Earthquake-induced Liquefaction Research Articles

Induced Partial Saturation: From Mechanical Principles to Engineering Design of an Innovative and Eco-Friendly Countermeasure against Earthquake-Induced Soil Liquefaction

Earthquake-induced soil liquefaction is a catastrophic phenomenon that can damage existing building foundations and other structures, resulting in significant economic losses. Traditional mitigation techniques against liquefaction present critical aspects, such as high construction costs, impact on surrounding infrastructure and effects on the surrounding environment. Therefore, research is ongoing in order to develop new approaches and technologies suitable to mitigate liquefaction risk. Among the innovative countermeasures against liquefaction, Induced Partial Saturation (IPS) is considered one of the most promising technologies. It consists of introducing gas/air bubbles into the pore water of sandy soils in order to increase the compressibility of the fluid phase and then enhance liquefaction resistance. IPS is economical, eco-friendly and suitable for urbanised areas, where the need to reduce the risk of liquefaction must be addressed, taking into account the integrity of existing buildings. However, IPS is still far from being a routine technology since more aspects should be better understood. The main aim of this review is to raise some important questions and encourage further research and discussions on this topic. The review first analyses and discusses the effects of air/gas bubbles on the cyclic behaviour of sandy soils, focusing on the soil volume element scale and then extending the considerations to the real scale. The use of useful design charts is also described. Moreover, a section will be devoted to the effect of IPS under shallow foundations. The readers will fully understand the research trend of IPS liquefaction mitigation and will be encouraged to further explore new practical aspects to overcome the application difficulties and contribute to spreading the use of this technology.

Explainable AI models for predicting liquefaction-induced lateral spreading

Introduction: Earthquake-induced liquefaction can cause substantial lateral spreading, posing threats to infrastructure. Machine learning (ML) can improve lateral spreading prediction models by capturing complex soil characteristics and site conditions. However, the “black box” nature of ML models can hinder their adoption in critical decision-making.Method: This study addresses this limitation by using SHapley Additive exPlanations (SHAP) to interpret an eXtreme Gradient Boosting (XGB) model for lateral spreading prediction, trained on data from the 2011 Christchurch Earthquake.Result: SHAP analysis reveals the factors driving the model's predictions, enhancing transparency and allowing for comparison with established engineering knowledge. Notably, the SHAP values expose an unexpected behavior in the PGA feature. Moreover, the results demonstrate that the XGB model successfully identifies the importance of soil characteristics derived from Cone Penetration Test (CPT) data in predicting lateral spreading, validating its alignment with domain understanding.Discussion: This work highlights the value of explainable machine learning for reliable and informed decision-making in geotechnical engineering and hazard assessment.

Liquefaction ground deformations and cascading coastal flood hazard in the 2023 Kahramanmaraş earthquake sequence

The 2023 Kahramanmaraş earthquake sequence produced extensive liquefaction-induced ground deformations and ongoing flooding along the shoreline of the Mediterranean port city of İskenderun, Türkiye. This study compiles field observations and analyses from cross-disciplinary perspectives to investigate whether earthquake-induced liquefaction was a significant factor for increasing the flood hazard in İskenderun. Geotechnical reconnaissance observations following the earthquakes included seaward lateral spreading, settlement beneath buildings, and failures of coastal infrastructure. Three presented lateral spreading case histories indicate consistent ground deformation patterns with areas of reclaimed land. Persistent scatterer interferometry (PSI) measurements from synthetic aperture radar (SAR) imagery identify a noticeably greater rate of pre- and post-earthquake subsidence within the İskenderun coastal and urban areas relative to the surrounding regions. The PSI measurements also indicate subsidence rates accelerated following the earthquakes and were typically highest near the observed liquefaction manifestations. These evaluations suggest that while the liquefaction of coastal reclaimed fill caused significant ground deformations in the shoreline area, ongoing subsidence of İskenderun and other factors likely also exacerbated the flood hazard. Insights from this work suggest the importance of evaluating multi-hazard liquefaction and flood consequences for enhancing the resilience of coastal cities.

