Nowadays, structural health monitoring (SHM) is one of the most critical subjects in geotechnical engineering. All structures (such as buildings and bridges) have a limited life span. Phenomena such as corrosion, fatigue, and excessive loading cause damage to these structures. The main goal of SHM is the timely detection of structural damages during or after a dynamic excitation to prevent destructive damage due to failure. The study investigates the effectiveness of wavelet transform techniques (discrete and continuous approach) as a signal processing-based method to monitor the condition of Pile-soil-superstructure systems. The primary objective is to identify defects in these systems under earthquake excitations. In this way, the first step determined the seismic response of a single pile embedded in one layer of Nevada sand using Abaqus finite element software. In the second step, the ability of acceleration signal processing recorded in different parts of the pile has been evaluated to identify pile defects. In the present study, damage is not simulated in the finite element model. The yielding of the pile under the earthquake record was considered a defect. In this context, the main goal is to detect yielding in the pile cross using acceleration signal processing during earthquake excitations.

The shape, size, and abundance of rocks on the Moon's surface are essential for understanding impact cratering and weathering processes, interpreting remote sensing observations, and ensuring landing safety and rover trafficability. In most previous studies, rock information was extracted from optical images using visual identification or automatic detection methods. However, optical images cannot provide 3-D information on rocks and cannot be used in lunar permanently shadowed regions (PSRs), where rock information is critical to deciphering anomalously high radar echoes in water ice deposit detection. In this study, we proposed an automatic method for extracting 3-D information about rocks from topography data based on the geometry and clustering tendency of lunar surface rocks. A geometric shape model for lunar surface rocks is first developed by analyzing 3196 rocks in elevation data. In the proposed approach, rocks are detected from topography data using multiscale 2-D continuous wavelet transform (2-D CWT) and Hopkins statistic, and then a 3-D shape parameter extraction method is introduced to obtain the shape information directly from the detected irregular rock boundary by a region growing-based algorithm. To demonstrate the accuracy of the method, we applied the proposed method to both the simulated and real-topography data with various spatial resolutions and vertical uncertainties. The results show that, compared with the ground truth and manual detection results, the detection rate of rocks >4 pixels in size varies from 50% to 90%, depending mainly on the vertical uncertainty of elevation data. In addition, for the first time, we provide 3-D information on surface rocks (>10 m) in lunar PSRs from topography data. Our analyses suggest that, for future missions to the lunar PSRs (e.g., China's Chang'E-7), the vertical uncertainty of elevation data needs to be better than 0.2 m in order to accurately gather 3-D information of rocks larger than 2 m. Our method can be utilized for extracting 3-D information on rocks from topography data, selecting landing sites, and guiding instrument design for future altimeters.