Hybrid GA-MANFIS Model for Organizational Competencies and Performance in Construction
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Bibliographic record
Abstract
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, which makes 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 of fuzzy 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 as being prone to early convergence due to local minima entrapment. To address these limitations, this paper proposes a hybrid NFS combining the evolutionary optimization technique of a genetic algorithm (GA) with a multi-output adaptive neuro-fuzzy inference system (MANFIS) that can handle multi-input multi-output (MIMO) problems for CEM applications. The proposed modeling approach is demonstrated using a case study that showed good results in predicting multiple organizational performance metrics using organizational competencies. The contributions of this paper are threefold: It (1) proposes a novel methodology of integrating different computing techniques for developing a GA-based multi-output adaptive neuro-fuzzy inference system (GA-MANFIS) model that can handle complex and nonlinear MIMO problems inherent in construction processes 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 that significantly influence organizational performance. By uniquely integrating these techniques, this model enables construction organizations to evaluate their competencies and predict multiple organizational performance metrics simultaneously, and researchers can adapt it for a variety of construction contexts.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it