A Bayesian decision network–based pre-disaster mitigation model for earthquake-induced cascading events to balance costs and benefits on a limited budget

Cascading disasters induced by earthquakes amplify the severity of the initial impact on environment. Although decisionmakers may face uncertainty, an effective mitigation strategy is critical in environmental management. We propose an earthquake-induced cascading disaster mitigation–Bayesian decision network (ECDM-BDN) model to assess pre-disaster mitigating strategies under limited budgets from the perspective of systematic thinking. This model graphically represents the complex relationship among various variables in a seismic hazard system and resistance system based on disaster system theory. It can predict the triggering of cascading events through probabilistic reasoning and identify the key variable accountable for the range of outputs observed through sensitivity analysis. In addition, cost–benefit analyses are carried out by combining Bayesian decision network utility nodes and dynamic programming to obtain a balance between costs and benefits in the context of limited budgets. An earthquake-induced liquefaction served as a case study to demonstrate the proposed model’s effectiveness. Experimental results indicate that the ECDM-BDN model can balance the costs and effects of each pre-disaster mitigating strategy as well as select the optimal one according to the utility value. The proposed model can perform a “white-box” decision-making process, which is expected to guide earthquake-induced cascading event pre-disaster mitigation in cases of limited budgets.

Earthquake-induced liquefaction potential and risk assessment of the world’s largest mobile manufacturing plant, Noida, Uttar Pradesh

Earthquake-induced liquefaction potential and risk assessment of the world’s largest mobile manufacturing plant, Noida, Uttar Pradesh

Probabilistic Evaluation of Earthquake-Induced Liquefaction Using Bayesian Network Based on A Side-by-Side SPT-CPT Database

In situ tests such as standard penetration test (SPT) and cone penetration test (CPT) are often conducted to evaluate the probability of earthquake-induced liquefaction. However, the models adopted have seldom attempted to utilize SPT and CPT data simultaneously. In this study, a side-by-side SPT-CPT database at historical earthquake sites is established; then, a Bayesian network model is constructed to predict the probability of soil liquefaction based on this database, with which the SPT and CPT data are utilized simultaneously. Next, comparative studies are undertaken to illustrate the superiority of the Bayesian network-based probabilistic soil liquefaction model developed over other models, in terms of six SPT- and CPT-based conventional liquefaction models in the literature and two Bayesian network-based models. It should be noted that the liquefaction sites with two in situ tests are scarce and side-by-side SPT-CPT data can be incomplete, which leads to challenges in applying the Bayesian network model developed. To address this problem, correlations between SPT and CPT data are analyzed, and these correlations are further included in the Bayesian network model; as a result, a modified Bayesian network model is reached. Finally, the influence of the proportion of missing data in the incomplete SPT-CPT data on the liquefaction prediction accuracy is discussed.

Spatial variability of shear wave velocity: implications for the liquefaction response of a case study from the 2010 Maule Mw 8.8 Earthquake, Chile

Assessing the potential and extent of earthquake-induced liquefaction is paramount for seismic hazard assessment, for the large ground deformations it causes can result in severe damage to infrastructure and pose a threat to human lives, as evidenced by many contemporary and historical case studies in various tectonic settings. In that regard, numerical modeling of case studies, using state-of-the-art soil constitutive models and numerical frameworks, has proven to be a tailored methodology for liquefaction assessment. Indeed, these simulations allow for the dynamic response of liquefiable soils in terms of effective stresses, large strains, and ground displacements to be captured in a consistent manner with experimental and in-situ observations. Additionally, the impact of soil properties spatial variability in liquefaction response can be assessed, because the system response to waves propagating are naturally incorporated within the model. Considering that, we highlight that the effect of shear-wave velocity Vs spatial variability has not been thoroughly assessed. In a case study in Metropolitan Concepción, Chile, our research addresses the influence of Vs spatial variability on the dynamic response to liquefaction. At the study site, the 2010 Maule Mw 8.8 megathrust Earthquake triggered liquefaction-induced damage in the form of ground cracking, soil ejecta, and building settlements. Using simulated 2D Vs profiles generated from real 1D profiles retrieved with ambient noise methods, along with a PressureDependentMultiYield03 sand constitutive model, we studied the effect of Vs spatial variability on pore pressure generation, vertical settlements, and shear and volumetric strains by performing effective stress site response analyses. Our findings indicate that increased Vs variability reduces the median settlements and strains for soil units that exhibit liquefaction-like responses. On the other hand, no significant changes in the dynamic response are observed in soil units that exhibit non-liquefaction behavior, implying that the triggering of liquefaction is not influenced by spatial variability in Vs. We infer that when liquefaction-like behavior is triggered, an increase of the damping at the shallowest part of the soil domain might be the explanation for the decrease in the amplitude of the strains and settlements as the degree of Vs variability increases.

