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Surrogate Model Development for Digital Experiments in Welding
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Predicting subcontractor performance using web-based Evolutionary Fuzzy Neural Networks.

Chien-Ho Ko1

  • 1Department of Civil Engineering, National Pingtung University of Science and Technology, Neipu, Pingtung 912, Taiwan. fpecount@yahoo.com.tw

Thescientificworldjournal
|July 19, 2013
PubMed
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Predicting subcontractor performance is crucial for project success. This study introduces Evolutionary Fuzzy Neural Networks (EFNNs), a hybrid model that accurately forecasts subcontractor performance, improving project outcomes.

Area of Science:

  • Construction Management
  • Artificial Intelligence
  • Predictive Analytics

Background:

  • Subcontractor performance significantly impacts project timelines, costs, and quality.
  • Inaccurate subcontractor selection can lead to delays, budget overruns, and defects.
  • A reliable method for predicting subcontractor performance is essential in the construction industry.

Purpose of the Study:

  • To develop and validate a web-based system for predicting subcontractor performance.
  • To integrate Genetic Algorithms (GAs), Fuzzy Logic (FL), and Neural Networks (NNs) into an Evolutionary Fuzzy Neural Network (EFNN) model.
  • To enhance the accuracy and efficiency of subcontractor performance prediction.

Main Methods:

  • Development of web-based Evolutionary Fuzzy Neural Networks (EFNNs).

Related Experiment Videos

Last Updated: May 9, 2026

Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

  • EFNNs combine Fuzzy Logic for decision-making under uncertainty and Neural Networks for pattern recognition.
  • Genetic Algorithms optimize parameters within the Fuzzy Logic and Neural Network components.

    • Main Results:

    • The proposed EFNN model demonstrated superior performance compared to standalone Fuzzy Logic and Neural Networks.
    • EFNNs achieved higher prediction accuracy for subcontractor performance.
    • The model reduces the effort required for performance prediction, offering web-based remote access.

    Conclusions:

    • EFNNs provide a reliable and scientific mechanism for predicting subcontractor performance.
    • The developed system enhances project management by enabling informed subcontractor selection.
    • Web-based access to EFNNs empowers field operators with predictive capabilities.