Precise design and characteristics prediction of Ultra-High Performance Concrete (UHPC) based on artificial intelligence techniques

Fan, Dian; Yu, Rui; Fu, Shiyuan; Yue, Liang; Wu, Chunfeng; Shui, Zhonghe; Liu, Kangning; Song, Qiulei; Sun, Meijuan; Jiang, Chunyuan

doi:10.1016/j.cemconcomp.2021.104171

Cited by 90 publications

(12 citation statements)

References 61 publications

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“…Abellán-García (2020) employed the four-layer Multi-Layer Perceptron (MLP) for predicting the UHPC compressive strength and resulted in the satisfactory capability of MLP for strength prediction. A similar type of finding was also found in the literature for MLP modelling prediction (Abellán-García et al, 2020;Fan et al, 2021;Abellán-García and García-Castaño, 2022). Although, the application of these algorithms for UHPC compressive strength is reported several times in the literature; however, the employment of these models for predicting the flexural strength of UHPC has limited.…”

Section: Introductionsupporting

confidence: 79%

Application of machine learning algorithms to evaluate the influence of various parameters on the flexural strength of ultra-high-performance concrete

et al. 2023

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The effect of various parameters on the flexural strength (FS) of ultra-high-performance concrete (UHPC) is an intricate mechanism due to the involvement of several inter-dependent raw ingredients. In this digital era, novel artificial intelligence (AI) approaches, especially machine learning (ML) techniques, are gaining popularity for predicting the properties of concrete composites due to their better precision than typical regression models. In addition, the developed ML models in the literature for FS of UHPC are minimal, with limited input parameters. Hence, this research aims to predict the FS of UHPC considering extensive input parameters (21) and evaluate each their effect on its strength by applying advanced ML approaches. Consequently, this paper involves the application of ML approaches, i.e., Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Gradient Boosting (GB), to predict the FS of UHPC. The GB approach is more effective in predicting the FS of UHPC precisely than the SVM and MLP algorithms, as evident from the outcomes of the current study. The ensembled GB model determination coefficient (R2) is 0.91, higher than individual SVM with 0.75 and individual MLP with 0.71. Moreover, the precision of applied models is validated by employing the k-fold cross-validation technique. The validity of algorithms is ensured by statistical means, i.e., mean absolute error and root mean square errors. The exploration of input parameters (raw materials) impact on FS of UHPC is also made with the help of SHAP analysis. It is revealed from the SHAP analysis that the steel fiber content feature has the highest influence on the FS of UHPC.

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Section: Introductionsupporting

confidence: 79%

Application of machine learning algorithms to evaluate the influence of various parameters on the flexural strength of ultra-high-performance concrete

et al. 2023

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“…These often contain complex internal structures, described by parameters that offer little insight on what takes place inside the model during computations. Nevertheless, many researchers have used AI for concrete mix design optimization [72][73][74][75][76][77], while others have focused on fractional factorial design techniques to reduce experimental runs [78,79]. Recently, these two approaches were combined by the author in [22,80] to effectively estimate the trends existing in the evaluated domain of mix proportions without resorting to extensive experimental campaigns or use multiple-source datasets.…”

Section: Concrete MIX Design Optimization: New Opportunities With Aimentioning

confidence: 99%

Multi-criteria comparison tools to evaluate cost- and eco-efficiency of ultra-high-performance concrete

Tavares,

Skillen,

Shi

et al. 2023

Environ. Res.: Infrastruct. Sustain.

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This work was motivated by the increasing need for proper metrics and tools to demonstrate the effect of mechanical performance, as a function of concrete mix composition, in dictating the dimensions of structural elements and associated costs and embodied carbon dioxide (CO2) emissions. Mixture compositions associated with different concrete technologies were compared using multi-criteria comparison indices derived using structural design considerations and calculated using information on compressive strength, volumetric embodied CO2 and unit costs. In addition, predicted compressive strengths obtained with machine learning (ML) models are used to calculate these indices for a domain of mix proportions associated with ultra-high-performance concrete materials to generate multi-objective density diagrams (MODDs). The makeup of this tool facilitates the evaluation of rather complicated trends associated with mix proportions and multi-objective outcomes, allowing ML-based tools to be of easy interpretation by industry personnel with no expertise in artificial intelligence. MODDs could be used as aids in the decision-making process during mix design stages and serve as proof of mixture optimization that could be introduced in Environmental Product Declarations. Results show that, in contrast to conventional wisdom, high-binder content and ultra-high strength concrete technologies are not necessarily detrimental to cost and/or eco efficiencies. For the applications evaluated herein, optimum solutions were mostly obtained with these types of concrete, suggesting that industry trends toward requiring minimization of embodied carbon footprint on a per volume of concrete basis are misguided and should not be used as a standalone metric to minimize the total carbon footprint of concrete structures.

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“…Cement efficiency is a factor present in different studies to assess the relation of the characteristic strength of the concretes designed and the amount of cement to produce them [17], [33], [34]. Cement efficiency was calculated as the ratio of the cement content in 1 m 3 of concrete and the concrete strength obtained in MPa, as shown in Equation 2:…”

Section: Cement and Environmental Efficienciesmentioning

confidence: 99%

Reduction of the environmental impacts of reinforced concrete columns by increasing the compressive strength: a life cycle approach

Alievi

Santoro

Kripka

2022

Rev. IBRACON Estrut. Mater.

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The building industry is one of the greatest environmental impact causers in the planet. Cement is the second most used material in the world and the consumption of concrete ranges between 20 to 30 Gt yearly. This demand for the materials ten ds to increase for the next 100 years. The increase of concrete strength to reduce the material consumption is one of the options proposed in literature to reduce the environmental impacts in building industry. However, few studies have been carried about the actual advantages of this strategy in building production. In this paper, a 15-storey reinforced concrete building was designed with three different concrete grades for its columns: 30 MPa, 40 MPa and 50 MPa. The results for the volume of concrete and the amount of reinforcing steel to produce the columns were used to perform a cradle-to-gate life cycle assessment (LCA) to determine the alternative with less environmental impacts in the production stage. Results indicate an advantage to adopt higher strength concretes in columns to reduce environmental impacts and the consumption of materials. Direct effects of higher strength in concretes made possible to reduce the consumption of concrete by 15%. There was also a significant reduction caused by indirect effects of higher strengths in concrete, with the reducing of steel consumption up to 22%. With the combination of the direct and indirect effects of higher compressive strengths, it was possible to reduce the environmental impacts of reinforced concrete in all categories studied in the LCA.

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Precise design and characteristics prediction of Ultra-High Performance Concrete (UHPC) based on artificial intelligence techniques

Cited by 90 publications

References 61 publications

Application of machine learning algorithms to evaluate the influence of various parameters on the flexural strength of ultra-high-performance concrete

Application of machine learning algorithms to evaluate the influence of various parameters on the flexural strength of ultra-high-performance concrete

Multi-criteria comparison tools to evaluate cost- and eco-efficiency of ultra-high-performance concrete

Reduction of the environmental impacts of reinforced concrete columns by increasing the compressive strength: a life cycle approach

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