An AI-driven approach for modeling the compressive strength of sustainable concrete incorporating waste marble as an industrial by-product DOI Creative Commons
Ramin Kazemi, Seyedali Mirjalili

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Nov. 5, 2024

Abstract A key goal of environmental policies and circular economy strategies in the construction sector is to convert demolition industrial wastes into reusable materials. As an by-product, Waste marble (WM), has potential replace cement fine aggregate concrete which helps with saving natural resources reducing harm. While many studies have so far investigated effect WM on compressive strength (CS), it undeniable that conducting experimental activities requires time, money, re-testing changing materials conditions. Hence, this study seeks move from traditional approaches towards artificial intelligence-driven by developing three models—artificial neural network (ANN) hybrid ANN ant colony optimization (ACO) biogeography-based (BBO) predict CS concrete. For purpose, a comprehensive dataset including 1135 data records employed literature. The models’ performance assessed using statistical metrics error histograms, K -fold cross-validation analysis applied avoid overfitting problems, emphasize reliable predictive capabilities, generalize them. indicated ANN-BBO model performed best correlation coefficient (R) 0.9950 root mean squared (RMSE) 1.2017 MPa. Besides, distribution results revealed outperformed ANN-ACO narrower range errors 98% predicted points training phase experienced [-10%, 10%], whereas for models, percentage was 85% 79%, respectively. Additionally, SHapley Additive exPlanations (SHAP) clarify impact input variables prediction accuracy found specimen’s age most influential variable. Eventually, validate ANN-BBO, comparison previous studies’ models.

Language: Английский

Forecasting the strength of graphene nanoparticles-reinforced cementitious composites using ensemble learning algorithms DOI Creative Commons
Majid Khan, Roz‐Ud‐Din Nassar,

Waqar Anwar

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: 21, P. 101837 - 101837

Published: Feb. 6, 2024

Contemporary infrastructure requires structural elements with enhanced mechanical strength and durability. Integrating nanomaterials into concrete is a promising solution to improve However, the intricacies of such nanoscale cementitious composites are highly complex. Traditional regression models encounter limitations in capturing these intricate compositions provide accurate reliable estimations. This study focuses on developing robust prediction for compressive (CS) graphene nanoparticle-reinforced (GrNCC) through machine learning (ML) algorithms. Three ML models, bagging regressor (BR), decision tree (DT), AdaBoost (AR), were employed predict CS based comprehensive dataset 172 experimental values. Seven input parameters, including graphite nanoparticle (GrN) diameter, water-to-cement ratio (wc), GrN content (GC), ultrasonication (US), sand (SC), curing age (CA), thickness (GT), considered. The trained 70 % data, remaining 30 data was used testing models. Statistical metrics as mean absolute error (MAE), root square (RMSE) correlation coefficient (R) assess predictive accuracy DT AR demonstrated exceptional accuracy, yielding high coefficients 0.983 0.979 training, 0.873 0.822 testing, respectively. Shapley Additive exPlanation (SHAP) analysis highlighted influential role positively impacting CS, while an increased (w/c) negatively affected CS. showcases efficacy techniques accurately predicting nanoparticle-modified concrete, offering swift cost-effective approach assessing nanomaterial impact reducing reliance time-consuming expensive experiments.

Language: Английский

Citations

28
Utilization of all components of waste concrete: Recycled aggregate strengthening, recycled fine powder activity, composite recycled concrete and life cycle assessment DOI
Chao‐qiang Wang,

Lin-xiao Cheng,

Ying Yan

et al.

Journal of Building Engineering, Journal Year: 2023, Volume and Issue: 82, P. 108255 - 108255

Published: Dec. 4, 2023

Language: Английский

Citations

33
Machine and Deep Learning Methods for Concrete Strength Prediction: A Bibliometric and Content Analysis Review of Research Trends and Future Directions DOI
Raman Kumar, Essam Althaqafi, S. Gopal Krishna Patro

et al.

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 164, P. 111956 - 111956

Published: July 8, 2024

Language: Английский

Citations

14
Thermal and acoustic performance in textile fibre-reinforced concrete: An analytical review DOI Creative Commons
K.A.P. Wijesinghe, Chamila Gunasekara, David W. Law

et al.

Construction and Building Materials, Journal Year: 2024, Volume and Issue: 412, P. 134879 - 134879

Published: Jan. 1, 2024

Textile fibre-reinforced concrete based reviews have explored various engineering properties, such as strengthening of concrete, enhancing strain capacity, crack control, durability, and energy absorption. An essential missing component is a comprehensive analysis the thermal acoustic insulation performance textile concrete. The paper provides large-scale analytical database by analysing prior literature on It further microstructural pore-structural aspects to provide an overview underlying mechanisms driving these properties. This review explores impact fibre inclusion from 0–20 mass percentage (wt%) 0–40 volume (v%). key findings are that jute mortar demonstrated superior conductivity, achieving 0.068 W/mK at 20 wt% inclusion, followed 0.08 basalt fibres v% demonstrating possess commendable qualities. Notably, 30 2–4 mm miscanthus in showed outstanding dual performance, optimal conductivity 0.09 90% absorption 841 Hz. Finally, study suggests directions address identified gaps can be utilised design future research focusing end-user applications.

Language: Английский

Citations

11
Proposing Optimized Random Forest Models for Predicting Compressive Strength of Geopolymer Composites DOI Creative Commons
Bin Feng, Shahab Hosseini, Jie Chen

et al.

