A potential microbiological approach to the evaluation of earthquake-induced soil liquefaction.
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ABSTRACT:
SUBMITTER: Chao YT
PROVIDER: S-EPMC8403730 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Earthquake-induced soil-liquefaction is a devastating phenomenon associated with loss of soil rigidity due to seismic shaking, resulting in catastrophic liquid-like soil deformation. Traditionally, liquefaction is viewed as an effectively undrained process. However, since undrained liquefaction only initiates under high energy density, most earthquake liquefaction events remain unexplained, since they initiate far from the earthquake epicenter, under low energy density. Here we show that liquefaction can occur under drained conditions at remarkably low seismic-energy density, offering a general explanation for earthquake far-field liquefaction. Drained conditions promote interstitial fluid flow across the soil during earthquakes, leading to excess pore pressure gradients and loss of soil strength. Drained liquefaction is triggered rapidly and controlled by a propagating compaction front, whose velocity depends on the seismic-energy injection rate. Our findings highlight the importance of considering soil liquefaction under a spectrum of drainage conditions, with critical implications for liquefaction potential assessments and hazards.
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Project description:Many factors impact earthquake-induced liquefaction, and there are complex interactions between them. Therefore, rationally identifying the key factors and clarifying their direct and indirect effects on liquefaction help to reduce the complexity of the predictive model and improve its predictive performance. This information can also help researchers understand the liquefaction phenomenon more clearly. In this paper, based on a shear wave velocity (Vs) database, 12 key factors are quantitatively identified using a correlation analysis and the maximum information coefficient (MIC) method. Subsequently, the regression method combined with the MIC method is used to construct a multiple causal path model without any assumptions based on the key factors for clarifying their direct and mediation effects on liquefaction. The results show that earthquake parameters produce more important influences on the occurrence of liquefaction than soil properties and site conditions, whereas deposit type, soil type, and deposit age produce relatively small impacts on liquefaction. In the multiple causal path model, the influence path of each factor on liquefaction becomes very clear. Among the key factors, in addition to the duration of the earthquake and Vs, other factors possess multiple mediation paths that affect liquefaction; the thickness of the critical layer and thickness of the unsaturated zone between the groundwater table and capping layer are two indirect-only mediators, and the fines content and thickness of the impermeable capping layer induce suppressive effects on liquefaction. In addition, the constructed causal model can provide a logistic regression model and a structure of the Bayesian network for predicting liquefaction. Five-fold cross-validation is used to compare and verify their predictive performances.
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Project description:The data presented in this paper pertain to case records of liquefaction potential surveys in earthquake prone areas. Field performances of 219 sites obtained from various regions (U.S.A, Japan, Turkey, China, Canada, etc …) are put on display. Specifically, this database consists of 253 cone penetration test (CPT) field records, among which 72 cases that did not liquefied and 181 cases that liquefied. In total, 10 principal variables are tabulated including the earthquake magnitude, maximum ground surface acceleration, depth, water depth, total overburden stress, effective overburden stress, Cone Penetration Test (CPT) tip resistance, CPT friction ratio, fines content, shear stress ratio. These data were arbitrarily split into a testing set of 53 cases and a training set of 200 cases. These field observations are compared to prediction values of liquefaction potential assessed using the evolutionary neural network proposed for "Evaluation of soil liquefaction with AI technology incorporating a coupled ENN/t-SNE model" [1].
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Project description:Replant syndrome (RS) is a global problem characterized by reduced growth, production life, and yields of tree fruit/nut orchards. RS etiology is unclear, but repeated monoculture plantings are thought to develop a pathogenic soil microbiome. This study aimed to evaluate a biological approach that could reduce RS in peach (Prunus persica) orchards by developing a healthy soil bacteriome. Soil disinfection via autoclave followed by cover cropping and cover crop incorporation was found to distinctly alter the peach soil bacteriome but did not affect the RS etiology of RS-susceptible 'Lovell' peach seedlings. In contrast, non-autoclaved soil followed by cover cropping and incorporation altered the soil bacteriome to a lesser degree than autoclaving but induced significant peach growth. Non-autoclaved and autoclaved soil bacteriomes were compared to highlight bacterial taxa promoted by soil disinfection prior to growing peaches. Differential abundance shows a loss of potentially beneficial bacteria due to soil disinfection. The treatment with the highest peach biomass was non-autoclaved soil with a cover crop history of alfalfa, corn, and tomato. Beneficial bacterial species that were cultivated exclusively in the peach rhizosphere of non-autoclaved soils with a cover crop history were Paenibacillus castaneae and Bellilinea caldifistulae. In summary, the non-autoclaved soils show continuous enhancement of beneficial bacteria at each cropping phase, culminating in an enriched rhizosphere which may help alleviate RS in peaches.
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Project description:This data article provides a summary of seismic soil liquefaction triggering and non-triggering case histories, which were compiled, screened for data completeness and quality, and then processed for the development of triggering relationships proposed in "SPT-based probabilistic and deterministic assessment of seismic soil liquefaction triggering hazard" [1]. The database is composed of 113 liquefaction, 95 non-liquefaction, and 2 marginal liquefaction case histories, from seismic events with moment magnitude Mw values varying in the range of 5.9 to 8.3. A spreadsheet summary of these case histories are included along with a separate spreadsheet, by which maximum likelihood assessment was performed. These data transparently enable researchers to access case history input parameters and processing details, and to compare the case history processing protocols with the ones of different researchers (e.g.: "The influence of SPT procedures in soil liquefaction resistance evaluations." [2], "SPT-based liquefaction triggering procedures." [3]).
