Predictive modeling for compressive strength of 3D printed fiber-reinforced concrete using machine learning algorithms

Case Studies in Construction Materials, Journal Year: 2023, Volume and Issue: 20, P. e02728 - e02728

Published: Nov. 30, 2023

Three-dimensional (3D) printing in the construction industry is growing rapidly due to its inherent advantages, including intricate geometries, reduced waste, accelerated construction, cost-effectiveness, eco-friendliness, and improved safety. However, optimizing mixture composition for 3D-printed concrete remains a formidable task, encompassing multiple variables requiring comprehensive trial-and-error experimentation process. Accordingly, this study used seven machine learning (ML) algorithms, support vector regression (SVR), decision tree (DT), SVR-Bagging, SVR-Boosting, random forest (RF), gradient boosting (GB), gene expression programming (GEP) forecasting compressive strength (CS) of 3D printed fiber-reinforced (3DP-FRC). For model development, 299 data points were collected from experimental studies split into two portions: 70% training 30% validation. Various statistical metrics employed examine accuracy generalizability established models. The DT, RF, GB, GEP models demonstrated higher validation set, achieving correlation (R) values 0.987, 0.986, 0.98, respectively. exhibited mean absolute error (MAE) scores 4.644, 3.989, 3.90, 5.691, Furthermore, combination SVR with bagging techniques slightly compared individual model. Additionally, Shapley Additive exPlanations (SHAP) approach unveils proportional significance parameters influencing CS 3DP-FRC. SHAP technique revealed that water, silica fume, superplasticizer, sand content, loading directions are dominant estimating local interpretability intrinsic relationship between diverse input their impacts on offers significant insights optimum mix proportion

Language: Английский

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Use of interpretable machine learning approaches for quantificationally understanding the performance of steel fiber-reinforced recycled aggregate concrete: From the perspective of compressive strength and splitting tensile strength

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 137, P. 109170 - 109170

Published: Aug. 27, 2024

Language: Английский

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Explainable machine learning methods for predicting water treatment plant features under varying weather conditions

Results in Engineering, Journal Year: 2024, Volume and Issue: 21, P. 101930 - 101930

Published: March 1, 2024

Accurately predicting key features in WWTPs is essential for optimizing plant performance and minimizing operational costs. This study assesses the potential of various machine learning models inflow to anoxic sludge reactors. Firstly, it conducts a comprehensive evaluation diverse models, including k-Nearest Neighbors (kNN), Random Forest (RF), XGBoost, CatBoost, LightGBM, Decision Tree Regression (DTR), flow into Anoxic section under weather conditions (dry, rainy, stormy). Secondly, introduces parsimonious guided by variable importance from XGBoost algorithm. Furthermore, employs SHAP (SHapley Additive exPlanations) elucidate model predictions, providing insights contribution each feature. Data COST Benchmark Simulation Model (BSM1) used verify investigated models' effectiveness. Each dataset consists 14 days influent data at 15-minute intervals, with 80% training. Results show that ensemble methods, particularly CatBoost demonstrate satisfactory predictive results presence increased variability rainy stormy conditions. Notably, achieve average Mean Absolute Percentage Error values 1.33% 1.59%, outperforming other methods.

Language: Английский

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Estimating the compressive and tensile strength of basalt fibre reinforced concrete using advanced hybrid machine learning models

Structures, Journal Year: 2025, Volume and Issue: 71, P. 108138 - 108138

Published: Jan. 1, 2025

Language: Английский

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Vegetable Fibers in Cement Composites: A Bibliometric Analysis, Current Status, and Future Outlooks

Materials, Journal Year: 2025, Volume and Issue: 18(2), P. 333 - 333

Published: Jan. 13, 2025

The use of vegetable fibers (VFs) in cement-based composites has increased recent years owing to their minimal environmental impact and notable particular properties. VFs have aroused interest within the scientific community because potential as a sustainable alternative for construction. This study presents comprehensive bibliometric analysis cement using data from Scopus database scientometric tools explore publication trends, influential sources, research directions. Key findings reveal steady increase publications, with Construction Building Materials identified leading journal field China Brazil prominent contributors terms publications citations. highlights strong focus on mechanical properties durability, reflecting optimizing VF Furthermore, this includes revision most studies addressing classification, durability improvements, advanced applications building applications. Finally, future opportunities are outlined, emphasizing Life Cycle Assessment (LCA), industry integration, CO2 absorption, application machine learning techniques advance development composites. work provides overview field, suggesting guidelines promoting collaborative research.

Language: Английский

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Bayesian-optimized tree-based models for predicting the shear strength of U-shaped externally bonded FRP-strengthened RC beams

Asian Journal of Civil Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: March 6, 2025

Language: Английский

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Predicting the compressive strength of engineered geopolymer composites using automated machine learning

Construction and Building Materials, Journal Year: 2024, Volume and Issue: 442, P. 137509 - 137509

Published: July 31, 2024

Language: Английский

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Shear Wave Velocity Prediction with Hyperparameter Optimization

Information, Journal Year: 2025, Volume and Issue: 16(1), P. 60 - 60

Published: Jan. 16, 2025

Shear wave velocity (Vs) is an important soil parameter to be known for earthquake-resistant structural design and determining the dynamic properties of soils such as modulus elasticity shear modulus. Different Vs measurement methods are available. However, these methods, which costly labor intensive, have led search new Vs. This study aims predict (Vs (m/s)) using depth (m), cone resistance (qc) (MPa), sleeve friction (fs) (kPa), pore water pressure (u2) N, unit weight (kN/m3). Since varies with depth, regression studies were performed at depths up 30 m in this study. The dataset used open-source dataset, data from Taipei Basin. was extracted, a 494-line created. In study, HyperNetExplorer 2024V1, prediction based on shell (fs), (kN/m3) values could satisfactory results (R2 = 0.78, MSE 596.43). Satisfactory obtained Explainable Artificial Intelligence (XAI) models also used.

Language: Английский

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Modeling Soil Behavior with Machine Learning: Static and Cyclic Properties of High Plasticity Clays Treated with Lime and Fly Ash

Buildings, Journal Year: 2025, Volume and Issue: 15(2), P. 288 - 288

Published: Jan. 19, 2025

Soils may not always be suitable to fulfill their intended function. Soil improvement can achieved by mechanical or chemical methods, especially in transportation facilities. L and FA additives are frequently used as additives. In this study, two natural clay samples with extreme very high plasticity were improved using admixtures, properties under static repeated loads investigated ML methods. Two soil from different sites analyzed. eight datasets used. There 14 inputs, including specific gravity (Gs), void ratio (eo), sieve analysis (+No.4, −No.200), size, LL, plastic limit (PL), index (PI), linear shrinkage (Ls), (SL), cure day, agent, type, agent percentage. The outputs swelling (compressive, percent), compressive strengths, modulus of elasticity, compressibility soaked non-soaked conditions. Prediction is attempted (ML) techniques. techniques for regression (such Decision Tree Regression (DTR) K-nearest neighbors (KNN)). SHapley Additive Explanations (SHAP), the impact inputs on observed, it was generally found that PL LL had highest outputs. Different performance metrics evaluation. results showed these predict cyclic extremely clays (R2 > 0.99). These highlight general applicability models containing properties.

Language: Английский

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An Interpretable XGBoost-SHAP Machine Learning Model for Reliable Prediction of Mechanical Properties in Waste Foundry Sand-Based Eco-Friendly Concrete

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104307 - 104307

Published: Feb. 1, 2025

Language: Английский

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