Concrete beams are elements used in many civil structures; unfortunately, they can contain cracks that lead to the collapse of the structures if those defects are not detected early enough. In this article, a new method to determine the structural condition of concrete beams subjected to bending is proposed. In general, it is based on the processing of the acoustic emissions (AE) signals, which are generated during the application of a load, by using the mathematical tool called wavelet transform (WT). The sound of the internal energy/crack is recorded as a hit or AE signal event; then, those signals acquired as waveforms are post-processed with the continuous WT (CWT); then, the wavelet energy (WE) is calculated for each hit by using an adequate scale range and the most convenient mother wavelet. Thus, with this method, it is possible to determine the structural condition (healthy or damaged) of concrete beams subjected to bending just by calculating the WE of any hit at any time and, even more, it is possible to define more precisely the stage of the structural condition as a healthy condition, micro-cracks appearance, the manifestation of a principal crack (hit with the highest WE), propagation of the principal crack, and final rupture. This method is experimentally validated in the laboratory, and additionally, ultrasonic pulse velocity tests (UPVT) are performed for some specimens to confirm the change between healthy and damaged conditions. The results are promising in order to apply this effective method in concrete beams of real-life structures.

A new multiple signal classification (MUSIC)-based methodology is presented for detecting and locating multiple damage types in a truss-type structure subjected to dynamic excitations. The methodology is based mainly on two steps: in step 1, the MUSIC method is employed to obtain the pseudo-spectra of vibration signatures, healthy and damaged, to be used for damage detection. In step 2, a new damage index, based on the obtained pseudo-spectra, is proposed to measure the structure condition. Furthermore, the damage location is estimated according to the variation in the amplitudes of the estimated pseudo-spectra. The presented results show that the proposed methodology can make an accurate and reliable estimation of the condition and location of three specific damage conditions, i.e., loosened bolts, internal corrosion, and external corrosion.

Large civil structures such as bridges must be permanently monitored to ensure integrity and avoid collapses due to damage resulting in devastating human fatalities and economic losses. In this article, a wavelet-based method called the Wavelet Energy Accumulation Method (WEAM) is developed in order to detect, locate and quantify damage in vehicular bridges. The WEAM consists of measuring the vibration signals on different points along the bridge while a vehicle crosses it, then those signals and the corresponding ones of the healthy bridge are subtracted and the Continuous Wavelet Transform (CWT) is applied on both, the healthy and the subtracted signals, to obtain the corresponding diagrams, which provide a clue about where the damage is located; then, the border effects must be eliminated. Finally, the Wavelet Energy (WE) is obtained by calculating the area under the curve along the selected range of scale for each point of the bridge deck. The energy of a healthy bridge is low and flat, whereas for a damaged bridge there is a WE accumulation at the damage location. The Rio Papaloapan Bridge (RPB) is considered for this research and the results obtained numerically and experimentally are very promissory to apply this method and avoid accidents.

Condition monitoring and fault detection in wind turbines are essential for reducing repair and maintenance costs. Early detection of faults enables timely interventions before the damage worsens. However, existing methods often rely on costly scheduled inspections or lack the ability to effectively detect early stage damage, particularly under different operational speeds. This article presents a methodology based on convolutional neural networks (CNNs) and empirical mode decomposition (EMD) of vibration signals for the detection of blade crack damage. The proposed approach involves acquiring vibration signals under four conditions: healthy, light, intermediate, and severe damage. EMD is then applied to extract time–frequency representations of the signals, which are subsequently converted into images. These images are analyzed by a CNN to classify the condition of the wind turbine blades. To enhance the final CNN architecture, various image sizes and configuration parameters are evaluated to balance computational load and classification accuracy. The results demonstrate that combining vibration signal images, generated using the EMD method, with CNN models enables accurate classification of blade conditions, achieving 99.5% accuracy while maintaining a favorable trade-off between performance and complexity.

Concrete is currently the most used construction material, mainly due to its mechanical strength, chemical stability, and low cost. This material is affected by wear processes caused by the environment, which lead to a reduction in the useful life of the infrastructure in the long term. These wear processes can cause cracks, corrosion of reinforcing steel, loss of load capacity, and loss of concrete section, among other problems. Considering the above, it is necessary to carry out durability studies on concrete to determine the integrity conditions in which the infrastructure is found, the reasons for its deterioration, the environmental factors that affect it, and its useful life under these conditions, and develop restoration or protection plans. Generally, the durability studies include non-destructive testing such as ultrasonic pulse velocity, electrical resistivity, porosity measurement, and capillary absorption rate. These techniques make it possible to characterize the concrete and obtain information such as the total volume of pores, susceptibility to corrosion of the reinforcing steel, decrease in mechanical resistance, cracks, presence of humidity, and aggressive ions inside the concrete. In this work, two durability studies are presented with non-destructive tests carried out on active piers that are 20 and 40 years old. These are located in coastal areas in southern Mexico on the Gulf of Mexico side, with 80% average annual relative humidity, temperatures above 33 °C on average, high concentrations of salts, load handling, vibrations, flora and fauna typical of the marine ecosystem, etc. The results obtained reveal important information about the current state of the piers and the damage caused by the environment over time. This information allowed us to make decisions on preventive actions and develop appropriate and specific restoration projects for each pier.

After a catastrophic failure of the weld of the anchoring element of one cable in a stayed bridge, a non-destructive inspection was required to evaluate the weld condition of the 111 remaining anchoring elements to prevent future and similar failures. This examination was quite complicated since the anchoring elements are partially embedded in the reinforced concrete tower, and the weld is fully integrated into the concrete. Considering that direct access to the weld was not possible, acoustic emissions (AE) were a feasible alternative for these inspections. This study describes the inspection method, from laboratory tests simulating actual conditions for calibration to field tests for the method's tuning and evaluation. The AE inspection results are presented, and welds’ condition is classified according to the acoustic energy, measured through a severity index and graded from a zonal intensity plot. Two structural elements were selected for concrete demolition to expose the weld for penetrant and ultrasonic inspections to correlate measurements of the actual condition of the welds and their defect size. Because of the analysis, welds are identified for immediate repair and the rest for AE monitoring to evaluate defect evolution through the increase of the severity index.