Implementation of machine learning technologies in construction maintenance: A strategic analysis

Current predictive maintenance systems in construction rely on static machine learning approaches that fail to adapt to evolving operational environments, achieving only 3%–7% performance improvements over individual models and suffering 15%–25% performance degradation when transferred across domains. This research develops and validates an Adaptive Ensemble Framework that dynamically optimizes algorithm selection through real-time data assessment and performance feedback.

The framework’s meta-learning architecture continuously adapts ensemble weights using data complexity measures, temporal pattern analysis, and uncertainty quantification metrics. Unlike static approaches, the system integrates scikit-learn and TensorFlow models through dynamic optimization algorithms that respond to changing conditions without manual reconfiguration. The framework provides uncertainty-aware predictions with confidence intervals essential for safety-critical construction decisions.

Comprehensive evaluation across four industries using 50,000+ maintenance records from major construction firms demonstrates substantial improvements. The adaptive ensemble achieves F1-score of 0.934 in construction delay prediction, representing 15.3% improvement over individual models and 8.7% enhancement over static ensembles. Cross-industry validation reveals successful knowledge transfer with minimal performance degradation ($<$5%).

This research contributes three scholarly advances: (i) the first real-time adaptive ensemble framework eliminating manual hyperparameter tuning, (ii) uncertainty quantification mechanisms for safety-critical applications, and (iii) robust cross-industry transferability through systematic domain adaptation. The framework extends beyond construction to manufacturing, energy, and transportation sectors, demonstrating computational efficiency with sub-100ms latency and linear scaling characteristics. These contributions establish new benchmarks for adaptive machine learning in industrial predictive maintenance.