There are only a few worldwide cases where distant earthquakes have caused damage. One such example is the municipality of Centro located in Tabasco, Southeast Mexico, approximately 360 km from the Mesoamerican trench, where a strong ground shaking was felt during the M(w)8.2 earthquake of September 08, 2017. In this study, for 20 sites shear-wave velocity profiles were determined using Multichannel Analysis of Surface Wave and V-P profiles using Seismic Refraction techniques. Also V-S30 (shear-wave velocity up to a depth of 30 m) values were obtained for the same sites. The distribution of the V-S30 values in the study area varied from 120 m/s to 570 m/s and it was observed that sites where damage to buildings were reported lie near areas with V-S30 < 270 m/s. Additionally, the transfer functions of the 20 sites were estimated using the Thomson-Haskell method. The fundamental frequencies (f(0)) obtained through transfer functions had values varying from 0.9 <= f(0) <= 2.0 Hz. These transfer functions were convolved with the signal that represents the record in the bottom of the soil column in the study area to obtain synthetic accelerograms in the municipality of Centro. The only accelerograph station located in the study area (VHSA station) was used as a reference site. The horizontal-to-vertical spectral ratio of the VHSA location was used for site characterization to assess the effects of regional events. The study concludes that several factors contribute to the susceptibility of Centro municipality to distant seismic events. These factors include low shear-wave velocity (V-s), low fundamental frequency (f(0)), local site conditions, the presence of buildings on former lake zones, low seismic wave attenuation, and the regions' overall vulnerability to regional earthquakes.

Precompression stress, compression index, and swelling index are used for characterizing the compressive behavior of soils, and are essential soil properties for establishing decision support tools to reduce the risk of soil compaction. Because measurements are time-consuming, soil compressive properties are often derived through pedotransfer functions. This study aimed to develop a comprehensive database of soil compressive properties with additional information on basic soil properties, site characteristics, and methodological aspects sourced from peer-reviewed literature, and to develop random forest models for predicting precompression stress using various subsets of the database. Our analysis illustrates that soil compressive properties data primarily originate from a limited number of countries. There is a predominance of precompression stress data, while little data on compression index or recompression index are available. Most precompression stress data were derived from the topsoils of conventionally tilled arable fields, which is not compatible with knowledge that subsoil compaction is a serious problem. The data compilation unveiled considerable variations in soil compression test procedures and methods for calculating precompression stress across different studies, and a concentration of data at soil moisture conditions at or above field capacity. The random forest models exhibited unsatisfactory predictive performance although they performed better than previously developed models. Models showed slight improvement in predictive power when the underlying data were restricted to a specific precompression stress calculation method. Although our database offers broader coverage of precompression stress data than previous studies, the lack of standardization in methodological procedures complicates the development of predictive models based on combined datasets. Methodological standardization and/or functions to translate results between methodologies are needed to ensure consistency and enable data comparison, to develop robust models for precompression stress predictions. Moreover, data across a wider range of soil moisture conditions are needed to characterize soil mechanical properties as a function of soil moisture, similar to soil hydraulic functions, and to develop models to predict the parameters of such soil mechanical functions.

