Advancing waste-based construction materials through carbon dioxide curing: A comprehensive review DOI Creative Commons
Marsail Al Salaheen, Wesam Salah Alaloul, Khalid Mhmoud Alzubi

et al.

Results in Engineering, Journal Year: 2023, Volume and Issue: 20, P. 101591 - 101591

Published: Nov. 21, 2023

With an emphasis on solid waste-based construction materials, this study seeks to provide in-depth analysis of current advancements in CO2 curing processes for building materials. 715 publications were extracted from the Web Science and Scopus databases reviewed following systematic review guidelines integrated with bibliometric approach. The recent operational environmental benefits obtain characteristics optimal materials discussed. findings demonstrated that early-age densifies microstructure lowering porosity enhancing mechanical properties, impermeability, durability. Additionally, carbonation has potential enhance performance ash-based concretes as well physical recycled aggregates, hence promoting waste reutilization sector. Also, conducted studies revealed pre- post-curing conditions are critical chamber configuration. Moreover, exposure time, pressure concentration, all directly influenced material sequestration. More investigations related improving long-term products still required methods increasing rate.

Language: Английский

Machine learning for optimal design of circular hollow section stainless steel stub columns: A comparative analysis with Eurocode 3 predictions DOI Creative Commons
Ikram Abarkan, Musab Rabi, Felipe Piana Vendramell Ferreira

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 132, P. 107952 - 107952

Published: Feb. 15, 2024

Stainless steel has many advantages when used in structures, however, the initial cost is high. Hence, it essential to develop reliable and accurate design methods that can optimize material. As novel, soft computation methods, machine learning provided more predictions than analytical formulae solved highly complex problems. The present study aims models predict cross-section resistance of circular hollow section stainless stub column. A parametric conducted by varying diameter, thickness, length, mechanical properties This database train, validate, test models, Artificial Neural Network (ANN), Decision Trees for Regression (DTR), Gene Expression Programming (GEP) Support Vector Machine (SVMR). Thereafter, results are compared with finite element Eurocode 3 (EC3) assess their accuracy. It was concluded EC3 conservative an average Predicted-to-Actual ratio 0.698 Root Mean Square Error (RMSE) 437.3. presented highest level However, SVMR model based on RBF kernel a better performance ANN, GEP DTR RMSE value SVMR, 22.6, 31.6, 152.84 29.07, respectively. leads lowest accuracy among other three yet, EC3. were implemented user-friendly tool, which be purposes.

Language: Английский

Citations

13
Spatial analysis and predictive modeling of energy poverty: insights for policy implementation DOI
Sidique Gawusu, Seidu Abdulai Jamatutu, Xiao-Bing Zhang

et al.

Environment Development and Sustainability, Journal Year: 2024, Volume and Issue: unknown

Published: May 16, 2024

Language: Английский

Citations

12
Modeling the effect of implementation of artificial intelligence powered image analysis and pattern recognition algorithms in concrete industry DOI Creative Commons
Ahsan Waqar, Naraindas Bheel, Bassam A. Tayeh

et al.

Developments in the Built Environment, Journal Year: 2024, Volume and Issue: 17, P. 100349 - 100349

Published: Feb. 2, 2024

AI-powered image analysis and pattern recognition algorithms (IAPRA) are renowned for their capacity to identify concrete flaws, assess strength characteristics, anticipate the service life of concrete. However, its execution in a building is challenging due several unknown aspects. This research aims evaluate challenges encountered by IAPRA influence on industry's digital transformation success. We conducted quantitative methodology impediments success variables associated with picture algorithms. Structural Equation Modeling (SEM) tests were determine critical hurdles Three reliable valid formative constructs identified: complexity privacy, economic legal, technology integration. The developed model revealed significance three reflecting constructs: quality control, predictive maintenance, enhanced productivity. practical implications include, addressing identified related crucial transformation. By prioritizing productivity, stakeholders can optimize processes outcomes. major limitation this study reliance approach, which inherently restricts data collection specific features sample under investigation.

Language: Английский

Citations

11
Development of integrative data intelligence models for thermo-economic performances prediction of hybrid organic rankine plants DOI
Tao Hai, Omer A. Alawi, Haslinda Mohamed Kamar

et al.

Energy, Journal Year: 2024, Volume and Issue: 292, P. 130503 - 130503

Published: Feb. 1, 2024

Language: Английский

Citations

9
State-of-the-art review of geopolymer concrete carbonation: From impact analysis to model establishment DOI Creative Commons
Cheng Zhao, Ziqing Li,

Shuangdi Peng

et al.

Case Studies in Construction Materials, Journal Year: 2024, Volume and Issue: 20, P. e03124 - e03124

Published: April 3, 2024

Geopolymer concrete (GPC) is a relatively new, innovative and sustainable green civil engineering material, which has many advantages similar to ordinary Portland Cement (OPC). Through the investigations analyses of published literature, this review paper summarizes state-of-the-art research progress with respect carbonization performances GPC from following aspects. First all, advantages, mechanism, identification methods are introduced. Second, properties between OPC compared, as well influences different factors on performance analyzed, such admixtures, additives, environment, etc. Finally, series models evaluate degree listed, future prospect also put forward. Based literature summaries, existing researchers still have great controversy about results anti-carbonization geopolymer gelling materials, mainly because diversity raw materials in system, resulting large differences hydration products microstructure various pulps, so there their anti-carbon performance. It significant that influential will be importance determine GPC, can extended filling effect nanoparticles, changing proportion silicon aluminum substances, improving mechanical durability GPC. In addition, more types numerical for should established, considering impact factors, ensure applicability accuracy, integration needs strengthened serve complex practices. Overall, model establishment contribute analyzing provide some clues prolonging its life.

