Novel Probabilistic Approach for Quantification of Cost-Overruns Risk and Determination of Primary Causes

• Published in: Mathematical problems in engineering Vol. 2024; pp. 1 - 17
• Main Authors: Liu, Tianyu, Zhang, Shen, Wang, Xingyu
• Format: Journal Article
• Language: English
• Published: New York Hindawi 2024; John Wiley & Sons, Inc
• Subjects: Algorithms; Building information modeling; Construction costs; Construction industry; Cost analysis; Cost control; Deviation; Methods; Ontology; Probability density functions; Project management; Reliability analysis; Schedules; Semantics; Statistical analysis; Success
• ISSN: 1024-123X; 1026-7077; 1563-5147; 1563-5147
• DOI: 10.1155/2024/8893201

Cost management is a crucial component of construction project management, which assumes an increasingly critical role in modern large-scale projects. Ideally, cost management is achieved through a comparison between the current cost and the predetermined cost schedule. However, in real-world projects, it is often difficult to consistently obtain accurate costs for each cost item throughout the project life cycle and to predict the probability of various risk occurrences. To address this challenge, we propose a novel building information modeling- (BIM-) based algorithm to quantify the risk of cost overruns and cost deviation; and identify the primary causes without a detailed cost breakdown. The role of BIM in our approach is to automatically extract precise quantitative information that we can use to calculate the probability density function of the current cost and the expected total cost and eventually combine reliability analysis to provide the risk probability. The numerical experiment demonstrates that this algorithm can estimate the probability of cost overruns and cost deviation, and identify the primary cause of deviation. Additionally, the sensitivity requirements for cost management can be adjusted by adjusting the parameters, indicating its potential to meet multiple requirements in the different projects. The proposed algorithm provides a method for detecting the current risk of cost overruns and cost deviation, as well as identifying the primary cause of deviation, which is of potential value in the practical applications.