Abstract In high‐altitude regions characterized by cold climates and complex terrain, the engineering performance of geosynthetic‐reinforced soil slopes requires careful consideration. This paper discusses the design and construction of a reinforced soil slope based on an ultra‐high voltage converter station project in a high‐altitude region and verifies the reliability of the design scheme through cross‐validation using two numerical software programmes. A 40 m high reinforced soil slope was selected for monitoring and analysis of temperature, slope deformation, settlement, deep horizontal displacement, acceleration, earth pressure and geogrid strain. The results indicate that the safety factors of the slope under normal, earthquake and heavy rainfall conditions all satisfy relevant standard requirements. Both the lateral displacement and settlement of the slope surface increased rapidly during construction and then gradually stabilized. Twenty‐six months after completion, the maximum lateral displacement and settlement were 523.41 and 420.30 mm, respectively, both within the allowable range specified in the standards. Soil temperature exhibited a lag relative to ambient temperature, and temperature variations induced periodic daily fluctuations in deformation. Lateral displacement and settlement showed negative and positive correlations with soil temperature, respectively, whereas repeated freeze–thaw cycles led to the gradual accumulation of deformation over time. The vertical earth pressure within the slope decreased gradually with increasing slope height and showed notable deviations from theoretical values due to the shielding effect of the geogrids. Geogrid strain was higher near the slope surface and decreased with increasing slope height, with a maximum strain of 1.8% observed across all layers. The deep horizontal displacement gradually stabilized 1 year after the slope was completed. No seismic events were recorded at the site during the 2‐year period following slope completion.
Abstract This study aims to perform a comprehensive site characterization in the Balıkesir region of north‐western Turkey, with a particular focus on the Bandırma and Erdek districts. Microtremor measurements were carried out at systematically selected locations, and the horizontal‐to‐vertical spectral ratio (HVSR) technique was applied to determine the dominant period and HVSR peak amplitudes ( A 0 ). In addition, V s 30 values and soil classification types were obtained using surface wave analysis and evaluated in accordance with the Building Seismic Safety Council guidelines. Based on these results, seismic microzonation maps were generated using ArcGIS to illustrate the spatial distribution of dominant period, HVSR peak amplitudes ( A 0 ), V s 30 values and soil classes. These maps were then compared to assess the consistency among HVSR peak amplitudes, dominant period, V s 30 values and estimated soil depth. In the Bandırma district, HVSR peak amplitudes ( A 0 ) varied between 0.40 and 8.22, whereas dominant periods ranged from 0.065 s to 2.100 s. In the Erdek district, HVSR peak amplitudes ( A 0 ) ranged between 0.97 and 6.14, with dominant periods varying from 0.051 s to 3.100 s. The results emphasize the spatial variability of local soil conditions and highlight their potential influence on site response characteristics in urbanized areas. This study contributes to future seismic microzonation efforts and provides fundamental data for earthquake risk mitigation, land‐use planning and seismic hazard assessment in the region.
Abstract In studies of subsurface hydrology, monitoring and imaging techniques are used to estimate the moisture content, or to track moisture or injected tracer movement. One applied imaging technique is electrical resistance tomography (ERT), in which measurements collected using electrodes placed on the ground surface or in boreholes are used to reconstruct the subsurface electrical conductivity, or its temporal changes. ERT image reconstructions are, however, very sensitive to measurement noise and especially modelling errors. In geophysical ERT, especially when using borehole electrodes, a significant modelling error may result from uncertain electrode positions. In this work, we study the effects of such modelling errors and apply the non‐linear difference (NLD) imaging and Bayesian approximation error approach to compensate for them. The approach is tested with numerical simulations representing tracer injection experiments into the subsurface. The results show that when electrode positions are uncertain, adopting the NLD imaging approach can yield feasible estimates for the conductivity change from the initial state in cases where the conventional linearized difference and absolute reconstructions are biased. Moreover, the study demonstrates that while the absolute values of the conductivity obtained by NLD imaging can be biased due to electrode position errors, enhancing the NLD reconstruction with Bayesian approximation error modelling improves the reconstruction of the initial conductivity and the temporal change of conductivity, leading to improvement in the absolute conductivity estimates.
Abstract Traditional geotechnical site investigations often depend on invasive borings, which require significant time and resources. In contrast, surface wave methods gather data without disturbing the soil and are faster and more cost‐effective, allowing investigation of more extensive areas. However, these techniques involve an inversion process which necessitates significant computational effort and often leads to multiple solutions. The convergence of the inversion process and the accuracy of its output heavily rely on the initial assumptions about soil layering, including the number of layers, shear‐wave velocity and density. In this study, a machine learning (ML) approach was developed to refine the estimation of the most appropriate number of soil layers for a model. Nine hundred thousand synthetic shear‐wave velocity soil profiles were generated, each with a total thickness ranging from 20 m to 60 m. The number of layers ranges between two and seven, with each layer having a randomly determined thickness and shear‐wave velocity, then converted into Rayleigh and Love dispersion curves and horizontal‐to‐vertical spectral ratio (HVSR) curves for ML training. Three separate tail‐masked gated recurrent units with decay classifiers were then trained for each data type. To determine the best‐performing models, various network architectures with different optimizers were tested, and their confusion matrices and test accuracies were evaluated. The selected models were then used to produce a probability vector indicating the most likely number of layers for each individual Rayleigh dispersion curve, Love dispersion curve or HVSR curve. Based on these probability vectors, the final layer count prediction was made. These models demonstrate satisfactory accuracy when validated against field data, offering a practical way to refine initial assumptions in the inversion process for prediction of the number of model soil layers for the inversion process. All three classifiers are bundled in the soil layer estimator GUI, which visualizes input curves, enables adjustment of per‐model weights and exports Dinver‐compatible parameter files for the inversion setup. This approach provides a versatile tool for improving both the speed and precision of the inversion process for surface wave investigations.
This study concurrently applies multiple cutting‐edge survey techniques, including those based on airborne and shipborne geophysical methods, to investigate buried, potentially archaeological targets in the Taijiang Waters of Taiwan. The Taijiang Waters are archaeologically significant because they served as essential shipping channels in the seventeenth century, when many ships sank in their waters. However, conventional archaeological surveying methods are not applicable due to rapid morphological changes, river currents and poor water visibility. Through integrated analysis of various geophysical data, seven suspected underwater cultural heritage (UCH) targets were identified at burial depths ranging from 0.3 to 5.1 m below the riverbed, with approximate lengths ranging from 1 to 10 m. By comparing the geophysical results with historical records and the regional sedimentation rate of 0.2–0.4 cm/year, inferred from geological dating, our results suggest that the suspected UCH targets are possibly linked to the Dutch East India Company shipwrecks, including the vessels Maarssen , Koudekerke , Valk and Immenhorn , and therefore warrant further archaeological investigation.
