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CURATOR
A pinboard by
Juyeong Choi

PhD Candidate, Purdue University

PINBOARD SUMMARY

Purdue Index for Construction enables data driven decision making in the construction industry.

Purdue Index for Construction was initiated from the discussions within the industry advisory board of the Division of Construction Engineering Management at Purdue University, emphasizing the need for more informed and data oriented decision making. Construction industry involves a complex decision context that is defined by unique nature of projects, temporary collaboration of a diverse range of stakeholders with different specialties in each project, and long payback periods. These characteristics are coupled with the importance of the industry in terms of its contribution to GDP and employment. Therefore, there is a need for systematic frameworks to facilitate data oriented decision- and policy-making in the construction industry. However, the traditional approach to data analysis in the construction industry has primarily focused on the financial aspects of the industry without considering other important dimensions. To address this gap, this research was shaped at the intersection of the academia and industry to develop a systematic framework for multi-dimensional trend analysis in the construction industry. As the first step, a survey was sent to the industry using a mixture of open questions complimented with structured questions to define health of the construction industry. Based on the outcome of the first phase, the framework of composite index was developed including the five-dimensions of health (i.e., economic, stability, social, development and quality). Each dimension of the proposed framework of Purdue Index for Construction (Pi-C) is composed of relevant sub-indices that were developed based on the first phase and further factor analysis to verify the interrelation of the sub-indices and determine their weights to compute a composite index. Publicly available data is then used to showcase the application of the framework in benchmarking, interpreting and analyzing the data associated with each dimension. Statistical analysis of selected factors was performed to further analyze dynamics of sub-indices such as diversity of the project areas. Finally, the beta version of the Pi-C is reported and shared with the construction industry through the web portal (http://wpvcemweb01.itap.purdue.edu/CEM/Pi-C/) to promote data driven and informed policy and strategy development. Specifically, decision makers can formulate and implement strategies in view of the trajectory of the trajectory of the multi-dimensional trend of the health of the industry.

3 ITEMS PINNED

MCPCM: A DEMATEL-ANP-Based Multi-criteria Decision-Making Approach to Evaluate the Critical Success Factors in Construction Projects

Abstract: Project success is a fundamental issue for any planning, design and construction development. Thus, it effects the stakeholders which involve government, developer, constructor and communities as the end users. Critical success factors (CSFs) have been key aspects of a construction project which are considered to be a means to improve the effectiveness of project. CSF literature has been widely addressed, and many researchers evaluated the gaps in CSFs for construction projects’ success. However, this paper highlights the importance levels of interdependency among the CSFs which has rarely been explored in the prior studies. In this study, most influential factors in successfully completing construction projects are used to develop a new integrated model, multi-criteria construction projects CSF model. A novel hybrid multi-criteria decision-making (MCDM) model is used to address the dependence relationships of factors with the aid of grey relational analysis, analytical network process (ANP) and Decision-Making Trial and Evaluation Laboratory (DEMATEL). The initial model of the study is designed by considering five main criteria with 43 sub-criteria. DEMATEL is applied to construct interrelations among criteria and sub-criteria in the integrated model. By using this approach, the interdependencies strength among the criteria and sub-criteria is tested. The ANP method is then adopted in order to determine the relative importance of the CSFs, and used to identify how the CSFs are weighted and prioritized by the construction professionals, who are all working in different areas of the construction industry. The development of this MCDM model helps project parties in Malaysia in identifying the key elements and factors that need to be thoroughly considered and managed for construction project success. The results of this study can also be a guide for the construction organizations to allocate their resources such as financial and time for the construction projects according to the importance level of construction project success factors.

Pub.: 03 Dec '14, Pinned: 28 Jun '17

Health performance and cost management model for sustainable healthy buildings

Abstract: The construction industry is a flourishing business. Demand for ‘sustainable healthy buildings’ is rapidly increasing with the growing population. As the population increases, problems associated with health impacts of a building would also increase. Addressing the health problems of buildings could require raising of construction cost. In order to maintain a reasonable cost, a construction project might possibly have to compromise health performance and cost, which thus illustrates the major dilemmas being faced in the construction industry. Proper building health management is obligatory to reduce and control health problems and to maintain comfort levels throughout a building’s life cycle. To achieve this, a health performance evaluation model has been proposed to measure the level of health performance throughout a building’s life cycle. However, detail solutions of cost issues are not within scope of this paper. The objective of this paper is to propose a health performance and cost management model to achieve satisfactory health performance level within the project budget. The main significance of this model is to establish a decision-making process for decision-makers to improve and identify problems that could affect the health performance of a building throughout a building’s life cycle, thus allowing stakeholders to resolve shortcomings and to apply advanced solutions for building upgrade.

Pub.: 19 Jul '16, Pinned: 28 Jun '17