Predict a model of the reliability engineering approach to estimating, risk management, and cost predicting for private residential projects in Iran

• Published in: Asian journal of civil engineering. Building and housing Vol. 24; no. 3; pp. 869 - 883
• Main Authors: Karaji, Arash Gorgin, Pourrostam, Towhid, Abbasianjahromi, Hamidreza, Monajeminejad, Soheil
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.04.2023
• Subjects: Building Materials; Civil Engineering; Engineering; Sustainable Architecture/Green Buildings; Predict; Risk; SEM; Reliability; Cost; Estimation
• ISSN: 1563-0854; 2522-011X
• DOI: 10.1007/s42107-022-00538-5

Like other engineering fields, reliability engineering is developing and progressing rapidly, and its purpose is to expand methods and adequate tools for modeling, predicting, and evaluating the reliability, serviceability, and availability of systems. Alternatively, the system's ability to initiate and control the process and activity is limited. This study is functional in terms of its purpose, and it uses a field survey questionnaire to acquire primary data. This study was done due to the importance of the subject. Little research has been conducted on all aspects of accurate estimating, anticipating housing costs, and reliability in Iran. This article aims to predict engineering reliability in cost and risk management, which is done by private-sector residential projects. The case study focused on private-sector residential builders in Iran. The approach used was primary random sampling, and 120 people participated in the survey. Using Cronbach's alpha, the questionnaire's reliability was verified. The collected data was analyzed by unique statistical methods with SPSS and AMOS. The factor analysis model and structural equation modeling were used to predict the model. The evaluation indicators of the overall model are desirable, standard, and have high strengths for measuring engineering reliability in relation to risk management and cost predicting. The results indicate the essential challenges, so we have provided practical solutions for them.