This study discusses the hybridization of these modeling techniques and the future trends for cost model development, limitations, and recommendations. In addition, the study reviews different computational intelligence (CI) techniques and ensemble methods conducted to develop practical cost prediction models.
This study reviews the common practices and procedures conducted to identify the cost drivers that the past literature has classified into two main categories: qualitative and quantitative procedures. This paper systematically categorizes CEM application domains (decision making, prediction/forecasting, evaluation/assessment, system modeling and analysis, simulation, and optimization) and maps them to NFS based on their suitability, which is determined using the performance evaluation criteria of convergence speed, computational complexity, interpretability, accuracy, and local minima trapping. The literature review reveals that NFS classification methods are based on NFS architecture, learning algorithm, fuzzy method, and application area. This paper contributes three things previously lacking in CEM literature: a systematic review and content analysis of published articles related to NFS topics in CEM research identification of criteria to evaluate different NFS and recommendations to researchers and industry practitioners in choosing a suitable subset of NFS techniques for solving different types of CEM problems. Neuro-fuzzy systems (NFS) can explicitly represent and model the input–output relationships of complex problems and non-linear systems, like those inherent in real-world construction engineering and management (CEM) problems.
0 kommentar(er)
