Earthquakes are common geological disasters, and slopes under seismic loading can trigger coseismic landslides, while also becoming unstable due to accumulated damage caused by the seismic activity. Reinforced soil slopes are widely used as seismic-resistant geotechnical systems. However, traditional geosynthetics cannot sense internal damage in reinforced soil systems, and existing in-situ distributed monitoring technologies are not suitable for seismic conditions, thus limiting accurate post-earthquake stability assessments of slopes. This study presents, for the first time, the use of a batch molding process to fabricate self-sensing piezoelectric geogrids (SPGG) for distributed monitoring of soil behavior under seismic conditions. The SPGG's reinforcement and damage sensing abilities were verified through model experiments. Results show that SPGG significantly enhances soil seismic resistance and can detect soil failure locations through voltage distortions. Additionally, the tensile deformation of the reinforcement material can be quantified with sub-centimeter precision by tracking impedance changes, enabling high-precision distributed monitoring of reinforced soil under seismic conditions. Notably, when integrated with wireless transmission technology, the SPGG-based monitoring system offers a promising solution for real-time monitoring and early warning in road infrastructure, where rapid detection and response to seismic hazards are critical for mitigating catastrophic outcomes.

Seismic fragility denotes the probabilities of a system exceeding some prescribed damage levels under a range of seismic intensities. Classical seismic fragility studies in slope engineering usually construct fragility functions by making some assumptions for fragility curve shape, and always neglect spatial variability of soil materials. In this study, an assumption-free method on the basis of probability density evolution theory (PDET) is proposed for seismic fragility assessment of slopes. The random input earthquakes and spatially-variable soil parameters in slope are simultaneously quantified. By the proposed method, assumption-free fragility curves of a slope are established without limiting the fragility curve shape. The obtained fragility results are also compared with those from two classic parametric fragility methods (linear regression and maximum likelihood estimation) and Monte Carlo simulation. The results demonstrate that the proposed assumption-free method has potential to gives more rigorous and accurate fragility results than classical parametric fragility analysis methods. With the proposed method, more reliable fragility results can be obtained for slope seismic risk assessment.

A novel thermo-hydro-mechanical-chemical (THMC) coupling model grounded in thermodynamic dissipation theory was established to unravel the intricate behavior of unsaturated sulfate-saline soils during cooling crystallization. The model quantifies energy transfer and dissipation during crystallization and introduces a method to calculate the amount of sulfate crystallization. It intricately captures the interdependencies between crystallization, pore water pressure, crystallization pressure and volumetric expansion, while also accounting for the dynamic feedback of latent heat from phase transitions on heat conduction. The reliability of the model was validated through experimental data. Numerical simulations explored the effects of cooling paths, thermal conductivity, initial salt content and initial porosity on the crystallization behavior and mechanical properties. The model provides theoretical support for optimizing the engineering design and facility maintenance of sulfatesaline soils.

Current practice to model the occurrence of submarine landslides is based on methods that assess the potential of site-specific failures, all with the objective of providing elements to identify and quantify regional features associated to geohazards, before a project development takes place. Also, survey data to estimate parameters required to model submarine landslides show typically limited availability, mainly because of the cost associated to offshore surveying campaigns. In this paper, a probabilistic calibration approach is introduced using Bayesian statistical inference to maximize the use of available site investigation data, and to best estimate the occurrence of a marine landslide. For this purpose, a landslide model thought for its simplicity is used to illustrate the applicability and potential of the calibration methodology. The aim is to introduce a systematic approach to produce prior probability distributions of the model parameters, based on an actual integrated marine site investigation including geological, geophysical, and geomatics data, to then compare it with a posterior probability distribution of the same model parameters, but estimated after collecting in situ soil samples and testing them in the laboratory to produce the corresponding soil strength properties. This comparison allows to explore (a) the influence of the number of in situ samples, (b) the influence of a landslide factor of safety, and (c) the influence of the soil heterogeneity, into the likelihood of the occurrence of a marine landslide. The model parameters that are considered for calibration include the initial state of the submerged and saturated soil unit weight, the thickness of the soils' unit layers, the pseudo-static seismic coefficient, and the slope angle, while the soil undrained shear strength is considered as the reference parameter to conduct the calibration (i.e., to compare model predictions vs. actual observations). Results show the potential of the proposed methodology to produce landslide geohazard maps, which are needed for the planning and design of marine infrastructure.