Language: Английский

Citations

9
Emerging Harris Hawks Optimization based load demand forecasting and optimal sizing of stand-alone hybrid renewable energy systems– A case study of Kano and Abuja, Nigeria DOI Creative Commons
Sani I. Abba,

Bara’u Gafai Najashi,

Abdulazeez Rotimi

et al.

Results in Engineering, Journal Year: 2021, Volume and Issue: 12, P. 100260 - 100260

Published: July 30, 2021

This paper presents load forecasting and optimal sizing for minimizing the Annualized Cost of System (ACS) a stand-alone photovoltaic (PV)/wind/battery hybrid renewable energy system. To achieve forecasting, Support Vector Regression (SVR) was integrated with emerging Harris Hawks Optimization (HHO) Particle Swarm (PSO) algorithms to form two SVR (SVR-HHO SVR-PSO). The single obtained were used predict demand variability remote areas in Kano Abuja, Nigeria. For sizing, PSO algorithm used. prediction accuracy evaluated using Correlation Coefficient (R), Determination (R2), Mean Square Error (MSE), Root (RMSE). results show that both outperformed terms accuracy. Furthermore, SVR-HHO has highest goodness fit lowest error. Besides, proved merit over SVR-PSO despite its reliability. These concluded metaheuristic are more promising hence can serve as reliable tool decision making.

Language: Английский

Citations

46
New control strategy for multifunctional grid-connected photovoltaic systems DOI Creative Commons

Ricsa Alhassane Soumana,

Michael Juma Saulo,

Christopher Maina Muriithi

et al.

Results in Engineering, Journal Year: 2022, Volume and Issue: 14, P. 100422 - 100422

Published: April 21, 2022

The main aim of this work consists proposing a new control strategy for multifunctional grid-connected photovoltaic systems (GCPVSs) to enhance the power quality at point common coupling (PCC) while considering inverter-rated capacity. In addition, an Adaptive neuro-fuzzy inference system (ANFIS) based maximum tracking (MPPT) controller two-phase interleaved boost converter is proposed improve dc-link voltage oscillation GCPVS. takes into account inverter's rated capacity in terms power, which defined by its maximal current modulus. It limits inverter prevent overrating operations, and it also manages GCPVS's functions: active injection, reactive compensation, harmonic filtering. Active injection grid precedence over enhancement. Then, compensation priority filtering nonlinear load harmonics. applied PV through three-level neutral clamped (NPC) inverter. Various scenarios with different solar irradiation levels are investigated using MATLAB/Simulink environment. Compared another existing total distortion (THD) enhancement, simulations results indicate superiority method. Furthermore, simulation show that GCPVS can perfectly perform all functions simultaneously up 16.95% reduction THD.

Language: Английский

Citations

38
Prediction of concrete porosity using machine learning DOI Creative Commons
Chong Cao

Results in Engineering, Journal Year: 2022, Volume and Issue: 17, P. 100794 - 100794

Published: Dec. 5, 2022

Porosity is an important indicator of the durability performance concrete. The objective this study to apply machine learning methods empirically predict porosity high-performance concrete containing supplementary cementitious materials. assembled database for consists 240 data records, featuring 74 unique mixture designs. compositional features include water/cement ratio, fly ash, slag, aggregate content, superplasticizers and curing conditions. numerical results suggest that gradient boosting trees outperform random forests in terms their prediction accuracy. XGBoost achieves best with additional regularization over model complexity prevent overfitting. Compared conventional chemo-mechanical predicting porosity, proposed data-driven approach not only overcomes difficulty estimating time-dependent degree hydration, but also a higher accuracy R2 = 0.9770, MAPE 2.97%, RMSE 0.431 (%). predictor importance plot shows days, water/binder content are most predictors porosity.

Language: Английский

Citations

36
Prediction and uncertainty quantification of ultimate bond strength between UHPC and reinforcing steel bar using a hybrid machine learning approach DOI
Abdulwarith Ibrahim Bibi Farouk, Jinsong Zhu,

Jingnan Ding

et al.

Construction and Building Materials, Journal Year: 2022, Volume and Issue: 345, P. 128360 - 128360

Published: July 9, 2022

Language: Английский

Citations

33
Mapping of groundwater salinization and modelling using meta-heuristic algorithms for the coastal aquifer of eastern Saudi Arabia DOI
Sani I. Abba, Mohammed Benaafi, A. G. Usman

et al.

The Science of The Total Environment, Journal Year: 2022, Volume and Issue: 858, P. 159697 - 159697

Published: Nov. 2, 2022

Language: Английский

Citations

32