There is a global research trend to enhance condition assessment of the concrete infrastructure by the development of advanced nondestructive testing (NDT) methods. Computer vision–based systems have been developed to detect different types of defects in both regular and thermographic images because these systems could offer a timely and cost-effective solution and are able to tackle the inconsistency issues of manual assessment. This paper investigates the performance of different deep neural network models to detect main concrete anomalies, including delamination, cracks, spalling, and patches in thermographic and regular images captured from a variety of distances and viewpoints. These models were trained and tested using images taken from a century-old buttress dam and validated in images captured from the decks of two concrete bridges. The results showed that the MobileNetV2 had promising performance in the identification of multiclass damages in the thermal images, identifying 79.7% of the total delamination, cracks, spalling, and patches on the test images of highly damaged concrete areas. The VGG 16 model showed better precision by reducing the number of false detections.

One of the concerns about the use of passive Infrared Thermography (IRT) for structural health monitoring (SHM) is the determination of a favorable period to conduct the inspections. This paper investigates the use of numerical simulations to find appropriate periods for IRT-based detection of subsurface damages in concrete bridge slabs under passive heating along a 1 year of time span. A model was built using the Finite Element Method (FEM) and calibrated using the results of a set of thermographic field inspections on a concrete slab sample. The results showed that the numerical simulation properly reproduced the experimental thermographic measurements of the concrete structure under passive heating, allowing the analysis to be extended for a longer testing period. The long-term FEM results demonstrated that the months of spring and summer are the most suitable for passive IRT inspections in this study, with around 17% more detections compared to the autumn and winter periods in Brazil. By enhancing the possibility of using FEM beyond the design stage, we demonstrate that this computation tool can provide support to long-term SHM.

This work presents a new computational approach to estimate the fatigue life in welded joints, taking into account three-dimensional, non-proportional, and out-of-phase stresses; the thermomechanical properties of steel; parameters of the welding process; and the effects of residual stresses. We use the mechanical equations of continuous and cumulative fatigue damage found from Multiaxial RainFlow, the Wang and Brown and critical plane methods, and the Findley criteria approach. The numerical finite element method, available with software, is used to simulate the formation of residual stress and quantify its influence by solving equations. Based on the analysis of numerical results, we were able to prevision the crack location and angles of propagation of a high cycle fatigue crack with totally random loads in a metallic structure. We conclude that the result of the computational method is valid and can be used to estimate and life in fatigue in welded joints.

This research presents an alternative way to perform a bridge inspection, which considers the dynamics parameters from the structure. It shows an experimental phase with use of a mobile phone to extract the accelerations answers from two concrete bridges, from those records is feasible to obtain natural frequencies using the Fast Fourier Transform (FFT).Numerical models with uses finite element model (FEM) allow to determine the natural frequencies from the two concrete bridges and compare with the experimental phase of each one. The final results shows it's possible to use mobiles phones to extract vibration answers from concrete bridges and define the structural behavior of bridges from natural frequencies, this procedure could be used to evaluate bridges with lower costs.