Most Australian vegetable growers apply fumigants or nematicides as a precautionary nematode control measure when crops susceptible to root-knot nematode (RKN, Meloidogyne spp.) are grown in soils and environmental conditions suitable for the nematode. The only way growers can make rational decisions on whether these expensive and environmentally disruptive chemicals are required is to regularly monitor RKN populations and decide whether numbers prior to planting are high enough to cause economic damage. However, such monitoring programs are difficult to implement because nematode quantification methods vary in efficiency and the damage threshold for RKN on highly susceptible vegetable crops is often < 10 root-knot nematodes /200 mL soil. Consequently, five nematode quantification methods were tested to see whether they could reliably detect these very low population densities of RKN. Two novel methods produced consistent results: 1) extracting nematodes from 2 L soil samples using Whitehead trays, quantifying the RKN DNA in the nematode suspension using molecular methods, and generating a standard curve so that the molecular results provided an estimate of the total number of RKN individuals in the sample, and 2) a bioassay in which two tomato seedlings were planted in pots containing 2 L soil and the number of galls produced on roots were counted after 21-25 days. Both methods could be used to quantify low populations of RKN, but bioassays are more practical because expensive equipment and facilities are not required and they can be done at a local level by people lacking nematological or molecular skills.

Earthen sites, such as the Great Wall of China, are important elements of cultural heritage, but are at high risk of erosion due to environmental changes. In this study, unmanned aerial vehicle low-altitude oblique photography was used to assess the erosion of the Ming Great Wall in Gansu Province. The erosion characteristics (height, depth, area, and ratio) were quantified using a 3D point-cloud model. Combined with onsite sampling and analysis, the deterioration distribution was examined, and the progression of damage summarised using historical images. The degree of erosion in the rammed earth Great Wall was linked to the soluble salt content in the soil. The degree of deterioration of the walls indicates a significantly larger hollowing area on the southern side than on the northern side, and a slightly larger area on the western side than on the eastern side. This paper addresses the challenges of assessing and quantifying erosion development in specific segments and provides a risk assessment of erosion at any point in each segment. It also provides a valuable reference and scientific support for the protection and restoration projects of the Great Wall during the Ming period.

Accurate estimation of soil properties is crucial for reliability-based design in engineering practices. Conventional empirical equations and prevalent data-driven models rarely consider uncertainty quantification in both measurement and modelling processes. This study tailors three uncertainty quantification methods including Bayesian learning, Markov chain Monte Carlo and ensemble learning into data-driven modelling, in which support vector regression is selected as the baseline algorithm. The compression index of clay is adopted as an example for model training and testing. In this context, Bayesian learning and Markov chain quantify uncertainty by considering the distribution of function and hyper-parameters, respectively, while different sampled data are employed to explore model uncertainty. These models are evaluated in terms of accuracy, reliability and cost-effectiveness and also compared with Gaussian process regression, etc. The results reveal that based on built-in structural risk minimization, sparse solution and uncertainty quantification, developed models can capture more accurate and reliable correlations from actual measured data over other methods. Their practicability and generalization ability are also verified on a new creep index database. The proposed probabilistic methods are also compiled into a user-friendly platform, showing a significant potential to enrich the data-driven modelling framework and be applied in other geotechnical properties.

