Enhancing urban sustainability: a study on lightweight and pervious concrete incorporating recycled plastic

Discover Sustainability, Journal Year: 2024, Volume and Issue: 5(1)

Published: Nov. 20, 2024

Abstract Increasing of plastic waste threatening ecosystems globally, this experimental work investigates recycled plastics as sustainable aggregate replacements in pervious concrete. Pervious concrete allows water passage but has installation/maintenance difficulty due to high weight. This research addresses the lack eco-friendly lightweight solutions by assessing physical and mechanical performance mixes with 100% traditional percentages. Density reduced 12% using a mix, achieving 1358 kg/m 3 compressive strength 3.92 MPa, adequate for non-structural applications. A 7.8% decrease absorption versus conventional signifies retained porosity permeability despite aggregates. Though early material limitations increase costs over 199.32%, show viability effective, substitutes natural aggregates With further availability affordability improvements, these recyclable can enable significantly greener construction practices. Findings provide key insights on balancing structural requirements, eco-friendliness infiltration capacity plastic-based broader adoption. The examines durability characteristics Light-Weight Concrete (LWPC) composed entirely aggregate. It also economic potential urban cost assessment reveals long-term environmental advantages, even though initial expenses are higher. Additionally, study considers an approach that combines plant growth promote greater sustainability.

Language: Английский

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Multi-Objective Optimization of Blended Cement Mortars from Copper Slags

Published: Jan. 1, 2025

Language: Английский

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How Can We Assess Long-Term Multiple Properties Using Only Very Little High-Variance Experimental Data Fused Physical Information?-A Real-Life Concrete Dam Application

Published: Jan. 1, 2025

Language: Английский

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Novel approaches in prediction of tensile strain capacity of engineered cementitious composites using interpretable approaches

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

Published: Jan. 1, 2025

Abstract The performance and durability of conventional concrete (CC) are significantly influenced by its weak tensile strength strain capacity (TSC). Thus, the intrusion fibers in cementitious matrix forms ductile engineered composites (ECCs) that can cater to this area CC. Moreover, ECCs have become a reasonable substitute for brittle plain due their increased flexibility, ductility, greater TSC. prediction ECC is crucial without need laborious experimental procedures. achieve this, machine learning approaches (MLAs), namely light gradient boosting (LGB) approach, extreme (XGB) artificial neural network (ANN), k -nearest neighbor (KNN), were developed. data gathered from literature comprise input parameters which fiber content, length, cement, diameter, water-to-binder ratio, fly ash (FA), age, sand, superplasticizer, TSC as output utilized. assessment models gauged with coefficient determination ( R 2 ), statistical measures, uncertainty analysis. In addition, an analysis feature importance carried out further refinement model. result demonstrates ANN XGB perform well train test sets > 0.96. Statistical measures show all give fewer errors higher , depict robust performance. Validation via K -fold confirms showing correlation determination. reveals FA major contribution ECC. graphical user interface also developed help users/researchers will facilitate them estimate practical applications.

Language: Английский

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How machine learning can transform the future of concrete

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

Published: March 14, 2025

Language: Английский

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Development of low carbon concrete with high cement replacement ratio by multi-response optimization

Cleaner Materials, Journal Year: 2025, Volume and Issue: unknown, P. 100304 - 100304

Published: March 1, 2025

Language: Английский

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Experimental Investigation on Shear Strength at the Permeable Concrete–Fine-Grained Soil Interface for Slope Stabilization Using Deep Socket Counterfort Drains

GeoHazards, Journal Year: 2024, Volume and Issue: 5(3), P. 917 - 931

Published: Sept. 17, 2024

In slopes where high pore water pressure exists, deep counterfort drains (also called drainage trenches or trench drains) represent one of the most effective methods for improving stability mitigating landslide risks. cases very slip surfaces, this method represents only possible intervention. Trench can be realized by using panels secant piles filled with coarse granular material permeable concrete. If are adequately “socket” into stable ground (for example sufficiently below sliding surface a critical marginally slopes) and filling has sufficient shear strength stiffness, like porous concrete, there is further increase in due to “shear keys” effect. The both intrinsic resistance concrete on at concrete–soil interface (on lateral trench). latter significant relation thickness mass, “socket depth”, spacing between trenches. linked keys effect” depends state interface. For silty–clayey base soils, it same order magnitude as permanent reduction (draining effect). This paper presents results an experimental investigation fine-grained soils demonstrates significance effectiveness

Language: Английский

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Predicting the compressive strength of self-compacting concrete by developed African vulture optimization algorithm-Elman neural networks

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

Published: Aug. 29, 2024

The compressive strength of concrete depends on various factors. Since these parameters can be in a relatively wide range, it is difficult for predicting the behavior concrete. Therefore, to solve this problem, an advanced modeling needed. aim literature achieve ideal and flexible solution necessary develop new approaches. Artificial Neural Networks (ANNs) have evolved from theoretical method widely utilized technology by successful applications variety issues. Actually, ANNs are strong computing tool that provides right solutions problems use conventional methods. Inspired biological neural system, networks now used solving range complicated civil engineering. This study''s target evaluating performance developed African vulture optimization algorithm (DAVOA)-Elman (ENNs) considering different input self-compacting strength. Hence, once 8 again get as close possible prediction conditions laboratory, 140 entered improved version Elman input. According results, element network has lowest mean squares test error 7 28 days 100 repetitions. Further, both strengths, grid with Logsig-Purelin interlayer transfer function error, which determines optimal function. Moreover, results showed DAVOA reliable time cost savings high power desired characteristics. Also, 7-day 28-day strength, built 74.54 70.44% improvement over 8-parameter networks, respectively, directly affects effect. Further considered rate properties.

Language: Английский

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

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

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Predictive Modeling of the Long-term Effects of Combined Chemical Admixtures on Concrete Compressive Strength Using Machine Learning Algorithms

Case Studies in Chemical and Environmental Engineering, Journal Year: 2024, Volume and Issue: 10, P. 101008 - 101008

Published: Nov. 13, 2024

Language: Английский

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