ArticleScientific reports2025
Hybrid metaheuristic optimized Catboost models for construction cost estimation of concrete solid slabs.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A hybrid experimental and machine learning framework for designing and predicting compressive strength of ultra-high-performance concrete.Scientific reports · 2026Article
- Evaluation of advanced AI models for accurate prediction and uncertainty analysis of aeration inception in hydrofoil-crested stepped spillways.Scientific reports · 2026Article
- Assessing the impact of groundwater abstraction and concrete dam fractures on saltwater intrusion using numerical modeling and interpretable machine learning.Scientific reports · 2026Article
- Binary Pufferfish Optimization Algorithm for Combinatorial Problems.Biomimetics (Basel, Switzerland) · 2025Article
- Machine learning glucose forecasting models for septic patients.Scientific reports · 2025Article
- Explainable ML modeling of saltwater intrusion control with underground barriers in coastal sloping aquifers.Scientific reports · 2025Article
- Hydraulic Performance Modeling of Inclined Double Cutoff Walls Beneath Hydraulic Structures Using Optimized Ensemble Machine Learning.Scientific reports · 2025Article
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Authors and funding
2 authors.
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Abstract
Accurate construction cost prediction is essential for effective project planning and resource allocation, particularly in the competitive construction industry. This study introduces an advanced approach to predicting the costs of concrete solid slabs by combining the Categorical Boosting (CatBoost) model with three hybrid metaheuristic optimization models: Phasor Particle Swarm Optimization (PPSO-CatBoost), Dwarf Mongoose Optimization (DMO-CatBoost), and Atom Search Optimization (ASO-CatBoost). These hybrid models were designed to optimize critical hyperparameters, including depth, learning rate, and iterations, and were benchmarked against a standalone CatBoost model using various performance metrics, such as residual error cumulative distribution (REC) curves, scatter plots, violin plots, and quantitative measures. The results reveal that the hybrid models consistently outperform the standalone CatBoost model, with ASO-CatBoost achieving the best overall performance with determination coefficient (R
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