A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Neuro-fuzzy systems in construction engineering and management research
Abstract 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. 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. The literature review reveals that NFS classification methods are based on NFS architecture, learning algorithm, fuzzy method, and application area. 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.
Highlights Extensive literature review and content analysis of Neuro-fuzzy systems (NFS) in Construction Engineering and Management (CEM) Comprehensive review of latest advances in NFS in general and classification approaches of NFS Systematic categorization of CEM application domains and mapping to suitable NFS Identification of criteria to evaluate appropriateness and suitability of NFS in CEM applications Development of a guide to recommend suitable subset of NFS techniques to solve different types of CEM problems
Neuro-fuzzy systems in construction engineering and management research
Abstract 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. 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. The literature review reveals that NFS classification methods are based on NFS architecture, learning algorithm, fuzzy method, and application area. 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.
Highlights Extensive literature review and content analysis of Neuro-fuzzy systems (NFS) in Construction Engineering and Management (CEM) Comprehensive review of latest advances in NFS in general and classification approaches of NFS Systematic categorization of CEM application domains and mapping to suitable NFS Identification of criteria to evaluate appropriateness and suitability of NFS in CEM applications Development of a guide to recommend suitable subset of NFS techniques to solve different types of CEM problems
Neuro-fuzzy systems in construction engineering and management research
Tiruneh, Getaneh Gezahegne (author) / Fayek, Aminah Robinson (author) / Sumati, Vuppuluri (author)
2020-07-01
Article (Journal)
Electronic Resource
English
Neuro-fuzzy systems in construction engineering and management research
| Elsevier | 2020
Management of Pollution in Groundwater Systems Using Neuro-Fuzzy Model
| HENRY – Federal Waterways Engineering and Research Institute (BAW) | 2010
Energy Evaluation of Highway Construction Using Neuro-Fuzzy Techniques
| Tema Archive | 2012
Edificio project: a neuro-fuzzy approach to building energy management systems
| British Library Conference Proceedings | 1998