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Project description:This data article provides summarized information on the liquefaction case histories of gravelly soil from 17 earthquakes. 234 historical cases based on the shear wave velocity test and dynamic penetration test are carefully compiled and corrected according to the data processing procedures recommended in the related research article [1]. All necessary information that helps develop the corresponding deterministic or probabilistic prediction models for gravelly soil liquefaction, including earthquake name and time, site name and its location, moment magnitude (Mw), peak ground acceleration, epicentral distance, bracketed duration, fines content, gravel content, average particle size, normalized dynamic penetration test blow count or normalized shear wave velocity, groundwater table, depth of gravelly soil deposit, total and effective overburden stress, the thickness of the impermeable capping layer, the thickness of the unsaturated zone between the groundwater table and capping layer, cyclic stress ratio for Mw = 7.5, and liquefaction information, are given for the datasets.
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Project description:This Data in Brief article provides summarized information of the liquefaction case histories from the 12 May 2008 M w7.9 Wenchuan earthquake. According to the data processing procedures recommended in the related research article, all eighty-one case histories investigated by the present authors and seven other case histories from the literature are carefully compiled. All necessary information and the mean and standard deviation for some key parameters are given for these liquefaction case histories (i.e., site name, site location, site inclination angle, liquefaction manifestations, critical layer, soil sampling information, ground motion, total and effective stresses, shear-mass participation parameter, cyclic stress ratio (CSR) for M w=7.5, normalized shear wave velocity (V s1), data class and field testing time). These data may be used by colleagues to study the effect of gravel content on liquefaction behaviour of gravelly soils and develop the corresponding deterministic or probabilistic methods for liquefaction triggering evaluation.
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Project description:The pollution of arable lands and water with petroleum-derived products is still a valid problem, mainly due the extensive works aimed to improve their production technology to reduce fuel consumption and protect engines. An example of the upgraded fuels is the BP 98 unleaded petrol with Active technology. A pot experiment was carried out in which Eutric Cambisol soil was polluted with petrol to determine its effect on the microbiological and biochemical properties of this soil. Analyses were carried out to determine soil microbiome composition-with the incubation and metagenomic methods, the activity of seven enzymes, and cocksfoot effect on hydrocarbon degradation. The following indices were determined: colony development index (CD); ecophysiological diversity index (EP); index of cocksfoot effect on soil microorganisms and enzymes (IFG); index of petrol effect on soil microorganisms and enzymes (IFP); index of the resistance of microorganisms, enzymes, and cocksfoot to soil pollution with petrol (RS); Shannon-Weaver's index of bacterial taxa diversity (H); and Shannon-Weaver's index of hydrocarbon degradation (IDH). The soil pollution with petrol was found to increase population numbers of bacteria and fungi, and Protebacteria phylum abundance as well as to decrease the abundance of Actinobacteria and Acidobacteria phyla. The cultivation of cocksfoot on the petrol-polluted soil had an especially beneficial effect mainly on the bacteria belonging to the Ramlibacter, Pseudoxanthomonas, Mycoplana, and Sphingobium genera. The least susceptible to the soil pollution with petrol and cocksfoot cultivation were the bacteria of the following genera: Kaistobacter, Rhodoplanes, Bacillus, Streptomyces, Paenibacillus, Phenylobacterium, and Terracoccus. Cocksfoot proved effective in the phytoremediation of petrol-polluted soil, as it accelerated hydrocarbon degradation and increased the genetic diversity of bacteria. It additionally enhanced the activities of soil enzymes.
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Project description:The 6.6 Mw Iburi-Tobu earthquake struck southern Hokkaido, Japan on 6 September 2018. The earthquake triggered widespread slope collapses in the hills near the epicenter, resulting in destructive landslides that killed 36 people. Volcanic deposits covering the region slid downhill in a flow-like manner suggestive of fluidized landslides. Here, we report a distinctive example of liquefaction in the field, which could be a prerequisite for the generation of fluidized landslides triggered by large earthquakes. In the scarp of a typical landslide, an altered halloysite-bearing volcanic layer is observed at a level almost coincident with the sliding surface. The layer is intensely undulating and can be divided into an upper clay-rich layer and a lower pumice-rich layer, suggesting that the altered layer had liquefied as a result of the strong coseismic ground motion. The layer had been soaked by heavy rainfall just one day before the earthquake and could have liquefied, producing a weak and slippery plane, resulting in the catastrophic landslides in this area.
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Project description:The regional nature of liquefaction records and limited information available for a certain set of explanatories motivate the development of complex prediction techniques. Indirect methods are commonly applied to incidentally derive a hyperplane to this binary classification problem. Machine learning approaches offer evolutionary prediction models which can be used as direct prediction methods to liquefaction occurrence. Ensemble learning is a recent advancement in this field. According to a predefined ensemble architecture, a number of learners are trained and their inferences are integrated to produce stable and improved generalization ability. However, there is a need to consider several aspects of the ensemble learning frameworks when exploiting them for a particular application; a comprehensive evaluation of an ensemble learner's generalization ability is required but usually overlooked. Also, the literature falls short on work utilizing ensemble learning in liquefaction prediction. To this extent, this work examines useful ensemble learning approaches for seismic-induced liquefaction prediction. A comprehensive analysis of fifteen ensemble models is performed. The results show improved prediction performance and diminishing uncertainty of ensembles, compared with single machine learning models.
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