Sample collection and measurement of soil bulk density (BD) are often labor-intensive and expensive in large regions. Conversely, soil spectra are easy to measure and facilitate BD prediction. However, the literature suggests that the damage to the physical structure of soil during scanning spectra on the ground and/or sieved samples might hinder the capacity of spectral technology to accurately predict BD. In addition, because some soil properties that have high correlations with BD, such as the soil organic matter (SOM), are routinely measured and available in most soil databases, coupling them with soil spectra may improve BD prediction compared to using soil properties or spectra. Therefore, in this study, we propose a novel spectral pedo-transfer function (spectral PTF) that couples the measured visible and near-infrared spectra of soils on intact samples and other soil properties to accurately predict the BD (BD = f (soil spectra, soil properties)), which is different from the traditional PTF that uses only soil properties (BD = f (soil properties)) or spectra alone (BD = f (soil spectra)). In this study, we collected topsoil (0-20 cm) and subsoil (20-40 cm) samples from 586 sites in Northeast China, covering a large area of 1.09 million km(2) characterized by black soils with high SOM contents. Five routinely measured soil properties were selected: SOM, moisture content (MC), Sand, Silt, and Clay, and various spectral PTFs with one, two, and three soil properties were calibrated using the partial least square regression. The cross-validation results show that the traditional PTF can only predict BD for subsoil with an R-2 of 0.51 and an RMSE of 0.11 g center dot cm(-3) when using SOM + MC + Silt or SOM + MC. Compared to subsoil, topsoil and all layers (topsoil + subsoil) had a lower BD prediction accuracy, and a saturation effect was observed for BD values above 1.5 g center dot cm(-3). Unexpectedly, the soil spectra did not provide a higher BD prediction accuracy than traditional PTFs, although the spectra were measured on intact samples. However, adding soil properties to the spectral PTF improved the prediction accuracy and saturation effect for high BD values. The optimal spectral PTF with a single soil property (MC) showed an acceptable BD prediction performance with R-2 >= 0.49, RPD>1.4, and RPIQ>1.7 regardless of whether the sample was topsoil, subsoil, or all layers. Furthermore, the spectral PTF with two or three soil properties yielded a slightly better prediction performance and a more stable prediction among different combinations of soil properties. These results indicate that soil properties and spectra are irreplaceable for BD prediction. Our study demonstrates the potential of spectral PTFs for the accurate prediction of BD and offers insights into the prediction of other soil properties using soil spectra.

South Korea has implemented borehole -type seismometers for reliable earthquake observations and earthquake early -warning systems, with approximately 85% of seismometers being replaced by borehole -type seismometers after the Gyeongju earthquake. Although these seismometers are more effective at detecting earthquakes owing to the reduced artificial ambient noise, they do not record surface -level shaking. Therefore, it is necessary to estimate ground surface shaking directly associated with potential damage when using borehole -type seismometers without surface sensors. This study investigated and compared various methods, including the stochastic point -source ground -motion model, transfer function based on ambient noise, and one-dimensional site response, to estimate horizontal seismograms of the ground surface. We assessed the accuracy of these methods by comparing the waveforms generated in event cases (magnitude from 2.5 to 5.8, with epicentral distances spanning 22 km - 209 km) in terms of Fourier spectra, intensity, and spectral acceleration. Among the methods assessed, the transfer function approach, which does not account for the geophysical characteristics such as V S 30 , proved to be the most appropriate for correcting ground -surface effects.

Pavement design in cold regions is challenging due to the difficult conditions of soils, humidity, and temperatures. Insulation layers have been identified as a suitable solution for these conditions. Due to their unique engineering properties, foam glass aggregates (FGAs) are a promising material for use as an insulating granular layer in pavement design. However, understanding their mechanical performance is critical for predicting longterm layer and pavement behavior. In this laboratory study, an empirical transfer function was developed using an environmental and heavy vehicle simulator and an experimental pavement built in an indoor test pit. The study aimed to determine the allowable number of load repetitions for an FGAs insulation layer and to develop an empirical transfer function that can be used as part of a mechanistic-empirical pavement design procedure. This article proposes a linear relationship between permanent deformation, the number of load cycles, and the equivalency factor between the effect of resilient strain, or vertical stress, and allowable damage. The proposed empirical transfer functions allow defining an allowable number of load repetitions for a characteristic resilient strain or vertical stress and an allowable damage. The allowable damage can be modulated with respect to road classification, and a damage value of 0 % to FGAs layer can be considered as a safety factor. The findings of this study provide valuable insights into the use of FGAs as an insulating granular layer in pavement design in cold regions.

