Artificial Intelligence has one of the most efficient methods for solving engineering and materials problems because of its impressive performance and can reach higher accuracy. The Deep Learning theory is an approach based on Deep Neural Networks for establishing numerical analysis and value predictions. This paper proposes a fresh approach, using a Deep Learning model for predicting the compressive strength in a particular concrete just based on non-destructive test measurements (NDTs). The model proposed is an attractive alternative to estimate the resistance of compressive strength in any structure, just taking data like ultrasonic pulse velocity, electrical resistivity, and resonance frequencies. The present work employs data science techniques to find the correlation values between the NDTs and the compressive strength effort and realized broad numerical exploration about concrete performance. An amount of 285 specimens of concrete were monitored during this research. The model proposed contains 600 neurons and uses a Rectified Linear Unit and Sigmoid as activation functions where the NDTs were established as the input data. The dataset was segmented into two groups: train and test. In order to evaluate the model, the authors tested it in a validation set with different concrete features, achieving an accuracy of 94%.

In this paper, the artificial neural network (ANN) method is utilized to predict ground-penetrating radar reflection amplitudes from four different inputs, namely, temperature, ambient relative humidity, chloride level, and corrosion condition on the surface of the reinforcing bar. A total of 288 ground penetrating radar (GPR) data points were collected from a series of chloride-contaminated concrete slabs under various environmental profiles that were used to train, validate, and test the proposed ANN model. The ANN model performed well in predicting the GPR reflection signals, with the overall coefficient of determination (R2) being 0.9958. The overall mean squared error (MSE) and root mean squared error (RSME) values are 0.015 and 0.122, respectively. These values are very low, which means that the ANN model has an excellent prediction capability. The research results show that the GPR reflection amplitudes are more sensitive to the temperature changes and chloride level parameters than the ambient relative humidity and rust condition on the reinforcing bar surface. Using the ANN method to predict the GPR reflection amplitudes is relatively new for structural concrete applications. This study paves the way for further developments of neural networks in civil and structural engineering.

The probabilistic study of fire exposed structures is laborious and computationally challenging, especially when using advanced numerical models. Moreover, fragility curves developed through traditional approaches apply only to a particular design (structural detailing, fire scenario). Any alteration in design necessitates the computationally expensive re-evaluation of the fragility curves. Considering the above challenges, the use of surrogate models has been proposed for the probabilistic study of fire exposed structures. Previous contributions have confirmed the potential of surrogate models for developing fragility curves for single structural members including reinforced concrete slabs and columns. Herein, the potential of regression-based surrogate models is investigated further with consideration of structural systems. Specifically, an advanced finite element model for evaluating the fire performance of a composite slab panel acting in tensile membrane action is considered. A surrogate model is developed and used to establish fire fragility curves. The results illustrate the potential of surrogate modeling for probabilistic structural fire design of composite structures.

A computational framework employing machine learning (ML) is applied to predict failure mode of ultra-high-performance concrete (UHPC) beams. For this purpose, results from a number of tests on UHPC beams with different geometric and loading configurations and material characteristics are collected and utilized as an input to the ML framework. Results from numerical studies are not included in the data set due to the fact that they are highly dependent upon the adopted material models, meshing practices, as well as other assumptions used in modeling. Artificial neural network is used to predict the failure mode of the UHPC beams. Results indicate that the proposed ML framework is capable of predicting failure mod of UHPC beams with varying reinforcement and configurations, and can be considered for use in design applications. This paper aims to promote the applicability of ML for a practical engineering problem, detecting structural response of UHPC beams.

The bond between reinforcing bars and concrete is an important property that determines the performance of reinforced concrete structures. Accurate prediction of the bond strength is essential for ensuring the safety and economy of the structures. This paper proposes an artificial neural network for predicting the bond strength between straight deformed reinforcing bars and concrete under tensile load. The neural network was trained using a large dataset of test results from the ACI Committee 408 database. A robust ten-fold cross-validation method was employed for evaluating network performance and finding optimal network parameters. Hyperparameter tuning was carried out to establish the optimal network hyperparameters. The relative impact of the neural network input parameters on the bond strength was evaluated using the SHAP method. The developed neural network with the optimal hyperparameters shows a good agreement with the test results. Its accuracy exceeds the accuracy of the descriptive equations.