Simulation-Based Fuzzy Logic Approach to Assessing the Effect of Project Quality Management on Construction Performance

Abstract

This paper reports the development of an approach to integrate the appropriate modeling techniques for estimating the effect of project quality management (PQM) on construction performance. This modeling approach features a causal structure that depicts the interaction among the PQM factors affecting quality performance in a given construction operation. In addition, it makes use of fuzzy sets and fuzzy logic in order to incorporate the subjectivity and uncertainty implicit in the performance assessment of these PQM factors to discrete-event simulation models. The outcome is a simulation approach that allows experimenting with different performance levels of the PQM practices implemented in a construction project and obtaining the corresponding productivity estimates of the construction operations. These estimates are intended to facilitate the decision making regarding the improvement of a PQM system implemented in a construction project. A case study is used to demonstrate the usefulness of the proposed simulation approach for evaluating diverse performance improvement alternatives for a PQM system.

Resource-Based Quality Performance Estimation Method for Construction Operations

“…In addition, since quality performance is greatly influenced by the management structure of the organization and complex quality management processes must include the construction process model [31]. Therefore, it is necessary to analyze the factors that affect the quality performance of the construction process in the Project Quality Management (PQM) and apply it to the process model to evaluate the quality performance [32]. However, while DES is a quantitative data-centered analysis technology, quality performance is a qualitative element-centered evaluation method, so qualitative quality performance quantification is essential to the construction simulation model.…”

Fuzzy-Based Multivariate Analysis for Input Modeling of Risk Assessment in Wind Farm Projects

“…A fuzzy subset (e.g., subset = O), or one row from the matrix, will be selected such that the product of the row summation and the corresponding frequency of occurrence is the maximum. Then, the selected fuzzy subset (e.g., subset = O) will be used to calculate the mean, µ I , and the variance, σ 2 I , of the marginal risk impact according to the following equations derived from [50,[59][60][61]:…”

Measuring the performance of project risk management: a preliminary model

“…This is a permanent activity within organizations for closing gaps, identifying opportunities, establishing mechanisms to solve problems, and improving processes continuously. This is particularly important for those processes that are critical for the success of projects (Corona-Suárez et al, 2014).…”