Balance and Organizational Commitment of Women in the U.S. Construction Industry. Journal of Construction Engineering and Management, 140(03).

Oviedo-Haito, R J, Jiménez, J, Cardoso, F F and Pellicer, E (2014) Survival Factors for Subcontractors in Economic Downturns. Journal of Construction Engineering and Management, 140(03).