Automated prioritization of construction project requirements using machine learning and fuzzy Failure Mode and Effects Analysis (FMEA)

Hassan, Fahad; Nguyen, Tien; Le, Tuyen; Le, Chau

doi:10.1016/j.autcon.2023.105013

Cited by 8 publications

(5 citation statements)

References 29 publications

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“…One of the notable advantages of the fuzzyfied FMEA approach is its ability to provide a more refined prioritization of risks (Alizadeh et al. , 2022; Hassan et al. , 2023).…”

Section: Discussionmentioning

confidence: 99%

“…One of the notable advantages of the fuzzyfied FMEA approach is its ability to provide a more refined prioritization of risks (Alizadeh et al, 2022;Hassan et al, 2023). By incorporating triangular and trapezoidal membership functions, the fuzzyfied FMEA tool accounts for variations in each variable, offering a more realistic representation of potential hazard (Abbasbandy and Hajjari, 2009;Azam et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

“…Tailored for accuracy, this method provided a nuanced perspective on risk evaluation within this complex sector. Hassan et al . (2023) ventured into construction projects, integrating machine learning, fuzzy logic and FMEA for requirement prioritization.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Tailored for accuracy, this method provided a nuanced perspective on risk evaluation within this complex sector. Hassan et al (2023) ventured into construction projects, integrating machine learning, fuzzy logic and IJQRM 41,4 FMEA for requirement prioritization. In the electric vehicle charging sector, Tian et al (2023) introduced fuzzy set linguistic distribution combined with FMEA.…”

Section: Integrating Fuzzy Logic and Fmeamentioning

confidence: 99%

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Proposal of a facilitating methodology for fuzzy FMEA implementation with application in process risk analysis in the aeronautical sector

Resende,

Dedini,

Eckert

et al. 2023

IJQRM

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PurposeThis study aims to propose a facilitating methodology for the application of Fuzzy FMEA (Failure Mode and Effect Analysis), comparing the traditional approach with fuzzy variations, supported by a case application in the aeronautical sector.Design/methodology/approachBased on experts' opinions in risk analysis within the aeronautical sector, rules governing the relationship between severity, occurrence, detection and risk factor were defined. This served as input for developing a fuzzyfied FMEA tool using the Matlab Fuzzy Logic Toolbox. The tool was applied to the sealing process in a company within the aeronautical sector, using triangular and trapezoidal membership functions, and the results were compared with the traditional FMEA approach.FindingsThe results of the comparative application of traditional FMEA and fuzzyfied FMEA using triangular and trapezoidal functions have yielded valuable insights into risk analysis. The findings indicated that fuzzyfied FMEA maintained coherence with the traditional analysis in identifying higher-risk effects, aligning with the prioritization of critical failure modes. Additionally, fuzzyfied FMEA allowed for a more refined prioritization by accounting for variations in each variable through fuzzy rules, thereby improving the accuracy of risk analysis and providing a more realistic representation of potential hazards. The application of the developed fuzzyfied FMEA approach showed promise in enhancing risk assessment in the aeronautical sector by considering uncertainties and offering a more detailed and context-specific analysis compared to conventional FMEA.Practical implicationsThis study emphasizes the potential of fuzzyfied FMEA in enhancing risk assessment by accurately identifying critical failure modes and providing a more realistic representation of potential hazards. The application case reveals that the proposed tool can be integrated with expert knowledge to improve decision-making processes and risk mitigation strategies within the aeronautical industry. Due to its straightforward approach, this facilitating methodology could also prove beneficial in other industrial sectors.Originality/valueThis paper presents the development and application of a facilitating methodology for implementing Fuzzy FMEA, comparing it with the traditional approach and incorporating variations using triangular and trapezoidal functions. This proposed methodology uses the Toolbox Fuzzy Logic of Matlab to create a fuzzyfied FMEA tool, enabling a more nuanced and context-specific risk analysis by considering uncertainties.

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“…One of the notable advantages of the fuzzyfied FMEA approach is its ability to provide a more refined prioritization of risks (Alizadeh et al. , 2022; Hassan et al. , 2023).…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Theoretical Backgroundmentioning

confidence: 99%

Section: Integrating Fuzzy Logic and Fmeamentioning

confidence: 99%

See 2 more Smart Citations

Proposal of a facilitating methodology for fuzzy FMEA implementation with application in process risk analysis in the aeronautical sector

Resende,

Dedini,

Eckert

et al. 2023

IJQRM

View full text Add to dashboard Cite

show abstract

“…Furthermore, researchers have explored data-driven approaches using machine learning to continuously update and predict risk priority numbers (RPNs) for new failure modes (Peddi et al, 2023). Hassan et al (2023) successfully used historical data and convolutional neural networks (CNNs) to automate the prioritization of contract requirements. Yucesan et al (2021) used fuzzy best-worst and fuzzy Bayesian network methods to evaluate risk parameters in FMEA.…”

Section: Advances In Ai For Fmeamentioning

confidence: 99%

Integrating large language models for improved failure mode and effects analysis (FMEA): a framework and case study

Hassani,

Masrour,

Kourouma

et al. 2024

Proc. Des. Soc.

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The manual execution of failure mode and effects analysis (FMEA) is time-consuming and error-prone. This article presents an approach in which large language models (LLMs) are integrated into FMEA. LLMs improve and accelerate FMEA with human in the loop. The discussion looks at software tools for FMEA and emphasizes that the tools must be tailored to the needs of the company. Our framework combines data collection, pre-processing and reliability assessment to automate FMEA. A case study validates this framework and demonstrates its efficiency and accuracy compared to manual FMEA.

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Applying Machine Learning and Particle Swarm Optimization for predictive modeling and cost optimization in construction project management

almahameed,

Bisharah

2023

Asian J Civ Eng

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Automated prioritization of construction project requirements using machine learning and fuzzy Failure Mode and Effects Analysis (FMEA)

Cited by 8 publications

References 29 publications

Proposal of a facilitating methodology for fuzzy FMEA implementation with application in process risk analysis in the aeronautical sector

Proposal of a facilitating methodology for fuzzy FMEA implementation with application in process risk analysis in the aeronautical sector

Integrating large language models for improved failure mode and effects analysis (FMEA): a framework and case study

Applying Machine Learning and Particle Swarm Optimization for predictive modeling and cost optimization in construction project management

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