• Advances in concrete construction
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• v.16 no.4
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• pp.177-188
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• 2023

In the context of the shortage of river sand, two types of manufactured sand (MS) were used to partially replace river sand (RS) to design manufactured sand concrete (MSC). A total of 81 specimens were designed for uniaxial compression test and beam flexure test. Two parameters were considered in the tests, including the types of MS (i.e. limestone manufactured sand (LMS), pebble manufactured sand (PMS)) and the MS replacement percentage (i.e., 0%, 25%, 50%, 75%, 100%). The stress-strain curves of MSC were obtained. The effects of these parameters on the compressive strength, elastic modulus, peak strain, toughness and flexural strength were discussed. Additionally, the sensitivity of particle size distributions to the performance of MSC was evaluated based on the grey correlation analysis. The results showed that compared with river sand concrete (RSC), the rising slope of the stress-strain curves of limestone manufactured sand concrete (LMSC) and pebble manufactured sand concrete (PMSC) were higher, the descending phrase of LMSC were gentle but that of PMSC showed an opposite trend. The physical and mechanical properties of MSC were affected by the MS replacement percentage except the compressive strength of PMSC. When the replacement percentage of LMS and PMS were 50% and 25% respectively, the corresponding performances of LMSC and PMSC were better. In generally, when the replacement percentage of LMS and PMS were same, the comprehensive performance of LMSC were better than that of PMSC. The constitutive model and the equations for mechanical properties were proposed. The influence of particle ranging from 0.15 mm to 0 mm on the performance of MSC was lower than particle ranging from 4.75 mm to 0.15 mm but this influence should not be ignored.

• Advances in nano research
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• v.15 no.6
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• pp.533-539
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• 2023

This study aims to examine four machine learning (ML)-based models for their potential to estimate the splitting tensile strength (STS) of manufactured sand concrete (MSC). The ML models were trained and tested based on 310 experimental data points. Stone nanopowder content (SNPC), curing age (CA), and water-to-cement (W/C) ratio were also studied for their impacts on the STS of MSC. According to the results, the support vector regression (SVR) model had the highest correlation with experimental data. Still, all of the optimized ML models showed promise in estimating the STS of MSC. Both ML and laboratory results showed that MSC with 10% SNPC improved the STS of MSC.

• Advances in nano research
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• v.16 no.4
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• pp.375-394
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• 2024

The extensive utilization of concrete has given rise to environmental concerns, specifically concerning the depletion of river sand. To address this issue, waste deposits can provide manufactured-sand (MS) as a substitute for river sand. The objective of this study is to explore the application of machine learning techniques to facilitate the production of manufactured-sand concrete (MSC) containing stone nano-powder through estimating the splitting tensile strength (STS) containing compressive strength of cement (CSC), tensile strength of cement (TSC), curing age (CA), maximum size of the crushed stone (Dmax), stone nano-powder content (SNC), fineness modulus of sand (FMS), water to cement ratio (W/C), sand ratio (SR), and slump (S). To achieve this goal, a total of 310 data points, encompassing nine influential factors affecting the mechanical properties of MSC, are collected through laboratory tests. Subsequently, the gathered dataset is divided into two subsets, one for training and the other for testing; comprising 90% (280 samples) and 10% (30 samples) of the total data, respectively. By employing the generated dataset, novel models were developed for evaluating the STS of MSC in relation to the nine input features. The analysis results revealed significant correlations between the CSC and the curing age CA with STS. Moreover, when delving into sensitivity analysis using an empirical model, it becomes apparent that parameters such as the FMS and the W/C exert minimal influence on the STS. We employed various loss functions to gauge the effectiveness and precision of our methodologies. Impressively, the outcomes of our devised models exhibited commendable accuracy and reliability, with all models displaying an R-squared value surpassing 0.75 and loss function values approaching insignificance. To further refine the estimation of STS for engineering endeavors, we also developed a user-friendly graphical interface for our machine learning models. These proposed models present a practical alternative to laborious, expensive, and complex laboratory techniques, thereby simplifying the production of mortar specimens.