Machine learning in concrete science: applications, challenges, and best practices

Li, Zhanzhao; Yoon, Jinyoung; Zhang, Rui; Rajabipour, Farshad; Srubar, Wil V.; Dabo, Ismaïla; Radlińska, Aleksandra

doi:10.1038/s41524-022-00810-x

Cited by 144 publications

(62 citation statements)

References 185 publications

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“…Finally, it is noted that the application of data science methods, such as machine learning [65,66], to the present dataset might yield insights into the carbonation resistance of alkali-activated materials in addition to those obtained in the present analysis. To enable such analyses in future studies, the full dataset underlying the present analysis is provided as Electronic Supplementary Material alongside this article.…”

Section: Discussionmentioning

confidence: 87%

Carbonation rate of alkali-activated concretes and high-volume SCM concretes: a literature data analysis by RILEM TC 281-CCC

Gluth

Vollpracht

et al. 2022

Mater Struct

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The current understanding of the carbonation and the prediction of the carbonation rate of alkali-activated concretes is complicated inter alia by the wide range of binder chemistries used and testing conditions adopted. To overcome some of the limitations of individual studies and to identify general correlations between mix design parameters and carbonation resistance, the RILEM TC 281-CCC ‘Carbonation of Concrete with Supplementary Cementitious Materials’ Working Group 6 compiled and analysed carbonation data for alkali-activated concretes and mortars from the literature. For comparison purposes, data for blended Portland cement-based concretes with a high percentage of SCMs (≥ 66% of the binder) were also included in the database. The analysis indicates that water/CaO ratio and water/binder ratio exert an influence on the carbonation resistance of alkali-activated concretes; however, these parameters are not good indicators of the carbonation resistance when considered individually. A better indicator of the carbonation resistance of alkali-activated concretes under conditions approximating natural carbonation appears to be their water/(CaO + MgOeq + Na2Oeq + K2Oeq) ratio, where the subscript ‘eq’ indicates an equivalent amount based on molar masses. Nevertheless, this ratio can serve as approximate indicator at best, as other parameters also affect the carbonation resistance of alkali-activated concretes. In addition, the analysis of the database points to peculiarities of accelerated tests using elevated CO2 concentrations for low-Ca alkali-activated concretes, indicating that even at the relatively modest concentration of 1% CO2, accelerated testing may lead to inaccurate predictions of the carbonation resistance under natural exposure conditions.

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Section: Discussionmentioning

confidence: 87%

Carbonation rate of alkali-activated concretes and high-volume SCM concretes: a literature data analysis by RILEM TC 281-CCC

Gluth

Vollpracht

et al. 2022

Mater Struct

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“…When discussing future plans in relation to the status of the research and its limitations, we must identify the following research priorities: In the hardware area: expanding the number and types of sensors and effectors that can be used; and In the software area: expanding the system’s capabilities to include non-precision data processing, inference, trend analysis, and prediction [ 29 , 30 ]. …”

Section: Discussionmentioning

confidence: 99%

“…In the software area: expanding the system’s capabilities to include non-precision data processing, inference, trend analysis, and prediction [ 29 , 30 ].…”

Section: Discussionmentioning

confidence: 99%

AI-Based Support System for Monitoring the Quality of a Product within Industry 4.0 Paradigm

Rojek

Dostatni

Kopowski

et al. 2022

Sensors

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Three-dimensional (3D) printing, also known as additive manufacturing (AM), has already shown its potential in the fourth technological revolution (Industry 4.0), demonstrating remarkable applications in manufacturing, including of medical devices. The aim of this publication is to present the novel concept of support by artificial intelligence (AI) for quality control of AM of medical devices made of polymeric materials, based on the example of our own elbow exoskeleton. The methodology of the above-mentioned inspection process differs depending on the intended application of 3D printing as well as 3D scanning or reverse engineering. The use of artificial intelligence increases the versatility of this process, allowing it to be adapted to specific needs. This brings not only innovative scientific and technological solutions, but also a significant economic and social impact through faster operation, greater efficiency, and cost savings. The article also indicates the limitations and directions for the further development of the proposed solution.

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“…Nevertheless, there are some limitations associated with the use of ML methods. Specifically, challenges associated with dataset generation, model validation, and model deployment, as reported by (LI et al, 2022B). The allocation of testing and training data samples utilized for the model was 30% and 70%, respectively.…”

Section: Methodsmentioning

confidence: 99%

Machine learning techniques to evaluate the ultrasonic pulse velocity of hybrid fiber-reinforced concrete modified with nano-silica

et al. 2022

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It is evident that preparing materials, casting samples, curing, and testing all need time and money. The construction sector will benefit if these problems can be handled using cutting-edge techniques like machine learning. Also, a material’s ultrasonic pulse velocity (UPV) is affected by various variables, and it is difficult to study their combined effect experimentally. This research used machine learning to assess the UPV and SHapley Additive ExPlanations techniques to study the impact of input parameters of hybrid fiber-reinforced concrete modified with nano-silica (HFRNSC). Three ML algorithms were employed, i.e., gradient boosting regressor, adaptive boosting regressor, and extreme gradient boosting, for ultrasonic pulse velocity evaluation. The accuracy of machine learning models was measured via the coefficient of determination (R2), k-fold analysis, statistical tests, and comparing the predicted and actual ultrasonic pulse velocity. This study determined that the gradient boosting and adaptive boosting models had a good level of accuracy for ultrasonic pulse velocity, but the extreme gradient boosting method estimated the ultrasonic pulse velocity of HFRNSCs with a greater degree of precision. Also, from the statistical checks and k-fold approach, it was discovered that the extreme gradient boosting method is more exact in estimating the ultrasonic pulse velocity of HFRNSCs. The SHapley Additive ExPlanations analysis revealed that the age of the specimen and nano-silica had a greater positive impact on the ultrasonic pulse velocity of HFRNSCs, whereas the coarse aggregate to fine aggregate ratio had a negative impact. In addition, fiber volume was found to have both positive and negative effects. By aiding the development of rapid and low-cost methods for determining material properties and the influence of input parameters, the construction industry may profit from the use of such technologies.

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Machine learning in concrete science: applications, challenges, and best practices

Cited by 144 publications

References 185 publications

Carbonation rate of alkali-activated concretes and high-volume SCM concretes: a literature data analysis by RILEM TC 281-CCC

Carbonation rate of alkali-activated concretes and high-volume SCM concretes: a literature data analysis by RILEM TC 281-CCC

AI-Based Support System for Monitoring the Quality of a Product within Industry 4.0 Paradigm

Machine learning techniques to evaluate the ultrasonic pulse velocity of hybrid fiber-reinforced concrete modified with nano-silica

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