Coupled extreme gradient boosting algorithm with artificial intelligence models for predicting compressive strength of fiber reinforced polymer- confined concrete

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 134, P. 108674 - 108674

Published: June 3, 2024

Language: Английский

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Assessment of acoustic and mechanical properties in modified rubberized concrete

Case Studies in Construction Materials, Journal Year: 2024, Volume and Issue: 20, P. e03063 - e03063

Published: March 20, 2024

Utilizing waste crumb rubber and substituting cement in concrete with industrial materials, such as ground granulated blast furnace slag (GBFS), represents a promising pathway towards achieving sustainable development. This study assesses the inclusion of powder (RP) alongside graphene nanoplatelets (GnPs) an efficient surfactant (Tween 80), conjunction (GBFS) concrete, terms acoustical mechanical properties. The RP content varied from 4% to 18% replacement for sand, while GnPs (0.1% 0.7%) GBFS (30%) were utilized substitutes cement. compressive (CS), flexural (FS), tensile strength (TS), sound absorption (α), noise reduction coefficient (NRC) modified rubberized experimentally theoretically evaluated. outcomes revealed that optimum was 11% 0.4%, respectively, which CS, FS, TS 48.2 MPa, 6.3 2.7 compared control mix (45.5 5.2 2.5 MPa). In addition, 0.556 at frequency 1760 Hz 0.16, (0.44, 0.109), highest value α (0.603) achieved when 18%. It can be concluded proposed mixture fulfilled requirements both properties well enhanced sustainability by addressing disposal minimizing CO2 emissions. also suggests feasible direction further exploration into its performance under elevated temperatures aggressive environmental conditions.

Language: Английский

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Supplementary cementitious materials-based concrete porosity estimation using modeling approaches: A comparative study of GEP and MEP

REVIEWS ON ADVANCED MATERIALS SCIENCE, Journal Year: 2024, Volume and Issue: 63(1)

Published: Jan. 1, 2024

Abstract Using supplementary cementitious materials in concrete production makes it eco-friendly by decreasing cement usage and the corresponding CO 2 emissions. One key measure of concrete’s durability performance is its porosity. An empirical prediction porosity high-performance with added elements goal this work, which employs machine learning approaches. Binder, water/cement ratio, slag, aggregate content, superplasticizer (SP), fly ash, curing conditions were considered as inputs database. The aim study to create ML models that could evaluate Gene expression programming (GEP) multi-expression (MEP) used develop these models. Statistical tests, Taylor’s diagram, R values, difference between experimental predicted readings metrics With = 0.971, mean absolute error (MAE) 0.348%, root square (RMSE) 0.460%, Nash–Sutcliffe efficiency (NSE) MEP provided a slightly better-fitted model improved when contrasted GEP, had 0.925, MAE 0.591%, RMSE 0.745%, NSE 0.923. water/binder conditions, content direct (positive) relationship concrete, while SP, slag an indirect (negative) association, according SHapley Additive exPlanations study.

Language: Английский

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Machine learning as alternative strategy for the numerical prediction of the shear response in reinforced and prestressed concrete beams

Results in Engineering, Journal Year: 2024, Volume and Issue: 22, P. 102139 - 102139

Published: April 17, 2024

Some materials, such as reinforced and prestressed concrete, involve non-linear constitutive relationships in elasticity problems defined on them. In particular, the shear strength of a concrete beam may be calculated by considering diagonal struts field context so-called "Compression Field Theories" (CFTs). This work presents an efficient Machine Learning method alternative to numerical methods for obtaining full response beams based CFT regarding stresses, strains, crack angles. For that, regression task is developed using state-of-the-art (ML) models. A ML model per output variable trained with existing Newton-Raphson solutions database. The solvability region embedded steel also considered, demonstrating comprehensive character proposed method. validated two real responses, where results obtained demonstrate that this algorithms effectively addresses problem prediction beams. approximation performs reasonably well without requiring any initial approximations. Moreover, regressor here shows low dependence tension stiffening area surrounding reinforcement, which significantly improves performance methodology higher number design cases. Thus, practical structural engineering, last approach establishes procedure mechanical models CFTs framework.

