Efficacy of sustainable cementitious materials on concrete porosity for enhancing the durability of building materials DOI Creative Commons

HaoYang Huang,

Muhammad Nasir Amin, Suleman Ayub Khan

et al.

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

Published: Jan. 1, 2024

Abstract The degradation of concrete structures is significantly influenced by water penetration since serves as the primary vehicle for movement harmful compounds. process capillary absorption widely recognized a crucial indicator durability unsaturated concrete, it allows dangerous substances to enter composite material. capacity intricately linked its pore structure, inherently porous. main goal this work create an innovative predictive tool that assesses porosity analyzing components using machine-learning (ML) framework. Seven distinct batch design variables were included in generated database: fly ash, superplasticizer, water-to-binder ratio, curing time, ground granulated blast furnace slag, binder, and coarse-to-fine aggregate ratio. Four distant ML algorithms, including AdaBoost, linear regression (LR), decision tree (DT), support vector machine (SVM), are utilized infer generalization capabilities algorithms estimate accurately. RReliefF algorithm was implemented calculate significant features influencing porosity. This study concludes comparison alternative techniques, AdaBoost method demonstrated superior performance with R 2 score 0.914, followed SVM (0.870), DT (0.838), LR (0.763). results evaluation indicated binder possesses remarkable influence on concrete.

Language: Английский

Predicting the crack repair rate of self-healing concrete using soft-computing tools DOI

Yuanfeng Lou,

Huiling Wang, Muhammad Nasir Amin

et al.

Materials Today Communications, Journal Year: 2024, Volume and Issue: 38, P. 108043 - 108043

Published: Jan. 5, 2024

Language: Английский

Citations

17
Strength Reduction Due to Acid Attack in Cement Mortar Containing Waste Eggshell and Glass: A Machine Learning-Based Modeling Study DOI Creative Commons
Fei Zhu, Xiangping Wu, Yijun Lü

et al.

Buildings, Journal Year: 2024, Volume and Issue: 14(1), P. 225 - 225

Published: Jan. 14, 2024

The present study utilized machine learning (ML) techniques to investigate the effects of eggshell powder (ESP) and recycled glass (RGP) on cement composites subjected an acidic setting. A dataset acquired from published literature was employed develop learning-based predictive models for mortar’s compressive strength (CS) decrease. Artificial neural network (ANN), K-nearest neighbor (KNN), linear regression (LR) were chosen modeling. Also, RreliefF analysis performed relevance variables. total 234 data points train/test ML algorithms. Cement, sand, water, silica fume, superplasticizer, powder, 90 days CS considered as input outcomes research showed that could be applied evaluate reduction percentage in composites, including ESP RGP, after being exposed acid. Based R2 values (0.87 ANN, 0.81 KNN, 0.78 LR), well assessment variation between test anticipated errors (1.32% 1.57% 1.69% it determined accuracy ANN model superior KNN LR. sieve diagram exhibited a correlation amongst predicted target results. suggested RGP significantly influenced loss samples with scores 0.26 0.21, respectively. research, approach suitable predicting mortar environments, thereby eliminating lab testing trails.

Language: Английский

Citations

15
Analyzing chloride diffusion for durability predictions of concrete using contemporary machine learning strategies DOI
Huiping Zhang, Xiaochao Li, Muhammad Nasir Amin

et al.

Materials Today Communications, Journal Year: 2024, Volume and Issue: 38, P. 108543 - 108543

Published: March 1, 2024

Language: Английский

Citations

12
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
Investigating the effectiveness of carbon nanotubes for the compressive strength of concrete using AI-aided tools DOI Creative Commons

Han Sun,

Muhammad Nasir Amin,

Muhammad Tahir Qadir

et al.