The structural integrity of buried pipelines is threatened by the effects of Permanent Ground Deformation (PGD), resulting from seismic-induced landslides and lateral spreading due to liquefaction, requiring accurate analysis of the system performance. Analytical fragility functions allow us to estimate the likelihood of seismic damage along the pipeline, supporting design engineers and network operators in prioritizing resource allocation for mitigative or remedial measures in spatially distributed lifeline systems. To efficiently and accurately evaluate the seismic fragility of a buried operating steel pipeline under longitudinal PGD, this study develops a new analytical model, accounting for the asymmetric pipeline behavior in tension and compression under varying operational loads. This validated model is further implemented within a fragility function calculation framework based on the Monte Carlo Simulation (MCS), allowing us to efficiently assess the probability of the pipeline exceeding the performance limit states, conditioned to the PGD demand. The evaluated fragility surfaces showed that the probability of the pipeline exceeding the performance criteria increases for larger soil displacements and lengths, as well as cover depths, because of the greater mobilized soil reaction counteracting the pipeline deformation. The performed Global Sensitivity Analysis (GSA) highlighted the influence of the PGD and soil-pipeline interaction parameters, as well as the effect of the service loads on structural performance, requiring proper consideration in pipeline system modeling and design. Overall, the proposed analytical fragility function calculation framework provides a useful methodology for effectively assessing the performance of operating pipelines under longitudinal PGD, quantifying the effect of the uncertain parameters impacting system response.

In the context of climate change, rainstorm events are becoming increasingly frequent. In particular, on the Loess Plateau, heavy rainstorms are the primary cause of soil erosion. This study investigated and analysed different types of soil erosion hotspots and influencing factors in small watersheds under different rainstorm events in different areas of the Loess Plateau. The results indicate that the erosion intensities of rills, gullies, landslides and collapses ranged from 13600-46244, 1982-772201, 1163-172153 t km-2 and 1867-94985 t km-2, respectively. Newly constructed terraces exhibited an erosion intensity 1.6 times greater than that of old terraces, while terraces constructed before the rainy season in the current year exhibited an erosion damage intensity 2.6 times greater than that of terraces constructed after the rainy season in the previous year. In addition, under rainstorm conditions, landslides represented the most severe type of erosion in the watersheds, with the maximum amount of erosion accounting for more than 90 % of the total erosion amount, followed by gully or collapse erosion, with the collapse of terrace risers as the main contributor. Slope cultivation land, unpaved roads, terrace risers, and valley slopes below the gully shoulder line were identified as erosion hotspot areas. Rainstorm erosion was significantly influenced by the land use type and slope, which explained 14.2 %-41.5 % and 9.7 %-15.1 %, respectively, of the total variance in erosion intensity. We suggest that soil erosion prevention and control efforts on the Loess Plateau should focus on landslides on valley slopes below gully shoulder lines, followed by gullies on unpaved roads and the collapse of terraced fields. Drainage ditches and water cellars should be constructed above the gully shoulder line and on the inside of roads and terraces, thereby reducing erosion. Our research is crucial for optimizing and adjusting watershed management measures and preventing rainstorm erosion disasters.

Probabilistic seismic performance assessments of engineered structures can be highly sensitive to the seismic input excitation and its variability. In the present study, the scenario-based performance assessment recommended by Federal Emergency Management Agency (FEMA) P-58 guidelines is adopted to estimate seismic fragility of concrete dams for various seismic hazard scenarios. Due to the scarcity of recorded ground motions and thereby their poor representation of uncertainties, stochastic ground motion simulation methods are utilized to obtain the required input excitations. Moreover, to understand the uncertainty in ground motion simulation models, two broadband stochastic simulation models are used to generate input excitations representing six seismic hazard scenarios defined by earthquake magnitude, source-to-site distance, and soil conditions. Optimal intensity measure parameters for each scenario are identified using a systematic procedure that considers criteria such as efficiency, practicality, proficiency, sufficiency, and hazard compatibility. Fragility curves and surfaces are derived using the cloud analysis technique, taking into account various damage measures and limit state functions. The study finds that the derived fragility curves are particularly sensitive to the selection of earthquake scenarios, the choice of records, and the methods used to calculate fragility curves, with less sensitivity observed to different engineering demand parameters. Given this sensitivity, particularly to ground motion selection, the study highlights the necessity of incorporating both model-to- model variability (epistemic uncertainty) and record-to-record variability (aleatory uncertainty), alongside the established material and modeling uncertainties, in the probabilistic seismic assessment.