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.

This study introduces three types of multivariable fragility surfaces, integrating effective structural features to improve damage assessment. The incorporation of additional information such as building occupancies, structural responses, and underlying soil types enhances the accuracy of conventional fragility curve predictions. Additionally, three modification factors are proposed to further refine conventional fragility curves and provide more precise predictions. The multivariable fragility surfaces are developed for eccentric brace frames modeled in Opensees software which is validated by experimental results and subjected to incremental dynamic analysis with 44 far-field ground motions. The influence of soil flexibilities on structural responses is incorporated through Winkler springs, representing soil-structure interaction. Diverse occupancies, such as hospitals, museums, and residential structures, are assessed using various peak floor acceleration thresholds and story drift ratios, employing multidimensional limit state functions to consider both structural and nonstructural losses. To account for uncertainties in structural responses and a single intensity measurement, a damage-sensitive feature derived from roof acceleration response, obtained through signal processing and system identification techniques, is introduced. The results for the proposed multivariable fragility surfaces indicate that the spectral acceleration corresponding to a 50% probability of exceedance could vary between 10.2 and 89%, in comparison to the corresponding conventional fragility curves. Finally, to evaluate the application of the enhanced fragility surface and modification factors, two instrumented EBF buildings, a 4-story EBF building, and a real 5-story hospital EBF, are selected as case studies. With additional details on soil types, occupancies, and structural responses, the process of employing modification factors resulting in enhanced fragility curves is demonstrated.

Tree root systems are crucial for providing structural support and stability to trees. However, in urban environments, they can pose challenges due to potential conflicts with the foundations of roads and infrastructure, leading to significant damage. Therefore, there is a pressing need to investigate the subsurface tree root system architecture (RSA). Ground-penetrating radar (GPR) has emerged as a powerful tool for this purpose, offering high-resolution and nondestructive testing (NDT) capabilities. One of the primary challenges in enhancing GPR's ability to detect roots lies in accurately reconstructing the 3-D structure of complex RSAs. This challenge is exacerbated by subsurface heterogeneity and intricate interlacement of root branches, which can result in erroneous stacking of 2-D root points during 3-D reconstruction. This study introduces a novel approach using our developed wheel-based dual-polarized GPR system capable of capturing four polarimetric scattering parameters at each scan point through automated zigzag movements. A dedicated radar signal processing framework analyzes these dual-polarized signals to extract essential root parameters. These parameters are then used in an optimized slice relation clustering (OSRC) algorithm, specifically designed for improving the reconstruction of complex RSA. The efficacy of integrating root parameters derived from dual-polarized GPR signals into the OSRC algorithm is initially evaluated through simulations to assess its capability in RSA reconstruction. Subsequently, the GPR system and processing methodology are validated under real-world conditions using natural Angsana tree root systems. The findings demonstrate a promising methodology for enhancing the accurate reconstruction of intricate 3-D tree RSA structures.