Solar desalination system for fresh water production performance estimation in net-zero energy consumption building: A comparative study on various machine learning models DOI Creative Commons

Ali Hussain Alhamami,

Emmanuel Falude,

Ahmed Osman Ibrahim

et al.

Water Science & Technology, Journal Year: 2024, Volume and Issue: 89(8), P. 2149 - 2163

Published: March 20, 2024

ABSTRACT This study employs diverse machine learning models, including classic artificial neural network (ANN), hybrid ANN and the imperialist competitive algorithm emotional (EANN), to predict crucial parameters such as fresh water production vapor temperatures. Evaluation metrics reveal integrated ANN-ICA model outperforms ANN, achieving a remarkable 20% reduction in mean squared error (MSE). The (EANN) demonstrates superior accuracy, attaining an impressive 99% coefficient of determination (R2) predicting freshwater comprehensive comparative analysis extends environmental assessments, displaying solar desalination system's compatibility with renewable energy sources. Results highlight potential for proposed system conserve resources reduce impact, substantial decrease total dissolved solids (TDS) from over 6,000 ppm below 50 ppm. findings underscore efficacy models optimizing solar-driven systems, providing valuable insights into their capabilities addressing scarcity challenges contributing global shift toward sustainable environmentally friendly methods.

Language: Английский

Sustainable off-grid residential heating and desalination: Integration of biomass boiler and solar energy with environmental impact analysis DOI
Jing Zhu, Tirumala Uday Kumar Nutakki, Pradeep Kumar Singh

et al.

Journal of Building Engineering, Journal Year: 2024, Volume and Issue: 87, P. 109035 - 109035

Published: March 11, 2024

Language: Английский

Citations

30
Optimizing the crashworthy behaviors of hybrid composite structures through Taguchi approach DOI
Mahmoud M. Awd Allah,

Mohamed Ibrahim Abd El Aal,

Marwa A. Abd El‐baky

et al.

Polymer Composites, Journal Year: 2024, Volume and Issue: 45(9), P. 7906 - 7917

Published: March 13, 2024

Abstract This paper calculates the crashworthiness capability of glass‐reinforced epoxy composites over wrapped polyvinyl chloride (PVC) circular tubes with a triggering mechanism in form cutouts. The intended were prepared by wet wrapping method; after which they subjected to quasi‐static axial compression. To compute indications, three design parameters, each at levels, used. parameters are hole diameter ( d ), hole's number n and position L ). Taguchi technique has been employed experiments (DOE) tactic obtain best parameters. With maximum specific energy absorbed (SEA) crashing force efficiency (CFE), optimal found. Furthermore, main effect, signal‐to‐noise ratio (S/N), as well analysis variance (ANOVA), have studied using commercial software programme MINITAB 18. A few accompanied L9 orthogonal array. According results, “N” largest impact on value SEA contribution percent 29.27%. While “d” influence CFE 67.29%. Lastly, tests for confirmation performed. verify predicted values light experimental results. optimized developed tube is 74.72%, higher than intact PVC 12.29% lower hybrid tube. However, optimum was also 48 29.82% tube, respectively. Highlights holes highest 29%. ideal specimen enhances 75% compared Hole most 67%. improves 48% related

Language: Английский

Citations

17
Introducing ANN-GP algorithm to estimate transient bending of the functionally graded graphene origami-enabled auxetic metamaterial structures DOI

Chunlei Lin,

Guangyong Pan,

Mohamed Abbas

et al.

Mechanics of Advanced Materials and Structures, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 20

Published: April 27, 2024

This article presents a new method called the artificial neural networks-genetic programming (ANNs-GP) algorithm, which effectively predicts bending behavior of functionally graded graphene origami-enabled auxetic metamaterial (FG-GORAM) structures under transient conditions. Functionally materials (FGMs) display spatial heterogeneity in their composition and microstructure, resulting distinctive mechanical characteristics that make them well-suited for wide range engineering applications. The objective this study is to create prediction model can accurately capture intricate FGM structures. To do this, researchers have used ANN-GP technique, combines ANNs with GP. ANN component acquires knowledge from dataset including actual or simulated data, while GP fine-tunes structure parameters network improve its ability accurate predictions. proposed algorithm strengths predict FG-GORAM robust efficient, allowing designers engineers optimize performance reliability these various effectiveness proved by comparing it experimental data. shows has potential be useful tool designing analyzing sophisticated

Language: Английский

Citations

14
Predicting the properties of concrete incorporating graphene nano platelets by experimental and machine learning approaches DOI Creative Commons
Rayed Alyousef, Roz‐Ud‐Din Nassar, Muhammad Fawad

et al.

