Predicting the mechanical properties of plastic concrete: An optimization method by using genetic programming and ensemble learners

Case Studies in Construction Materials, Год журнала: 2024, Номер 20, С. e03135 - e03135

Опубликована: Апрель 6, 2024

This study presents a comparative analysis of individual and ensemble learning algorithms (ELAs) to predict the compressive strength (CS) flexural (FS) plastic concrete. Multilayer perceptron neuron network (MLPNN), Support vector machine (SVM), random forest (RF), decision tree (DT) were used as base learners, which then combined with bagging Adaboost methods improve predictive performance. In addition, gene expression programming (GEP) was develop computational equations that can be CS FS An extensive database containing 357 125 data points obtained from literature, eight most impactful ingredients in model's development. The accuracy all models assessed using several statistical measures, including an error matrix, Akaike information criterion (AIC), K-fold cross-validation, other external validation equations. Furthermore, sensitivity SHAP performed evaluate input variables' relative significance impact on anticipated FS. Based measures criteria, GEP outpaces models, whereas, ELAs, SVR RF modified Bagging technique demonstrated superior SHapley Additive exPlanations (SHAP) reveal plastic, cement, water, age specimens have highest influence, while superplasticizer has lowest impact, is consistent experimental studies. Moreover, GUI GEP-based simple mathematical correlation enhance practical scope this effective tool for pre-mix design

Язык: Английский

---

Investigation of machine learning models in predicting compressive strength for ultra-high-performance geopolymer concrete: A comparative study

Construction and Building Materials, Год журнала: 2024, Номер 436, С. 136884 - 136884

Опубликована: Июнь 12, 2024

Язык: Английский

---

Prediction of high strength ternary blended concrete containing different silica proportions using machine learning approaches

Results in Engineering, Год журнала: 2023, Номер 17, С. 100973 - 100973

Опубликована: Фев. 25, 2023

The most often utilized material in construction is concrete. High plasticity, good economy, safety, and exceptional durability are a few of its characteristics. Concrete type structural that needs to be strong enough withstand different loads. compressive strength the concrete members crucial mechanical characteristic because brittleness. Furthermore, with ternary blended cementitious materials sophisticated composite material. present study explores binary mixes silica fume, ceramic powder, bagasse ash, alccofine, determine flexural strength. Results compression tests show mixes, including ultra-fine have higher impact additional on surface morphology was examined using scanning electron microscopy various mixes. This investigates linear regression, KNearest Neighbors (KNN), Bayesian-optimized extreme gradient boosting estimate (BO-XGBoost). Using coefficient determination (R2), mean absolute error (MAE), square (MSE), prediction results further validated. In comparison, regression BO-XGBoost models high accuracy towards outcome as indicated by R2 value equal 0.883 0.880, respectively, while for KNN 0.736. Additionally, normalized feature importance included determining input variables significantly influenced sensitivity model indicates CaO SiO2 shows significant predict

Язык: Английский

---

Modeling strength characteristics of basalt fiber reinforced concrete using multiple explainable machine learning with a graphical user interface

Scientific Reports, Год журнала: 2023, Номер 13(1)

Опубликована: Авг. 12, 2023

This study investigated the importance of applying explainable artificial intelligence (XAI) on different machine learning (ML) models developed to predict strength characteristics basalt-fiber reinforced concrete (BFRC). Even though ML is widely adopted in prediction concrete, black-box nature predictions hinders interpretation results. Among several attempts overcome this limitation by using AI, researchers have employed only a single explanation method. In study, we used three tree-based (Decision tree, Gradient Boosting and Light Machine) mechanical (compressive strength, flexural tensile strength) basal fiber For first time, two methods (Shapley additive explanations (SHAP) local interpretable model-agnostic (LIME)) provide for all models. These reveal underlying decision-making criteria complex models, improving end user's trust. The comparison highlights that obtained good accuracy predicting yet, their were either magnitude feature or order importance. disagreement pushes towards complicated based which further stresses (1) extending XAI-based research predictions, (2) involving domain experts evaluate XAI concludes with development "user-friendly computer application" enables quick basalt

Язык: Английский

---

Evaluating the Sensitivity of Machine Learning Models to Data Preprocessing Technique in Concrete Compressive Strength Estimation

Arabian Journal for Science and Engineering, Год журнала: 2024, Номер 49(10), С. 13709 - 13727

Опубликована: Фев. 26, 2024

Язык: Английский

---

A comparative study of prediction models for alkali-activated materials to promote quick and economical adaptability in the building sector

Construction and Building Materials, Год журнала: 2023, Номер 407, С. 133485 - 133485

Опубликована: Сен. 28, 2023

Язык: Английский

---

Predicting compressive strength of hollow concrete prisms using machine learning techniques and explainable artificial intelligence (XAI)

Heliyon, Год журнала: 2024, Номер 10(17), С. e36841 - e36841

Опубликована: Авг. 27, 2024

Язык: Английский

---

An interpretable probabilistic machine learning model for forecasting compressive strength of oil palm shell-based lightweight aggregate concrete containing fly ash or silica fume

Construction and Building Materials, Год журнала: 2024, Номер 426, С. 136176 - 136176

Опубликована: Апрель 8, 2024

Язык: Английский

---

Improving the experience of machine learning in compressive strength prediction of industrial concrete considering mixing proportions, engineered ratios and atmospheric features

Construction and Building Materials, Год журнала: 2024, Номер 444, С. 137884 - 137884

Опубликована: Авг. 15, 2024

Язык: Английский

---

Data-driven evolutionary programming for evaluating the mechanical properties of concrete containing plastic waste.

Case Studies in Construction Materials, Год журнала: 2024, Номер unknown, С. e03763 - e03763

Опубликована: Сен. 1, 2024

Язык: Английский

---