Machine learning and RSM-CCD analysis of green concrete made from waste water plastic bottle caps: Towards performance and optimization DOI
Mohammed Nayeemuddin, Andi Asiz, Mohammad Ali Khasawneh

et al.

Mechanics of Advanced Materials and Structures, Journal Year: 2023, Volume and Issue: 31(25), P. 6829 - 6837

Published: Aug. 7, 2023

AbstractThis study aims to serve as a performance indicator for the workability and strength of concrete when coarse aggregate, sand, cement, water are partially substituted with waste plastic bottle caps. The significance these alternative caps is reduce trash that difficult lapse prevent can be transformed something may employed in advancement technology future. principle "Reduce, Reuse, Recycle" used, which not only lowers environmental pollution but also reduces costs. In building industry, most often utilized material. order preserve natural resources minimize number materials end up landfills, green construction becoming more significant worldwide issue. Empty cans tops from drinking produce lot garbage. difficulty biodegrading need techniques recycling or reuse make this problem environment. Such problems being investigated determine whether it could possible replace aggregate 0, 6 12% manufacturing using discarded To evaluate compressive strength, split tensile, flexural test characteristics laboratory setting; were used replacements at different percentages. highest was determined 28.80 MPa replacement cement ratio 0.55. Advanced statistical methods, including RSM-CCD (Response Surface Method-Central Composite Design) machine learning models ANN-LM (Artificial Neural Network- Levenberg Marquardt), applied predict performances based on mix design variations. It found model displayed accurate prediction relative method.Keywords: Sustainable concreteplastic capscompressive strengthmachine learningartificial neural networkcentral composite AcknowledgementsAssistance testing PMU Civil Engineering Lab Technician, Mr. Rusty De Leon, greatly appreciated.Disclosure statementNo potential conflict interest reported by author(s).

Language: Английский

Compressive strength prediction of sustainable concrete incorporating rice husk ash (RHA) using hybrid machine learning algorithms and parametric analyses DOI Creative Commons
Abul Kashem, Rezaul Karim,

Pobithra Das

et al.

Case Studies in Construction Materials, Journal Year: 2024, Volume and Issue: 20, P. e03030 - e03030

Published: March 5, 2024

The construction industry is making efforts to reduce the environmental impact of cement production in concrete by incorporating alternative and supplementary cementitious materials, as well lowering carbon emissions. One such material that has gained popularity this context rice husk ash (RHA) due its pozzolanic reactions. This study aims forecast compressive strength (CS) RHA-based (RBC) examining effects several factors cement, RHA content, curing age, water usage, aggregate amount, superplasticizer content. To accomplish this, collected analyzed data from literature, resulting a dataset 1404 observations. Several machine learning (ML) models, light gradient boosting (LGB), extreme (XGB), random forest (RF), hybrid (HML) approaches like XGB-LGB XGB-RF were employed thoroughly analyze these parameters assess their on strength. was split into training testing groups, statistical analyses performed determine relationships between input CS. Moreover, performance all models evaluated using various evaluation criteria, including mean absolute percentage error (MAPE), coefficient efficiency (CE), root square (RMSE), determination (R2). model found have higher precision (R2 = 0.95, RMSE 5.255 MPa) compared other models. SHAP (SHapley Additive exPlanations) analysis revealed RHA, had positive effect Overall, study's findings suggest with identified can be used accurately predict CS RBC. application technologies sector facilitate rapid low-cost identification qualities parameters.

Language: Английский

Citations

39
Developing hybrid ELM-ALO, ELM-LSO and ELM-SOA models for predicting advance rate of TBM DOI
Chuanqi Li, Jian Zhou, Ming Tao

et al.

Transportation Geotechnics, Journal Year: 2022, Volume and Issue: 36, P. 100819 - 100819

Published: July 21, 2022

Language: Английский

Citations

61
COSMA-RF: New intelligent model based on chaos optimized slime mould algorithm and random forest for estimating the peak cutting force of conical picks DOI
Jian Zhou, Yong Dai, Kun Du

et al.

Transportation Geotechnics, Journal Year: 2022, Volume and Issue: 36, P. 100806 - 100806

Published: July 8, 2022

Language: Английский

Citations

42
Application of Six Metaheuristic Optimization Algorithms and Random Forest in the uniaxial compressive strength of rock prediction DOI Creative Commons
Jingze Li, Chuanqi Li,

Shaohe Zhang

et al.

Applied Soft Computing, Journal Year: 2022, Volume and Issue: 131, P. 109729 - 109729

Published: Oct. 20, 2022

Language: Английский

Citations

42
Stability prediction of hard rock pillar using support vector machine optimized by three metaheuristic algorithms DOI Creative Commons
Chuanqi Li, Jian Zhou, Kun Du

et al.

