Data Utilization and Partitioning for Machine Learning Applications in Civil Engineering

Sustainable civil infrastructures, Journal Year: 2024, Volume and Issue: unknown, P. 87 - 100

Published: Jan. 1, 2024

Language: Английский

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Advanced machine learning prediction of the unconfined compressive strength of geopolymer cement reconstituted granular sand for road and liner construction applications

Asian Journal of Civil Engineering, Journal Year: 2023, Volume and Issue: 25(1), P. 1027 - 1041

Published: July 20, 2023

Language: Английский

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The net-zero and sustainability potential of SCC development, production and flowability in concrete structures

International Journal of Low-Carbon Technologies, Journal Year: 2023, Volume and Issue: 18, P. 530 - 541

Published: Jan. 1, 2023

Abstract Climate action around the world has shifted to potential of global warming contribution from design and construction infrastructures, especially those in demand for concrete. Concrete production use have been identified as contributing >5% world’s greenhouse gas (GHG) emissions. The main aim this research work is critically study net-zero sustainability potentials that can leverage on development, flowability self-compacting concrete (SCC). Conventional made >50% ordinary cement, which contributes >7% GHG But 1988, a fluidized compacts under its self-weight, known SCC, was formed developed overcome need durability, skill manpower were dwindling Japan at time. This created pathway cement be replaced partially or totally by certain pozzolanic materials function viscosity-modifying admixture, high-water reducing agent microencapsulated phase-change mix. However, findings shown these alter there reduced yield stress moderate viscosity allowable internal friction based Bingham model, achieved same water–cement ratio. Fortunately, implication admixtures replacements cleaner and, such, CO2 emission associated with process.

Language: Английский

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The influence of fly ash and blast furnace slag on the compressive strength of high-performance concrete (HPC) for sustainable structures

Asian Journal of Civil Engineering, Journal Year: 2023, Volume and Issue: 25(1), P. 861 - 882

Published: July 26, 2023

Language: Английский

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Modeling the influence of bacteria concentration on the mechanical properties of self-healing concrete (SHC) for sustainable bio-concrete structures

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

Published: April 10, 2024

Abstract In this research paper, the intelligent learning abilities of gray wolf optimization (GWO), multi-verse (MVO), moth fly optimization, particle swarm (PSO), and whale algorithm (WOA) metaheuristic techniques response surface methodology (RSM) has been studied in prediction mechanical properties self-healing concrete. Bio-concrete technology stimulated by concentration bacteria utilized as a sustainable structural concrete for future built environment. This is due to recovery tendency structures after noticeable failures. However, it requires somewhat expensive exercise create medium growth needed ability. The method data gathering, analysis adopted propose parametric relationships between usage performance terms strength durability. makes cheaper design based on optimized mathematical models proposed from exercise. was tested using coefficient determination (R 2 ), root mean squared errors, absolute variance accounted error. At end protocol model evaluation, found that classified outclassed RSM their ability mimic human animal genetics mutation. Furthermore, can be finally remarked GWO other methods predicting slump (Sl) with R 0.998 0.989 train test, respectively, PSO rest flexural 0.937 respectively MVO others compressive 0.958 respectively.

Language: Английский

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Comparative Analysis of Machine Learning Models for Predicting the Compressive Strength of Ultra-High-Performance Steel Fiber Reinforced Concrete

Journal of Engineering Research, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

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The influence of nano-silica precursor on the compressive strength of mortar using Advanced Machine Learning for sustainable buildings

Asian Journal of Civil Engineering, Journal Year: 2023, Volume and Issue: 25(2), P. 1135 - 1148

Published: July 20, 2023

Language: Английский

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Tree-based machine learning models for predicting the bond strength in reinforced recycled aggregate concrete

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

Published: Sept. 2, 2024

Language: Английский

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Advanced Machine Learning Techniques for Predicting Concrete Compressive Strength

Infrastructures, Journal Year: 2025, Volume and Issue: 10(2), P. 26 - 26

Published: Jan. 21, 2025

Accurate estimation of concrete compressive strength is very important for the improvement mix design, quality assurance, and compliance with engineering specifications. Most empirical traditional models have failed to capture complex relationships inherent within varied constituents mixes. This paper develops a machine learning model prediction using design variables curing age from “Concrete Compressive Strength Dataset” obtained UCI Machine Learning Repository. After comprehensive data preprocessing feature engineering, various regression classification were trained evaluated, including gradient boosting, random forest, AdaBoost, k-nearest neighbors, linear regression, neural networks. The boosting regressor (GBR) achieved highest predictive accuracy an R2 value 0.94. Feature importance analysis showed that water–cement ratio are most crucial factors affecting strength. Advanced methods such as SHapley Additive exPlanations (SHAP) values partial dependence plots used attain deep insights about interaction view enhancing interpretability fostering trust in models. Results highlight potential improve aim sustainable construction through optimization material usage waste reduction. It recommended future research be undertaken expanding datasets, more features, richer enhance power.

Language: Английский

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Eco-sustainability analysis of precast-concrete utility poles manufacturing–A case study from Pakistan

Heliyon, Journal Year: 2023, Volume and Issue: 9(4), P. e14976 - e14976

Published: March 29, 2023

The civil construction sector is a major contributor to the emissions of greenhouse gases (GHGs), and accounts for 40 50% total GHGs produced all over world. Concrete utility poles are considered as pillars power distribution systems in many developing regions This study has analysed environmental sustainability low-tension (LT) high-tension (HT) types precast-concrete (PC) used Pakistan. Life cycle analysis (LCA) method assessment burdens associated with production-manufacturing stages these PC poles. LCA scores illustrated five impact categories: climate change, acidification, eutrophication, fine-particulate matter formation fossil resource scarcity. significant have been depicted change abiotic depletion categories as; 4.60E+01 kg CO2 eq. 1.24 E+01 oil eq (for LT pole) 1.55E+02 3.00E+01 HT pole), respectively. analytics further depict that manufacturing pole highly energy intensive process, hauling raw materials finished product which causes towards resources depletion. Overall, this research can offer several novel contributions field sustainable development engineering, including comprehensive impacts practices technologies identification links between economic growth.

Language: Английский

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