Development of machine learning methods to predict the compressive strength of fiber-reinforced self-compacting concrete and sensitivity analysis

Construction and Building Materials, Год журнала: 2023, Номер 367, С. 130339 - 130339

Опубликована: Янв. 13, 2023

Язык: Английский

---

Prediction and optimization model of sustainable concrete properties using machine learning, deep learning and swarm intelligence: A review

Journal of Building Engineering, Год журнала: 2023, Номер 80, С. 108065 - 108065

Опубликована: Ноя. 3, 2023

Язык: Английский

---

Multi objective optimization of recycled aggregate concrete based on explainable machine learning

Journal of Cleaner Production, Год журнала: 2024, Номер 445, С. 141045 - 141045

Опубликована: Фев. 8, 2024

Язык: Английский

---

Metaheuristic optimization based- ensemble learners for the carbonation assessment of recycled aggregate concrete

Applied Soft Computing, Год журнала: 2024, Номер 159, С. 111661 - 111661

Опубликована: Апрель 23, 2024

This study addresses the enhanced prevalence of carbonation, a process accelerating steel reinforcement corrosion, in recycled aggregate concrete (RAC) compared to natural concrete. Traditional carbonation depth assessment methods RAC are noted for being labor-intensive, costly, and requiring specialized expertise. There's deficiency application machine learning techniques accurately predicting RAC, gap this aims fill. Utilizing extreme gradient boosting (XGBoost) technique, recognized its efficacy ensemble learning, innovates modeling RAC. It emphasizes criticality hyperparameter optimization XGBoost algorithm maximizing model accuracy. To achieve this, three novel metaheuristic algorithms, including reptile search (RSA), Aquila optimizer (AO), arithmetic (AOA), were introduced as global optimizers tunning hyperparameters. The was underpinned by comprehensive database compiled from extensive literature, facilitating development an accurate model. Through rigorous evaluations, sensitivity analyses, Wilcoxon signed-rank test, runtime comparisons, synthesized models demonstrated exceptional accuracy, with coefficients determination exceeding 0.95. XGBoost-AO algorithm, particular, showcased superior performance, XGBoost-RSA providing efficient predictions considering runtime. SHapley Additive exPlanations (SHAP) interpretation highlighted environmental conditions significant influencers. A user-friendly graphical user interface developed, enhancing practical utility findings progression over time. research significantly advances predictive accuracy contributing sustainable management infrastructures emphasizing integration advanced structural engineering advancements.

Язык: Английский

---

Development of compressive strength prediction platform for concrete materials based on machine learning techniques

Journal of Building Engineering, Год журнала: 2023, Номер 80, С. 107977 - 107977

Опубликована: Окт. 20, 2023

Язык: Английский

---

Enhancing compressive strength prediction in self-compacting concrete using machine learning and deep learning techniques with incorporation of rice husk ash and marble powder

Case Studies in Construction Materials, Год журнала: 2023, Номер 19, С. e02557 - e02557

Опубликована: Окт. 7, 2023

Focusing on sustainable development, the demand for alternative materials in concrete, especially Self-Compacting Concrete (SCC), has risen due to excessive cement usage and resulting CO2 emissions. As Compressive Strength (CS) is dominant among concrete properties, this research concentrates developing SCC by incorporating Rice Husk Ash (RHA) Marble Powder (MP) as filler replacements, respectively, while applying Machine Learning (ML) Deep (DL) techniques forecast CS of RHA/MP-based SCC. The further evaluates material characteristics, with a strong emphasis ML DL prediction. samples various mixed ratios were cast examined after 91 days collect data model application. In experimental technique, 133 gathered, was predicted using seven input factors (cement, RHA, MP, superplasticizer, coarse aggregate, fine water) an 80:20 ratio. Various algorithms, including linear regression, ridge lasso K-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), random forest (RF), boosting methods such gradient boost (GB), XG (XGB), adaptive (ADB) are employed, along technique backpropagation neural network (BPNN) different optimizer algorithms (Adam, SGD, RMSprop) predict validated evaluation parameters R-squared (R2), mean squared error (MSE), normalized root (NRMSE), absolute (MAE), percentage (MAPE). Comparatively, ensemble BPNN Adam RMSprop optimizers demonstrate high accuracy predicting outcomes, indicated their coefficient correlation R2 values low values.

Язык: Английский

---

Prediction of non-uniform shrinkage of steel-concrete composite slabs based on explainable ensemble machine learning model

Journal of Building Engineering, Год журнала: 2024, Номер 88, С. 109002 - 109002

Опубликована: Март 12, 2024

Язык: Английский

---

A review on properties and multi-objective performance predictions of concrete based on machine learning models

Materials Today Communications, Год журнала: 2025, Номер unknown, С. 112017 - 112017

Опубликована: Фев. 1, 2025

Язык: Английский

---

Prediction of high-temperature creep in concrete using supervised machine learning algorithms

Construction and Building Materials, Год журнала: 2023, Номер 400, С. 132828 - 132828

Опубликована: Авг. 6, 2023

Язык: Английский

---

Classification of geogrid reinforcement in aggregate using machine learning techniques

International Journal of Geo-Engineering, Год журнала: 2024, Номер 15(1)

Опубликована: Фев. 12, 2024

Abstract The present study proposes a novel ML methodology for differentiating between unstabilized aggregate specimens and those stabilized with triangular rectangular aperture geogrids. This utilizes the compiled experimental results obtained from under repeated loading into balanced, moderate-sized database. efficacy of five models, including tree-ensemble single-learning algorithms, in accurately identifying each specimen class was explored. Shapley’s additive explanation used to understand intricacies models determine global feature importance ranking input variables. All could identify an accuracy at least 0.9. outperformed when all three classes (unstabilized by geogrids) were considered, light gradient boosting machine showing best performance—an 0.94 area curve score 0.98. According explanation, resilient modulus confining pressure identified as most important features across models. Therefore, proposed may be effectively type presence geogrid reinforcement aggregates, based on few material properties performance loading.

Язык: Английский

---