Machine-learning-aided improvement of mechanics-based code-conforming shear capacity equation for RC elements with stirrups

Quaranta, Giuseppe; Domenico, Dario De; Monti, Giorgio

doi:10.1016/j.engstruct.2022.114665

Cited by 20 publications

(7 citation statements)

References 24 publications

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“…This validates the proposed methodology's efficacy and underscores its potential to revolutionise industry standards. These tangible outcomes mark a crucial step towards more precise and reliable shear capacity assessments, laying the foundation for a future where machine learning is pivotal for optimising the design and safety of Reinforced Concrete structures [14]. This demonstrates the successful application of machine learning models, revealing high accuracy in shear strength predictions.…”

Section: Introductionmentioning

confidence: 94%

Comparative Analysis of Reinforced Concrete Beam Behaviour: Conventional Model vs. Artificial Neural Network Predictions

Ahmad,

Elahi,

Barbhuiya

2023

Materials

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This research aims to conduct a comparative analysis of the first crack load, flexural strength, and shear strength in reinforced concrete beams without stirrups. The comparison is made between the conventional model developed according to the current design code (ACI building code) and an unconventional approach using Artificial Neural Networks (ANNs). To accomplish this, a dataset comprising 110 samples of reinforced concrete beams without stirrup reinforcement was collected and utilised to train a Multilayer Backpropagation Neural Network in MATLAB. The primary objective of this work is to establish a knowledge-based structural analysis model capable of accurately predicting the responses of reinforced concrete structures. The coefficient of determination obtained from this comparison yields values of 0.9404 for the first cracking load, 0.9756 for flexural strength, and 0.9787 for shear strength. Through an assessment of the coefficient of determination and linear regression coefficients, it becomes evident that the ANN model produces results that closely align with those obtained from the conventional model. This demonstrates the ANN’s potential for precise prediction of the structural behaviour of reinforced concrete beams.

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Section: Introductionmentioning

confidence: 94%

Comparative Analysis of Reinforced Concrete Beam Behaviour: Conventional Model vs. Artificial Neural Network Predictions

Ahmad,

Elahi,

Barbhuiya

2023

Materials

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“…Additionally, the utilization of machine learning algorithms facilitates the prediction of additional concrete properties, such as flexural strength and durability, by effectively capturing intricate patterns and correlations within extensive datasets [24,26,27]. Consequently, the integration of machine learning with traditional engineering knowledge presents a promising avenue for advancing the field of concrete engineering, enabling the design and optimization of more sustainable and efficient structures [28,29].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Prediction of the Compressive Strength of Brazilian Concretes: A Dual-Dataset Study

et al. 2023

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Lately, several machine learning (ML) techniques are emerging as alternative and efficient ways to predict how component properties influence the properties of the final mixture. In the area of civil engineering, recent research already uses ML techniques with conventional concrete dosages. The importance of discussing its use in the Brazilian context is inserted in an international context in which this methodology is already being applied, and it is necessary to verify the applicability of these techniques with national databases or what is created from national input data. In this research, one of these techniques, an artificial neural network (ANN), is used to determine the compressive strength of conventional Brazilian concrete at 7 and 28 days by using a database built through publications in congresses and academic works and comparing it with the reference database of Yeh. The data were organized into nine variables in which the data samples for training and test sets vary in five different cases. The eight possible input variables were: consumption of cement, blast furnace slag, pozzolana, water, additive, fine aggregate, coarse aggregate, and age. The response variable was the compressive strength of the concrete. Using international data as a training set and Brazilian data as a test set, or vice versa, did not show satisfactory results in isolation. The results showed a variation in the five scenarios; however, when using the Brazilian and the reference data sets together as test and training sets, higher R2 values were obtained, showing that in the union of the two databases, a good predictive model is obtained.

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“…Particle swarm optimization adaptive network-based fuzzy interference and Genetic algorithm adaptive network fuzzy interference models have been developed to predict the compressive strength of alkali-activated concrete made from steel slag [ 44 ]. The gray model, a combination of ML and theoretical models, is developed to accurately predict the shear capacity of reinforced concrete [ 45 ]. Mangalathu et al [ 46 ] have explored the ability of ML models in predicting the failure mode of reinforced concrete shear walls.…”

Section: Introductionmentioning

confidence: 99%

Predicting Compressive Strength and Hydration Products of Calcium Aluminate Cement Using Data-Driven Approach

et al. 2023

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Calcium aluminate cement (CAC) has been explored as a sustainable alternative to Portland cement, the most widely used type of cement. However, the hydration reaction and mechanical properties of CAC can be influenced by various factors such as water content, Li2CO3 content, and age. Due to the complex interactions between the precursors in CAC, traditional analytical models have struggled to predict CAC binders’ compressive strength and porosity accurately. To overcome this limitation, this study utilizes machine learning (ML) to predict the properties of CAC. The study begins by using thermodynamic simulations to determine the phase assemblages of CAC at different ages. The XGBoost model is then used to predict the compressive strength, porosity, and hydration products of CAC based on the mixture design and age. The XGBoost model is also used to evaluate the influence of input parameters on the compressive strength and porosity of CAC. Based on the results of this analysis, a closed-form analytical model is developed to predict the compressive strength and porosity of CAC accurately. Overall, the study demonstrates that ML can be effectively used to predict the properties of CAC binders, providing a valuable tool for researchers and practitioners in the field of cement science.

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Machine-learning-aided improvement of mechanics-based code-conforming shear capacity equation for RC elements with stirrups

Cited by 20 publications

References 24 publications

Comparative Analysis of Reinforced Concrete Beam Behaviour: Conventional Model vs. Artificial Neural Network Predictions

Comparative Analysis of Reinforced Concrete Beam Behaviour: Conventional Model vs. Artificial Neural Network Predictions

Machine Learning-Based Prediction of the Compressive Strength of Brazilian Concretes: A Dual-Dataset Study

Predicting Compressive Strength and Hydration Products of Calcium Aluminate Cement Using Data-Driven Approach

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