Properties of hybrid steel-basalt fiber reinforced concrete exposed to different surrounding conditions

Khan, Mehran; Cao, Mingli; Chu, Sharon Lynn; Ali, Majid

doi:10.1016/j.conbuildmat.2022.126340

Cited by 81 publications

(33 citation statements)

References 31 publications

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“…The addition of dispersed short-discrete fibers to concrete increased the crack resistance and improved the mechanical characteristics [4][5][6][7][8][9][10][11][12]. Steel fibers are also employed to increase the toughness and post-cracking behavior of the cementitious material [13][14][15][16]. The concrete splitting-tensile strength is also affected by the size of the aggregates.…”

Section: Introductionmentioning

confidence: 99%

Use of Artificial Intelligence Methods for Predicting the Strength of Recycled Aggregate Concrete and the Influence of Raw Ingredients

et al. 2022

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Cracking is one of the main problems in concrete structures and is affected by various parameters. The step-by-step laboratory method, which includes casting specimens, curing for a certain period, and testing, remains a source of worry in terms of cost and time. Novel machine learning methods for anticipating the behavior of raw materials on the ultimate output of concrete are being introduced to address the difficulties outlined above such as the excessive consumption of time and money. This work estimates the splitting-tensile strength of concrete containing recycled coarse aggregate (RCA) using artificial intelligence methods considering nine input parameters and 154 mixes. One individual machine learning algorithm (support vector machine) and three ensembled machine learning algorithms (AdaBoost, Bagging, and random forest) are considered. Additionally, a post hoc model-agnostic method named SHapley Additive exPlanations (SHAP) was performed to study the influence of raw ingredients on the splitting-tensile strength. The model’s performance was assessed using the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). Then, the model’s performance was validated using k-fold cross-validation. The random forest model, with an R2 of 0.96, outperformed the AdaBoost models. The random forest models with greater R2 and lower error (RMSE = 0.49) had superior performance. It was revealed from the SHAP analysis that the cement content had the highest positive influence on the splitting-tensile strength of the recycled aggregate concrete and the primary contact of cement is with water. The feature interaction plot shows that high water content has a negative impact on the recycled aggregate concrete (RAC) splitting-tensile strength, but the increased cement content had a beneficial effect.

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Section: Introductionmentioning

confidence: 99%

Use of Artificial Intelligence Methods for Predicting the Strength of Recycled Aggregate Concrete and the Influence of Raw Ingredients

et al. 2022

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“… Further research should also be conducted for large-scale building construction and experimentation to ensure the real capacity of 3D printing technology and to depict the application of this technology in the industry. Furthermore, nowadays, calcium carbonate (CaCO 3 ) whisker is used as micro-fiber in cementitious composites to improve the micromechanical properties of concrete [ 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 ]. Hence, the exploration of CaCO 3 whisker for 3D printing concrete would be an interesting horizon to explore.…”

Section: Discussion and Future Perspectivesmentioning

confidence: 99%

“…Furthermore, nowadays, calcium carbonate (CaCO 3 ) whisker is used as micro-fiber in cementitious composites to improve the micromechanical properties of concrete [ 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 ]. Hence, the exploration of CaCO 3 whisker for 3D printing concrete would be an interesting horizon to explore.…”

Section: Discussion and Future Perspectivesmentioning

confidence: 99%

A Scientometric Review on Mapping Research Knowledge for 3D Printing Concrete

Zhang

YouWang

et al. 2022

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The scientometric analysis is statistical scrutiny of books, papers, and other publications to assess the “output” of individuals/research teams, organizations, and nations, to identify national and worldwide networks, and to map the creation of new (multi-disciplinary) scientific and technological fields that would be beneficial for the new researchers in the particular field. A scientometric review of 3D printing concrete is carried out in this study to explore the different literature aspects. There are limitations in conventional and typical review studies regarding the capacity of such studies to link various elements of the literature accurately and comprehensively. Some major problematic phases in advanced level research are: co-occurrence, science mapping, and co-citation. The sources with maximum articles, the highly creative researchers/authors known for citations and publications, keywords co-occurrences, and actively involved domains in 3D printing concrete research are explored during the analysis. VOS viewer application analyses bibliometric datasets with 953 research publications were extracted from the Scopus database. The current study would benefit academics for joint venture development and sharing new strategies and ideas due to the graphical and statistical depiction of contributing regions/countries and researchers.

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“…The addition of dispersed short-discrete fibers to concrete increased crack resistance and improved mechanical characteristics [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. Steel fibers are employed to increase the toughness and post-cracking behavior of the cementitious material [ 17 , 18 , 19 , 20 ]. Despite several experimental studies in the literature, it is still difficult to predict the characteristics of UHSCs containing various mixtures of components using computational methodologies.…”

Section: Introductionmentioning

confidence: 99%

Compressive Strength Evaluation of Ultra-High-Strength Concrete by Machine Learning

et al. 2022

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In civil engineering, ultra-high-strength concrete (UHSC) is a useful and efficient building material. To save money and time in the construction sector, soft computing approaches have been used to estimate concrete properties. As a result, the current work used sophisticated soft computing techniques to estimate the compressive strength of UHSC. In this study, XGBoost, AdaBoost, and Bagging were the employed soft computing techniques. The variables taken into account included cement content, fly ash, silica fume and silicate content, sand and water content, superplasticizer content, steel fiber, steel fiber aspect ratio, and curing time. The algorithm performance was evaluated using statistical metrics, such as the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The model’s performance was then evaluated statistically. The XGBoost soft computing technique, with a higher R2 (0.90) and low errors, was more accurate than the other algorithms, which had a lower R2. The compressive strength of UHSC can be predicted using the XGBoost soft computing technique. The SHapley Additive exPlanations (SHAP) analysis showed that curing time had the highest positive influence on UHSC compressive strength. Thus, scholars will be able to quickly and effectively determine the compressive strength of UHSC using this study’s findings.

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Properties of hybrid steel-basalt fiber reinforced concrete exposed to different surrounding conditions

Cited by 81 publications

References 31 publications

Use of Artificial Intelligence Methods for Predicting the Strength of Recycled Aggregate Concrete and the Influence of Raw Ingredients

Use of Artificial Intelligence Methods for Predicting the Strength of Recycled Aggregate Concrete and the Influence of Raw Ingredients

A Scientometric Review on Mapping Research Knowledge for 3D Printing Concrete

Compressive Strength Evaluation of Ultra-High-Strength Concrete by Machine Learning

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