Dataset Information

Physics-assisted machine learning methods for predicting the splitting tensile strength of recycled aggregate concrete.

ABSTRACT: Recycled aggregate concrete (RAC) has become a popular building material due to its eco-friendly features, but the difficulty in predicting the crack resistance of RAC is increasingly impeding its application. In this study, splitting tensile strength is adopted to describe the crack resistance ability of RAC, and physics-assisted machine learning (ML) methods are used to construct the predictive models for the splitting tensile strength of RAC. The results show that the AdaBoost model has excellent predictive performance with the help of the Firefly algorithm, and physical assistance plays a remarkable role in selecting features and verifying the ML models. Due to the limit in data size and the generalizability of the model, the dataset should be supplemented with more representative data, and an algorithm for small sample sizes could be studied in the future.

SUBMITTER: Liu J

PROVIDER: S-EPMC10241791 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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Physics-assisted machine learning methods for predicting the splitting tensile strength of recycled aggregate concrete.

Liu Jianguo J Han Xiangyu X Pan Yin Y Cui Kai K Xiao Qinghua Q

Scientific reports 20230605 1

Recycled aggregate concrete (RAC) has become a popular building material due to its eco-friendly features, but the difficulty in predicting the crack resistance of RAC is increasingly impeding its application. In this study, splitting tensile strength is adopted to describe the crack resistance ability of RAC, and physics-assisted machine learning (ML) methods are used to construct the predictive models for the splitting tensile strength of RAC. The results show that the AdaBoost model has excel ...[more]

PMID: 37277446

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Project description:Physics-informed modeling (PIM) using advanced machine learning (ML) represents a paradigm shift in the field of concrete technology, offering a potent blend of scientific rigor and computational efficiency. By harnessing the synergies between physics-based principles and data-driven algorithms, PIM-ML not only streamlines the design process but also enhances the reliability and sustainability of concrete structures. As research continues to refine these models and validate their performance, their adoption promises to revolutionize how concrete materials are engineered, tested, and utilized in construction projects worldwide. In this research work, an extensive literature review, which produced a global representative database for the splitting tensile strength (Fsp) of recycled aggregate concrete, was indulged. The studied concrete components such as C, W, NCAg, PL, RCAg_D, RCAg_P, RCAg_wa, Vf, and F_type were measured and tabulated. The collected 257 records were partitioned into training set of 200 records (80%) and validation set of 57 records (20%) in line with a more reliable partitioning of database. Five advanced machine learning techniques created using the "Weka Data Mining" software version 3.8.6 were applied to predict the Fsp and the Hoffman & Gardener method and performance metrics were also used to evaluate the sensitivity and performance of the variables and ML models, respectively. The results show the Kstar model demonstrates the highest level of performance and reliability among the models, achieving exceptional accuracy with an R2 of 0.96 and Accuracy of 94%. Its RMSE and MAE are both low at 0.15 MPa, indicating minimal deviations between predicted and actual values. Additional metrics such as WI (0.99), NSE (0.96), and KGE (0.96) further confirm the model's superior efficiency and consistent performance, making it the most dependable tool for practical applications. Also the sensitivity analysis shows that Water content (W) exerts the most significant impact at 40%, demonstrating that the amount of water in the mix is a critical factor for achieving optimal tensile strength. This underscores the need for careful water management to balance workability and strength in sustainable concrete production. Coarse natural aggregate (NCAg) has a substantial impact of 38%, indicating its essential role in maintaining the structural integrity of the concrete mix.

Project description:At present, many modification methods have been proposed to improve the performance of recycled aggregate concrete (RAC). In this study, tests on the compressive strength and damping properties of modified RAC with the addition of different proportions of recycled coarse aggregate (RCA) (0, 50, 100%), rubber powder (10, 15, 20%), steel fibre (5, 7.5, 10%) and fly ash (15, 20, 5%) are carried out. To elucidate the effect of the modification method on the interfacial transition zone (ITZ) performance of RAC, model ITZ specimens are used for push-out tests. The results show that when the replacement rate of RCA reaches 100%, the loss factor of the RAC is 6.0% higher than that of natural aggregate concrete; however, the compressive strength of the RAC decreases by 22.6%. With the addition of 20% rubber powder, the damping capacity of the modified RAC increases by 213.7%, while the compressive strength of the modified RAC decreases by 47.5%. However, with the addition of steel fibre and fly ash, both the compressive strength and loss factor of the RAC specimens increase. With a steel fibre content of 10 wt%, the compressive strength and loss factor of the RAC increase by 21.9% and 15.2%, respectively. With a fly ash content of 25 wt%, the compressive strength and loss factor of the RAC increase by 8.6% and 6.9%, respectively. This demonstrates that steel fibre and fly ash are effective in improving both the damping properties and compressive strength of RAC, and steel fibre is more effective than fly ash. Two methods were used for modification of the RAC: reinforcing the RCA through impregnation with a 0.5% polyvinyl alcohol (PVA) emulsion and nano-SiO2 solution, and strengthening the RAC integrally through the addition of fly ash as an admixture. Both of these techniques can improve the ITZ bond strength between the RAC and new mortar. Replacing 10% of the cement with fly ash in the new mortar is shown to be the best method to improve the ITZ strength.

Project description:The additive manufacturing of concrete, also known as 3D-printed concrete, is produced layer by layer using a 3D printer. The three-dimensional printing of concrete offers several benefits compared to conventional concrete construction, such as reduced labor costs and wastage of materials. It can also be used to build complex structures with high precision and accuracy. However, optimizing the mix design of 3D-printed concrete is challenging, involving numerous factors and extensive hit-and-trail experimentation. This study addresses this issue by developing predictive models, such as the Gaussian Process Regression model, Decision Tree Regression model, Support Vector Machine model, and XGBoost Regression models. The input parameters were water (Kg/m3), cement (Kg/m3), silica fume (Kg/m3), fly ash (Kg/m3), coarse aggregate (Kg/m3 & mm for diameter), fine aggregate (Kg/m3 & mm for diameter), viscosity modifying agent (Kg/m3), fibers (Kg/m3), fiber properties (mm for diameter and MPa for strength), print speed (mm/sec), and nozzle area (mm2), while target properties were the flexural and tensile strength of concrete (MPa data from 25 literature studies were collected. The water/binder ratio used in the dataset ranged from 0.27 to 0.67. Different types of sands and fibers have been used, with fibers having a maximum length of 23 mm. Based upon the Coefficient of Determination (R2), Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Error (MAE) for casted and printed concrete, the SVM model performed better than other models. All models' cast and printed flexural strength values were also correlated. The model's performance has also been checked on six different mix proportions from the dataset to show its accuracy. It is worth noting that the lack of ML-based predictive models for the flexural and tensile properties of 3D-printed concrete in the literature makes this study a novel innovation in the field. This model could reduce the computational and experimental effort required to formulate the mixed design of printed concrete.

Dataset Information

Physics-assisted machine learning methods for predicting the splitting tensile strength of recycled aggregate concrete.

Publications

Physics-assisted machine learning methods for predicting the splitting tensile strength of recycled aggregate concrete.

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