A Big Data and FMEA-based construction quality risk evaluation model considering project schedule for Shanghai apartment projects. Issue 1 (31st July 2019)

• Record Type: Journal Article
• Authors: Ma, Guofeng; Wu, Ming
• Is Part Of: International journal of quality & reliability management. Volume 37:Issue 1(2020)
• Page Start: 18
• Page End: 33
• Publication Date: 2019-07-31
• Subjects: Neural network -- Construction projects -- Failure mode and effect analysis -- Earned value management -- Quality risk assessment; Quality control -- Periodicals; 658.562
• DOI: 10.1108/IJQRM-11-2018-0318
• Languages: English
• ISSNs: 0265-671X

Abstract

Abstract : Purpose: The purpose of this paper is to mine information on the construction process of previous projects to develop a construction plan that meets both quality requirements and schedule constraints. Design/methodology/approach: This paper uses a failure mode and effect analysis to evaluate the construction quality of 311 apartments in Shanghai. The authors also evaluate construction-scheduling control using the earned value management technique and implement an artificial neural network to correlate the results. The authors then develop a quality risk and schedule correlation model based on Big Data. The model can predict the relationship between the planned schedule and the project quality risk using multiple variables such as the number of layers, the schedule performance index and budget costs. Findings: The methodology offers an innovative approach for assessment on the relationship between quality risk and project schedule. The authors have also built a multiple regression analysis model for comparative purposes with the model. The results show that the proposed model can better describe the relationship. The model can provide a quantitative quality risk value that changes with the planned schedule, as well as help project managers to understand the relationship between quality risk and project scheduling more accurately. Research limitations/implications: The research approach only focuses on quality risk under the impact of scheduling. Future efforts might focus on developing a model that connects failure models with project schedules and costs in order to improve the effort of quality management. Practical implications: The model based on Big Data in this paper is developed using real projects and reflects the relationship between project quality risk and scheduling in real environments. The created application provides support for project managers to develop and adjust quality plans and schedules, thereby reducing deviations in quality and scheduling objectives. Originality/value: The authors make full use of historical project data from the perspective of both quality and schedule management, and provide a novel method to intelligently and objectively analyze the relationship between quality risk and scheduling.