Case Studies in Construction Materials, Journal Year: 2024, Volume and Issue: 20, P. e03018 - e03018

Published: March 2, 2024

Modern infrastructure requirements necessitate structural components with improved durability and strength properties. The incorporation of nanomaterials (NMs) into concrete emerges as a viable strategy to enhance both the concrete. Nevertheless, complexities inherent in these nanoscale cementitious composites are notably intricate. Traditional regression models face constraints comprehensively capturing intricate compositions. Thus, posing challenges delivering precise dependable estimations. Therefore, current study utilized three machine learning (ML) methods, including artificial neural network (ANN), gene expression programming (GEP), adaptive neuro-fuzzy inference system (ANFIS), conjunction experimental investigation effect integration graphene nanoplatelets (GNPs) on electrical resistivity (ER) compressive (CS) containing GNPs. Concrete GNPs demonstrated an fractional change (FCR) strength. measures depict that enhancement was notable at GNP concentrations 0.05% 0.1%, showcasing increases 13.23% 16.58%, respectively. Simultaneously, highest observed FCR reached -12.19% -13%, prediction efficacy proved be outstanding forecasting characteristics For CS, GEP, ANN, ANFIS impressive correlation coefficient (R) values 0.974, 0.963, 0.954, resistivity, exhibited high R-values 0.999, 0.995, 0.987, comparative analysis revealed GEP model delivered predictions for ER CS. mean absolute error (MAE) GEP-CS 14.51% reduction compared ANN-CS substantial 48.15% improvement over ANFIS-CS model. Similarly, displayed MAE 38.14% lower Moreover, GEP-ER 56.80% 82.47% Shapley Additive explanation (SHAP) provided curing age SHAP score. indicating its predominant contribution CS prediction. In predicting ER, content score, signifying estimation. This highlights ML's accuracy properties nanoplatelets, offering fast cost-effective alternative time-consuming experiments.

Language: Английский

Citations

12
Demand side management optimization and energy labeling of multi-purpose buildings DOI
Amir Hossein Heydari, Ramin Haghighi Khoshkhoo, Rahim Zahedi

et al.

Journal of Building Engineering, Journal Year: 2024, Volume and Issue: 88, P. 109143 - 109143

Published: March 28, 2024

Language: Английский

Citations

11
Introduction to Functional DOI
Devyani Thapliyal,

Sarojini Verma,

Kshitij Tewari

et al.

Published: Feb. 9, 2024

Functional capabilities, superhydrophobicity, and much more. They play a crucial role in enhancing the performance, durability, efficiency of materials, structures, products. This chapter aims to introduce fascinating world functional coatings. It serves as gateway understanding diverse range coating technologies, applications, advancements that have emerged recent years. By exploring innovative techniques, research institutes, organizations dedicated research, this sets stage for deeper exploration subsequent chapters book. The then delves into specific coatings, starting with anticorrosion explores protective mechanisms classifications these providing foundation their importance preventing material degradation extending lifespan structures. further discusses advances corrosion-resistant including different types, formulating principles, properties, shedding light on latest innovations field. continues its which become increasingly vital healthcare, food packaging, environmental settings. discussion encompasses current mechanisms, challenges, opportunities associated developing antimicrobial Additionally, self-healing superhydrophobic respective showcasing potential functionality various domains. also advanced characterization machine learning cutting-edge developments coatings provide comprehensive overview. emphasizes enabling performance evaluation. end chapter, readers will gained solid field significance, evolving landscape development. paves way chapters, where delve topics applications within realm

Language: Английский

Citations

10
Tensile, compressive, and fracture behavior of Habeshian chopped banana/epoxy core sandwich woven banana composite DOI
Kiran Shahapurkar, Sindhu Ramesh, Nik Nazri Nik Ghazali

et al.

Biomass Conversion and Biorefinery, Journal Year: 2024, Volume and Issue: 14(17), P. 21553 - 21564

Published: March 13, 2024

Language: Английский

Citations

9
Evaluation of fracture indices of warm mix asphalt (WMA) modified with nano-additive under pure shear and pure tear deformations DOI
Yanfeng Chen, Alireza Naseri, Ali Attari

et al.

Theoretical and Applied Fracture Mechanics, Journal Year: 2024, Volume and Issue: 132, P. 104471 - 104471

Published: May 14, 2024

Language: Английский

Citations

6
Transient bending analysis of the graphene nanoplatelets reinforced sandwich concrete building structure validated by machine learning algorithm DOI
Xia Zhou, Yu-Yuan Chen,

Mohamed Abbas

et al.

Mechanics of Advanced Materials and Structures, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 22

Published: May 17, 2024

This paper demonstrates a thorough examination of the bending behavior sandwich concrete building structures that are reinforced with graphene nanoplatelets (GPLs). The analysis is confirmed using machine learning technique. Sandwich have notable benefits in terms strength, longevity, and thermal insulation, making them well-suited for many applications. Integrating GPLs into matrix improves mechanical characteristics performance these structures, especially behavior. study utilizes technique to verify characterization temporary structure nanoplatelets. approach dataset consisting simulated data create prediction model can reliably estimate response under different loading situations. algorithm's effectiveness dependability optimizing design demonstrated through validation against results. provides engineers designers powerful tool. enhances comprehension use approaches analyzing designing sophisticated structural materials systems.

Language: Английский

Citations

6
Thermoelastic damping in micro/nano-plate vibrations: 3D modeling using modified couple stress theory and the Moore–Gibson–Thompson equation DOI
Khalid Mujasam Batoo,

Shaymaa Abed Hussein,

Ehab Essam Aziz

et al.

Mechanics of Time-Dependent Materials, Journal Year: 2024, Volume and Issue: 28(3), P. 1787 - 1813

Published: March 5, 2024

Language: Английский

Citations

5