Hybrid GA-MANFIS Model for Organizational Competencies and Performance in Construction

• Author(s) / Creator(s)

  • Tiruneh, Getaneh Gezahegne
  • Fayek, Aminah Robinson

• The majority of competency and performance modeling methods available in the literature are deterministic conceptual, statistical,and/or regression models that cannot capture the subjective uncertainty, complex, and nonlinear relationships inherent in construction, whichmakes accurate prediction difficult. Past studies utilized neuro-fuzzy system (NFS) models, such as adaptive neuro-fuzzy inference system(ANFIS), that combine the learning power of artificial neural networks and functionality offuzzy systems to develop accurate predictive models.ANFIS is robust, fast, and effective in solving complex problems for a range of real-world construction engineering and management (CEM)applications. NFS models such as ANFIS have some limitations in handling multiple outputs common in construction industry problems, such asbeing prone to early convergence due to local minima entrapment. To address these limitations, this paper proposes a hybrid NFS combining theevolutionary optimization technique of a genetic algorithm (GA) with a multi-output adaptive neuro-fuzzy inference system (MANFIS) that canhandle multi-input multi-output (MIMO) problems for CEM applications. The proposed modeling approach is demonstrated using a case studythat showed good results in predicting multiple organizational performance metrics using organizational competencies. The contributions of thispaper are threefold: It (1) proposes a novel methodology of integrating different computing techniques for developing a GA-based multi-outputadaptive neuro-fuzzy inference system (GA-MANFIS) model that can handle complex and nonlinear MIMO problems inherent in constructionprocesses and practices; (2) relates organizational competencies to performance and predicts multiple organizational performance metrics; and(3) provides a GA-based feature selection approach that reduces data dimensionality, enabling identification of organizational competencies thatsignificantly influence organizational performance. By uniquely integrating these techniques, this model enables construction organizations toevaluate their competencies and predict multiple organizational performance metrics simultaneously, and researchers can adapt it for a variety ofconstruction contexts

• Date created

  2022-01-24

• Subjects / Keywords

  • Artificial intelligence
  • Construction
  • Hybrid neuro-fuzzy systems
  • Organizational issues
  • Organizational competency
  • Performance

• Type of Item

  Article (Draft / Submitted)

• DOI

  https://doi.org/10.7939/r3-rd52-dc28

• License

  © ASCE