Evaluating soil nonlinearity during cyclic loading is one of the most significant challenges in ground response analysis, especially when dealing with the inverse problem of deconvolution. Different schemes have already been developed for dynamic ground response analysis, both in the time and the frequency domain. The most accurate method to account for soil nonlinearity is the nonlinear dynamic analysis in the time domain. This approach is based on nonlinear constitutive models capable of accurately simulating highly nonlinear problems like soil liquefaction. However, the time-domain analysis is suitable only for the convolution analysis to define the ground motion at the free surface of a soil deposit from the bedrock motion. The frequency-domain analysis is the most common solution for the inverse problem called deconvolution, which is used to define the bedrock motion from the free surface ground motion. A well-known approach developed in the frequency domain for ground response analysis is the equivalent-linear method (EQL). This approach adopts an iterative procedure to define elastic shear modulus and damping ratio compatible with the induced strain level. Still, it presents some limitations, especially for highly nonlinear soil response, due to the use of strain-compatible but constant soil properties. This article presents a new scheme to conduct truly nonlinear dynamic analysis in the frequency domain based on the new concept of the short-time transfer function. Unlike the EQL method, which uses a constant transfer function, the proposed approach, called the Equivalent-Nonlinear method (EQNL), defines a soil transfer function evolving in time, depending on the shear stress and strain demands. The EQNL method approximates the response of a nonlinear system as an incrementally changing viscoelastic system and could represent a valuable tool for nonlinear deconvolution. This article shows the analytical formulation and the first set of validations of the EQNL approach, with detailed comparisons with the EQL and NL methods and vertical array data. These comparisons show the potentialities of the EQNL approach to reproduce the results of the nonlinear dynamic analysis. The EQNL approach has been implemented in MATLAB, and the source code is provided as supplementary material for this article. A more comprehensive validation is underway, aiming to better characterize the limitations and the capabilities of the method.

Soil corrosivity is a term used to describe the corroding susceptibility (risk) of metal infrastructure in different soil environments. Soil corrosivity mapping is a crucial step in identifying potentially problematic, high-maintenance fence lines and can help improve fence longevity by identifying soil environments where the use of more expensive, corrosion-resistant materials would be more cost-effective in the long term. Soil corrosion damage sustained on exclusion fences can be a serious management issue for conservation programs and initiatives, as it weakens the fence netting and provides opportunities for invasive animal migration and occupation (e.g. feral cats and foxes) into areas of high conservation value. The increasing accessibility of geospatial analysis software and the availability of open-source soil data provide land managers with the opportunity to implement digital soil databases and pedotransfer functions to produce fence corrosion risk maps using commonly measured soil attributes. This paper uses open-source government agency soil data (shapefiles) to map fence corrosion risk in the southern part of the Yorke Peninsula in South Australia, with the intention to assist with the installation of a new barrier (exclusion) fence as part of the Marna Banggara rewilding project. The risk classifications (low, moderate and high risk) made by this map were compared with rates of zinc corrosion (mu m/year zinc loss) observed at field sites and correctly predicted the amount of fence damage sustained at five of the eight sites. The mapping approach outlined in this study can be implemented by environmental managers in other areas to inform strategies for enhancing fence longevity.

This study investigates the response mechanisms between soil water-heat transfer and environmental factors during freeze-thaw periods and establishes soil water-heat transfer functions in a cold region. Based on field-measured values of soil temperature and liquid-phase water content collected at an automatic weather station in the black soil area of the Songnen plain, the influence of the cumulative negative temperature on the soil freezing depth was analyzed under different snow cover conditions. A gray correlation analysis method was used to screen the environmental factors and determine those with the most influence on changes in soil water-heat transfer processes. Then, soil water-heat transfer functions were established between the selected environmental factors and soil temperature, the liquid-phase soil water content. The results showed that during the freezing and thawing period, snow cover hindered the effects of the cumulative temperature on the thickness of the frozen soil layer. Additionally, the time of occurrence of the maximum freezing depth under natural snow (NS), compacted snow (CS) and thickened snow (TS) treatments was delayed 7, 12 and 20 days, respectively, compared with that under bare land (BL). The correlation between atmospheric temperature, total radiation and soil temperature was relatively high, and this effect decreased with the increasing of snow cover. The main driving factors of variations in the liquid-phase water content were ambient humidity and saturated vapor pressure, and the effects of these factors decreased with increasing soil depth and snow cover thickness, similarly. In the active frozen layer, the correlation coefficients of the soil water-heat transfer functions were relatively high, and the function model can be tested by the significance (P < 0.05) test. However, the R-2 values of functions below the active layer were relatively low, and the soil water-heat transfer in the area below the active layer was less affected by the environment. This study reveals the characteristics of energy transfer and mass transfer in a composite system of atmospheric factors and frozen soil under snow cover conditions. It provides a reference for accurate forecasting and the efficient utilization of soil water and heat resources in cold and arid regions.