International Concrete Abstracts Portal

Concrete and cement-based materials inherently possess an autogenous self-healing capacity, which is even higher in High- and Ultra-High-Performance Concrete (HPC, UHPC) because of the high content of cement and supplementary cementitious materials (SCM) and low water/binder ratios. In this study, quantitative correlation through statistical models have been investigated based on the meta-data analysis. The employed approaches aim at establishing a correlation between the mix proportions, exposure type, and time and width of the initial crack against suitably defined self-healing indices. This study provides a holistic investigation of the autogenous self-healing capacity of cement-based materials based on extensive literature data mining. This is also intended to pave the way towards consistent incorporation of self-healing concepts into durability-based design approaches for reinforced concrete structures. The study has shown that the exposure type and duration, crack width size, and chemical admixtures have the most significant promotion on self-healing indices. However, other parameters, such as fibers and mineral admixtures have less impact on the autogenous self-healing of UHPC. The study also proposes suitably built design charts to quickly predict and evaluate the self-healing efficiency of cement-based materials which can significantly reduce, in the design stage, the time and efforts of laboratory investigation.

Structural systems are critical components of modern societies, and as such, need constant condition assessments. Incurred damage and its accumulation due to various loading conditions may lead to complete structural failures with safety consequences. Thus, damage assessment and early fault detection are essential to decision-makers for strategic planning, performing resource-allocation, and obtaining the logistics of these systems. In this article, a data-driven methodology using fault detection of structural systems is proposed. This method utilizes Artificial Neural Networks (ANNs) to model damage due to earthquake loading using sophisticated nonlinear analyses of structures. As a proof-of-concept, the ANN approach for damage-detection is applied to a typical four-story reinforced concrete (RC) structure having varied concrete strengths. The approach is found to have a high potential for successful anomaly identification in RC structural systems.

Soft computing applications through artificial intelligence (AI) are becoming increasingly popular in civil engineering. From concrete technology to structural engineering, AIs have provided successful solutions to various problems and greatly reduced the computational costs while achieving excellent prediction accuracy. In this study, a review of the main artificial neural network (ANN) types used in civil engineering is presented. Each ANN type is described, and example applications are provided. As a new research contribution, a deep feedforward neural network (FFNN) is developed to predict the load capacities of post-installed adhesive anchors installed in cracked concrete, which is challenging and computationally expensive to achieve with conventional methods. The development of this FFNN is discussed, the influence of several parameters on its performance is demonstrated, and optimum parameter values are selected. In addition, a hybrid methodology that combines 2D nonlinear finite element (NLFE) techniques with the developed FFNN is briefly presented to account for real-life adverse effects in anchor analysis, including concrete cracking, wind-induced beam bending, and elevated temperatures. The results show that the developed network and methodology can rapidly and efficiently predict the load capacities of adhesive anchors installed into cracked concrete, accounting for the damage caused by the cracks, with high accuracies.

This paper presents the development of a data-driven deep neural network (DNN) for evaluating the fire resistance time of fiber-reinforced polymer (FRP) strengthened concrete beams. The model was trained for a scaled and unscaled dataset. For this, a comprehensive dataset of FRP-strengthened concrete beams with different geometry, insulation configuration, applied loading, and material characteristics was compiled. The DNN structure was selected after an extensive hyperparameter tuning in conjunction with ten-fold cross-validation scheme. The effect of different input parameters on the fire resistance prediction was analyzed. The DNN model developed using scaled data provides a reasonably accurate estimate, of the fire resistance of FRP-strengthened concrete beams with an R2 value of almost 92%. The developed model is further utilized to evaluate the impact of different parameters on fire resistance prediction for FRP-strengthened concrete beams. Results from the analysis indicate the thermal properties of insulation play an important role in determining the fire resistance of FRP-strengthened concrete 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.