Infrastructures, Journal Year: 2024, Volume and Issue: 9(10), P. 181 - 181

Published: Oct. 9, 2024

This paper explores advanced machine learning approaches to enhance the prediction accuracy of compressive strength (CoS) in geopolymer composites (GePC). Geopolymers, as sustainable alternatives Ordinary Portland Cement (OPC), offer significant environmental benefits by utilizing industrial by-products such fly ash and ground granulated blast furnace slag (GGBS). The accurate their is crucial for optimizing mix design reducing experimental efforts. We present a comparative analysis two hybrid models, Harris Hawks Optimization with Random Forest (HHO-RF) Sine Cosine Algorithm (SCA-RF), against traditional regression methods classical models like Extreme Learning Machine (ELM), General Regression Neural Network (GRNN), Radial Basis Function (RBF). Using comprehensive dataset derived from various scientific publications, we focus on key input variables including fine aggregate, GGBS, ash, sodium hydroxide (NaOH) molarity, others. Our results indicate that SCA-RF model achieved superior performance root mean square error (RMSE) 1.562 coefficient determination (R2) 0.987, compared HHO-RF model, which obtained an RMSE 1.742 R2 0.982. Both significantly outperformed methods, demonstrating higher reliability predicting GePC. research underscores potential advancing construction materials through precise predictive modeling, paving way more environmentally friendly efficient practices.

Language: Английский

Citations

7
Compressive strength of waste-derived cementitious composites using machine learning DOI Creative Commons
Qiong Tian, Yijun Lü, Ji Zhou

et al.

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

Published: Jan. 1, 2024

Abstract Marble cement (MC) is a new binding material for concrete, and the strength assessment of resulting materials subject this investigation. MC was tested in combination with rice husk ash (RHA) fly (FA) to uncover its full potential. Machine learning (ML) algorithms can help formulation better MC-based concrete. ML models that could predict compressive (CS) concrete contained FA RHA were built. Gene expression programming (GEP) multi-expression (MEP) used build these models. Additionally, evaluated by calculating R 2 values, carrying out statistical tests, creating Taylor’s diagram, comparing theoretical experimental readings. When MEP GEP models, yielded slightly better-fitted model prediction performance ( = 0.96, mean absolute error 0.646, root square 0.900, Nash–Sutcliffe efficiency 0.960). According sensitivity analysis, CS most affected curing age content, then contents. Incorporating waste such as marble powder, RHA, into building reduce environmental impacts encourage sustainable development.

Language: Английский

Citations

6
Thermal and acoustic performance of solid waste incorporated cement based composites: an analytical review DOI Creative Commons
K.A.P. Wijesinghe,

Gamini Lanarolle,

Chamila Gunasekara

et al.

Archives of Civil and Mechanical Engineering, Journal Year: 2025, Volume and Issue: 25(2)

Published: March 4, 2025

Language: Английский

Citations

0
Evaluating the impact of waste marble on the compressive strength of traditional concrete using machine learning DOI Creative Commons
Kennedy C. Onyelowe, Viroon Kamchoom‬, Ahmed M. Ebid

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 18, 2025

Waste marble, an industrial byproduct generated from marble cutting and polishing processes, can be effectively utilized as a partial replacement in concrete mixtures. Incorporating waste not only addresses environmental concerns related to disposal but also contributes the sustainability of construction materials. Using machine learning (ML) predict impact on compressive strength traditional offers several advantages over repeated laboratory experiments. ML powerful alternative costly time-consuming experiments, enabling faster more sustainable exploration potential improving concrete's strength. This research has focused evaluating using (ML). Advanced techniques such Group Methods Data Handling Neural Network (GMDH-NN), Support Vector Regression (SVR), K-Nearest Neighbors (kNN) Adaptive Boosting (AdaBoost) have been applied this work. The GMDH-NN model was created GMDH Shell 3.0 software, while AdaBoost, SVR kNN models were "Orange Mining" software version 3.36. Error indices sum squared error (SSE), mean absolute (MAE), (MSE), root (RMSE), (%), performance metrics Accuracy % R2 between predicted calculated parameters used evaluate overall behavior models. Finally, Hoffman sensitivity analysis procedure determine individual relative input variables output. At end total 1135 entries collected containing constituents cement density (C), (WM), fine aggregate (FAg), coarse (CAg), water (W), superplasticizer (PL) curing age (Age) model. records divided into training set (900 = 80%) validation (235 20%) following standard partitioning pattern reported literature. with SSE 1408.5 MPa2 1397 respectively tie 95.5% 0.985 showed best suggesting excellent worst. Conversely, RF balances accuracy complexity, making it practical AdaBoost. And lastly, Age, Coarse Aggregates, Water, Plasticizer play most significant roles determining strength, Cement, Marble, Fine Aggregates comparatively smaller impacts. However, considering proportion required for powder replace cement, remarkable influence thus recommended its cement.

Language: Английский

Citations

0
Numerical optimization of conformal cooling channels for thermal distribution and stress characterization in high pressure die casting die DOI
Xin Bo He, Xiaoming Wang,

Corey Vian

et al.

Engineering Failure Analysis, Journal Year: 2025, Volume and Issue: unknown, P. 109620 - 109620

Published: April 1, 2025

Language: Английский

Citations

0
Towards Sustainable Construction: Harnessing Potential of Pumice Powder for Eco-Friendly Concrete, Augmented by Hybrid Fiber Integration to Elevate Concrete Performance DOI Creative Commons
Umar Farooq, Muhammad Shahid Rizwan, Wasim Khaliq

et al.

Case Studies in Construction Materials, Journal Year: 2024, Volume and Issue: 21, P. e03815 - e03815

Published: Oct. 6, 2024

Language: Английский

Citations

3