International Journal of Mining Science and Technology, Journal Year: 2023, Volume and Issue: 33(8), P. 1019 - 1036

Published: July 18, 2023

Hard rock pillar is one of the important structures in engineering design and excavation underground mines. Accurate convenient prediction stability great significance for space safety. This paper aims to develop hybrid support vector machine (SVM) models improved by three metaheuristic algorithms known as grey wolf optimizer (GWO), whale optimization algorithm (WOA) sparrow search (SSA) predicting hard stability. An integrated dataset containing 306 pillars was established generate SVM models. Five parameters including height, width, ratio width uniaxial compressive strength stress were set input parameters. Two global indices, local indices receiver operating characteristic (ROC) curve with area under ROC (AUC) utilized evaluate all models' performance. The results confirmed that SSA-SVM model best highest values indices. Nevertheless, performance unstable (AUC: 0.899) not good those stable 0.975) failed 0.990). To verify effectiveness proposed models, 5 field cases investigated a metal mine other collected from several published works. validation indicated obtained considerable accuracy, which means combination feasible approach predict

Language: Английский

Citations

41
LGBM-based modeling scenarios to compressive strength of recycled aggregate concrete with SHAP analysis DOI
Bin Xi, Enming Li,

Yewuhalashet Fissha

et al.

Mechanics of Advanced Materials and Structures, Journal Year: 2023, Volume and Issue: 31(23), P. 5999 - 6014

Published: June 22, 2023

Concrete production contributes significantly to global greenhouse gas emissions, and its manufacture requires substantial natural resources. These concerns can be partly mitigated by recycling construction demolition waste as aggregates produce Recycled Aggregate (RAC). RAC has gained momentum due lower environmental impact, costs, increased sustainability. The aim of this study was advance the reasonable use recycled aggregate in concrete achieve optimal mixture ratio design. Four advanced machine learning algorithms, Support Vector Machine (SVR), Light Gradient Boosting (LGBM), Random Forest (RF), Multi-Layer Perceptron (MLP), were employed, novel optimization biogeography-based (BBO), Multi-Verse Optimizer (MVO) Gravitational Search Algorithm (GSA), integrated predict compressive strength RAC. Six potential influential factors for considered models. employed four evaluation metrics, Taylor diagrams Regression Error Characteristic plots compare model performance. result shows LGBM-based hybrid outperformed other methods, demonstrating high accuracy predicting strength. Shapley Additive Explanation (SHAP) results emphasize importance understanding interactions between various their effects on mechanical properties findings inform development more sustainable environmentally friendly building materials.

Language: Английский

Citations

32
Investigation on the rheology, self-shrinkage, pore structure, and fractal dimension of coral powder-cement slurry DOI
Qinglong Qin, Qingshan Meng,

Panpan Yi

et al.

Journal of Building Engineering, Journal Year: 2023, Volume and Issue: 77, P. 107517 - 107517

Published: Aug. 6, 2023

Language: Английский

Citations

25
Application of the Improved POA-RF Model in Predicting the Strength and Energy Absorption Property of a Novel Aseismic Rubber-Concrete Material DOI Open Access
Xiancheng Mei, Zhen Cui, Qian Sheng

et al.

Materials, Journal Year: 2023, Volume and Issue: 16(3), P. 1286 - 1286

Published: Feb. 2, 2023

The application of aseismic materials in foundation engineering structures is an inevitable trend and research hotspot earthquake resistance, especially tunnel engineering. In this study, the pelican optimization algorithm (POA) improved using Latin hypercube sampling (LHS) method Chaotic mapping (CM) to optimize random forest (RF) model for predicting performance a novel rubber-concrete material. Seventy uniaxial compression tests seventy impact were conducted quantify material performance, i.e., strength energy absorption properties four other artificial intelligence models generated compare predictive with proposed hybrid RF models. evaluation results showed that LHSPOA-RF has best prediction among all property concrete both training testing phases (R2: 0.9800 0.9108, VAF: 98.0005% 91.0880%, RMSE: 0.7057 1.9128, MAE: 0.4461 0.7364; R2: 0.9857 0.9065, 98.5909% 91.3652%, 0.5781 1.8814, 0.4233 0.9913). addition, sensitive analysis indicated rubber cement are most important parameters properties, respectively. Accordingly, POA-RF not only proven as effective predict materials, but also provides new idea assessing performances field

Language: Английский

Citations

24
Prediction of durability of reinforced concrete based on hybrid-Bp neural network DOI
Qiong Feng,

Xiaoyang Xie,

Penghui Wang

et al.

Construction and Building Materials, Journal Year: 2024, Volume and Issue: 425, P. 136091 - 136091

Published: April 1, 2024

Language: Английский

Citations

14
Quantifying compressive strength in limestone powder incorporated concrete with incorporating various machine learning algorithms with SHAP analysis DOI

Mihir Mishra

Asian Journal of Civil Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 14, 2024

Language: Английский

Citations

11