Case Studies in Construction Materials, Journal Year: 2024, Volume and Issue: 20, P. e03083 - e03083

Published: March 28, 2024

Sustainable development in the building industry can be achieved through use of versatile cementitious composites. Thus, incorporating nanoparticles into cement composites create materials with enhanced performance and numerous applications. The utilization carbon nanotubes (CNTs) construction has great promise for developing efficient solutions to establish a sustainable ecosystem diverse characteristics. However, forecasting characteristics these is significant challenge due their intricate composite structure nonlinear behavior. Designing conducting laboratory experiments on samples across multiple age groups challenging, time-consuming, costly. Moreover, there presently lack model that predict concrete's compressive strength (fc') nanoparticles. Three machine learning (ML) techniques, K-nearest neighbor (KNN), linear regression (LR), artificial neural network (ANN), were used fc' nanocomposites containing CNTs this research. A thorough database consisting 282 data entities CNTs-based concrete model's reliability was assessed using R2 test statistical error analysis. ANN had most outstanding value 0.885, while KNN LR models values 0.838 0.744, respectively. RReliefF analysis utilized evaluate primary components predicting outcomes. This research shows properties CNT-based are greatly affected by water-to-binder ratio, followed proportions coarse aggregates. ML algorithms exhibited superior generalization capabilities, suggesting potential accurate predictions properties.

Language: Английский

Citations

8
Adsorption optimization and modeling of Hg2+ ions from aqueous solutions using response surface methodology by SNPs–CS bionanocomposite produced from rice husk agro–industrial waste as a novel environmentally–friendly bionanoadsorbent DOI
Soran Kamari, Afsaneh Shahbazi, Farshid Ghorbani

et al.

Chemosphere, Journal Year: 2024, Volume and Issue: 351, P. 141279 - 141279

Published: Jan. 22, 2024

Language: Английский

Citations

6
Towards improved flexural behavior of plastic-based mortars: An experimental and modeling study on waste material incorporation DOI
Yingjie Li, Genhui Wang, Muhammad Nasir Amin

et al.

Materials Today Communications, Journal Year: 2024, Volume and Issue: 40, P. 109391 - 109391

Published: May 31, 2024

Language: Английский

Citations

5
Machine learning approaches to predict the strength of graphene nanoplatelets concrete: Optimization and hyper tuning with graphical user interface DOI
Turki S. Alahmari, Kiran Arif

Materials Today Communications, Journal Year: 2024, Volume and Issue: 40, P. 109946 - 109946

Published: July 26, 2024

Language: Английский

Citations

4
A systematic review of metakaolin-based alkali-activated and geopolymer concrete: A step toward green concrete DOI Creative Commons
Diyar N. Qader,

A. W. Mohd Jamil,

Alireza Bahrami

et al.

REVIEWS ON ADVANCED MATERIALS SCIENCE, Journal Year: 2025, Volume and Issue: 64(1)

Published: Jan. 1, 2025

Abstract Expanding the world’s infrastructure drives up demand for building materials, particularly ordinary Portland cement (OPC) concrete, whose high carbon dioxide (CO 2 ) emissions have a detrimental effect on environment. To address this issue, researchers looked into employing alternative supplementary cementitious materials (SCMs), including metakaolin (MK), which is derived from calcined kaolin clay with pozzolanic properties, to partially or completely replace OPC in concrete. This review article examines MK’s application alkali-activated (AAMs) and OPC-based By interacting calcium hydroxide, MK functions as additive enhancing its mechanical qualities durability. The use of source material AAMs, newly developed class sustainable binders, also covered article. effects different combinations additional SCMs, fly ash (FA), ground granulated blast furnace slag (GGBFS), silica fume, rice husk ash, characteristics concrete both fresh hardened states, are compiled. majority articles considered study past decade, while some relevant 2014 earlier taken account. results showed that adding combination FA GGBFS has excellent synergistic microstructural development, activity, strength increases. In particular, MK–FA mix demonstrated most encouraging performance gains. Because large surface area, nano-MK helped achieve denser geopolymer structure improve properties. best curing temperatures MK-based geopolymers gain were found be between 40 80°C total 28 days. pointed out compressive geopolymerization process enhanced by increasing mass ratio Na SiO 3 NaOH concentration. Nevertheless, was hampered unnecessarily alkali concentrations. Moreover, increased replacing TiO GGBFS. combining other SCMs highlight potential solutions lowering environmental footprint buildings.

Language: Английский

Citations

0
Machine learning modelling for strength prediction and durability investigation of alkali activated binders with POFA and granite dust DOI

Mehar Sai Komaragiri,

Subhani Shaik, Kumar Gedela Santhosh

et al.

Asian Journal of Civil Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: March 27, 2025

Language: Английский

Citations

0