International Concrete Abstracts Portal

The structural deterioration of aging infrastructure systems is becoming an increasingly important issue worldwide. To compound the problem, economic strains limit the resources available for the repair or replacement of such systems. Over the past several decades, structural health monitoring (SHM) has proven to be a cost-effective method for the detection and evaluation of damage in structures. Visual inspection and condition rating is one of the most commonly applied SHM techniques, but the effectiveness of SHM varies depending on the availability and experience of qualified personnel and largely qualitative damage evaluations. Simply supported three-dimensional reinforced concrete T-beams with varying geometric, material, and cracking properties were modeled using Abaqus finite element (FE) analysis software. Up to five cracks were considered in each beam, and the ratios of stiffness between cracked and healthy beams with the same geometric and material parameters were measured at nine equidistant nodes along the beam. A feedforward ANN utilizing backpropagation learning algorithms was then trained on the FE model database with beam properties and nodal stiffness ratios serving as inputs for the neural network model. The outputs consisted of the predicted parameters of location, depth, and width of up to five cracks. This inverse problem is very difficult or impossible to solve with the training done by the Artificial Neural Network. One ANN was trained to predict the parameters of the cracks using the full database of FE simulations. The damage prediction ANN achieved fair prediction accuracies, with coefficients of determination (R2) equal to 0.42. This result was the outcome of the no uniqueness in the prediction of this inverse analysis. Nevertheless, this ANN model provides a rough estimate of the cracking type and damage content in bridge girders once the nodal stiffness ratios are measured by applying a field vehicle loading and measuring the deflection using a theodolite. A touch-enabled user interface was developed to allow the ANN model to predict the crack configurations. The application was given the acronym DRY BEAM, for Damage Recognition Yielding Bridge Evaluation After Monitoring.

Many modeling approaches in engineering are based on physical principles. The input and output relationships are developed using physical laws (e.g., Newton's laws of motion and conservation of mass and energy). However, in many situations, the development of physically-based models requires simplifying assumptions due to the complicated nature of the systems, which could lead to a large degree of uncertainty. In these situations, data can be used to formulate models by detecting relationships between the system’s variables (inputs and outputs) without explicitly knowing the physical behavior of the system. Therefore, there is a paradigm shift from physically-based models to data-driven models. The objective of this study is to develop a drift capacity prediction model for structural walls with special boundary elements using the extreme gradient boosting (XGBoost) machine learning algorithm. The resulting prediction model is compared with the recently developed empirical model presented in literature i.e., the Abdullah & Wallace (2019) model. The results reveal the proposed model’s superior predictive capabilities relative to the empirical model.

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.