Language: Английский

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Estimating the mean cutting force of conical picks using random forest with salp swarm algorithm

Results in Engineering, Journal Year: 2023, Volume and Issue: 17, P. 100892 - 100892

Published: Jan. 13, 2023

Conical picks are widely used as cutting tools in shearers and roadheaders, the mean force (MCF) is one of important parameters affecting conical pick performance. As MCF depends on a number due to that existing empirical theoretical formulas numerical modelling not sufficient enough reliable predict proficient manner. So, this research, novel intelligent model based random forest algorithm (RF) heuristic called salp swarm (SSA) have been applied determine optimal hyper-parameters RF, root square error fitness function. A total 188 data samples including 50 rock types seven (tensile strength σt, compressive σc, cone angle θ, depth d, attack γ, rake α back-clearance β) were collected develop an SSA-RF for prediction. The prediction results compared with influential four classical models, such forest, extreme learning machine, support vector machine radial basis function neural network. absolute (MAE), (RMSE), percentage (MAPE) Pearson correlation coefficient (R2) employed evaluation indexes compare capability different predicting models. MAE (0.509 0.996), RMSE (0.882 1.165), MAPE (0.146 0.402) R2 (0.975 0.910) values between measured predicted training testing phases clearly demonstrate superiority other tools. sensitivity analysis has also performed understand influence each input parameter MCF, which indicates d σt most variables

Language: Английский

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The prediction and evaluation of recycled polypropylene fiber and aggregate incorporated foam concrete using Artificial Neural Networks

Construction and Building Materials, Journal Year: 2023, Volume and Issue: 411, P. 134646 - 134646

Published: Dec. 23, 2023

Language: Английский

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Ensemble machine learning models for predicting the CO2 footprint of GGBFS-based geopolymer concrete

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 472, P. 143463 - 143463

Published: Aug. 26, 2024

While geopolymer concrete (GPC) has gained popularity for its environmentally friendly attributes compared to ordinary Portland cement, the absence of a prediction model carbon footprint constituents presents challenges optimization within evolving industry.This study offers thorough CO 2 ground granulated blast-furnace slag (GGBFS)-based GPC, utilizing advanced AI techniques, including combination machine learning models and stacking ensembles.This research statistically examines crucial parameters responsible emissions in GGBFS-based GPC production, identifying factors like superplasticizer content, initial curing temperature, NaOH (dry) content as significant contributors.Emphasizing sustainability, advocates optimizing mixtures by considering ratio other activator materials.After rigorously evaluating 12 models, ensemble this identified M4-a Support Vector Regression (SVR) Neural Network (NN)-as weak Decision Tree (DT) meta-model, most effective predicting footprints.The choice M4 is supported various performance metrics such lowest Mean Squared Error 88.8 Root 9.42, alongside highest R , Adjusted Explained Variance scores, all approximately 0.95.Additional analyses, Euclidean distance Taylor diagrams, further substantiate selection M4.The findings have practical implications sustainable cleaner enabling businesses optimize GPC.

Language: Английский

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Machine Learning Prediction of Permeability Distribution in the X Field Malay Basin Using Elastic Properties

Results in Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 103421 - 103421

Published: Nov. 1, 2024

Language: Английский

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Comparative Analysis of Artificial Neural Networks Models for Predicting Mortar Properties with Diatomite Incorporation

Materials Chemistry and Physics, Journal Year: 2025, Volume and Issue: unknown, P. 130386 - 130386

Published: Jan. 1, 2025

Language: Английский

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Design of sustainable mortar incorporating construction and demolition waste through adaptive experiments accelerated by machine learning

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104264 - 104264

Published: Feb. 1, 2025

Language: Английский

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