Assessment of liquefaction effects on dynamic soil-structure interaction for the 1908 Messina and Reggio Calabria scenario earthquake

Earthquake-induced liquefaction can significantly modify the dynamic response of a soil deposit and interacting structures. In this study, dynamic soil-structure interaction analyses have been performed for a strategic building resting on liquefiable soil. The structure, located in the city of Messina (Sicily, Italy), was built after the strong earthquake of 1908 that caused severe ground shaking and also liquefaction phenomena. Numerical analyses have been conducted with the finite element code PLAXIS3D that adopts the UBC3D-PLM model to simulate the seismic response of liquefiable soils. The advanced UBC3D-PLM model has been calibrated by means of the simulation of cyclic direct simple shear tests (CDSS) using PLAXIS3D software. The seismic behaviour of the soil-structure system on liquefiable soil has been analysed in terms of liquefaction potential, accelerations, response spectra and displacements under three seismograms of the 1908 earthquake. Findings of this study can be of great significance and interest for the prevention and mitigation of seismic liquefaction hazards in areas with high seismic risk.

A cone penetration test database for multiple thin-layer correction procedure development

Cone penetration tests (CPTs) are a commonly used in situ method to characterize soil. The recorded data are used for various applications, including earthquake-induced liquefaction evaluation. However, data recorded at a given depth in a CPT sounding are influenced by the properties of all the soil that falls within the zone of influence around the cone tip rather than only the soil at that particular depth. This causes data to be blurred or averaged in layered zones, a phenomenon referred to as multiple thin-layer effects. Multiple thin-layer effects can result in the inaccurate characterization of the thickness and stiffness of thin, interbedded layers. Correction procedures have been proposed to adjust CPT tip resistance for multiple thin-layer effects, but many procedures become less effective as layer thickness decreases. To compare or improve these procedures and to develop new ones, it is critical to have pairs of measured tip resistance ( q m) and true tip resistance ( q t) data, where q m is the tip resistance recorded by the CPT in a layered profile, and q t represents the tip resistance that would be measured in the profile absent of multiple thin-layer effects. Unfortunately, data sets containing q m and q t pairs are extremely rare. Accordingly, this article presents a unique database containing laboratory and numerically generated CPT data from 49 highly interlayered soil profiles. Both q m and q t are provided for each profile. An accompanying Jupyter notebook is provided to facilitate the use of the data and prepare them for future statistical learning (or other) applications to support multiple thin-layer correction procedure development.

Effect of earthquake-induced liquefaction of runout-path material on the movement of landslide

Effect of earthquake-induced liquefaction of runout-path material on the movement of landslide

Liquefaction of industrial zone against earthquake loading using laboratory and field measurements

Purpose. To assess the liquefaction of Kirkuk’s industrial region following a series of earthquakes that struck the city during the previous five years based on the current earthquake activity in the region. Methodology. Initially, substantial relationships for shear wave velocity in different types of soil were collected and studied, where the majority of these correlations necessitated the use of standard penetration tests in the field. Indeed, two boreholes were drilled up to a maximum depth of 10 m, and the numbers of blows for conventional penetration tests were measured at various depths in each borehole. The stated shear wave velocity values from the literature, as well as the maximum and lowest shear wave velocity constraints, were employed in a simple technique to estimate the cyclic shear stress induced by earthquake loading. Findings. Based on laboratory and field data, the safety factor against earthquake-induced liquefaction can be determined. When the worst-case scenario was examined using the suggested values of shear wave velocity, the factor of safety against earthquake was reduced by 94 % as the depth increased from 3.5 to 9 m. Originality. No previous study has tried to quantify the liquefaction impact of industrial zone of Kirkuk city as such an important rich-oil area was influenced by series of earthquakes occurrence. More importantly, for the first time field soil samples from on-site boreholes in Kirkuk city have been collected and used for liquefaction assessment since such real field data can be utilized properly in liquefaction evaluation process in the absence of any comparable quantification for the investigated area. Practical value. Precious liquefaction analysis should be performed prior to any proposed project construction in the light of increased earthquake activity in the industrial zone in Kirkuk city (Iraq).

Assessing the impact of inertial load on the buckling behavior of piles with large slenderness ratios in liquefiable deposits

Pile foundations used in liquefiable deposits often have large slenderness ratios, and these piles are particularly vulnerable to earthquake-induced soil liquefaction and occur buckling instability under axial load alone as soils around the pile liquefy. However, insight into the precise impact of inertial load on the buckling behavior of piles in liquefiable deposits is still lacking, and the current methods used to calculate the critical buckling load of the pile in liquefiable ground usually ignore inertial load. In light of the above, two shaking table tests were carried out to clarify the impact of inertial load on the buckling behavior of piles partly embedded in saturated sands. Moreover, a novel calculation method considering inertial load was presented to accurately estimate the critical buckling load of the pile in liquefiable deposits. The feasibility of the presented method had been verified using the results from a centrifuge experiment and an analytical method along with a finite element method. Furthermore, parametric analysis was conducted to study the effect of soil type, peak ground acceleration (PGA), pile diameter, and unsupported length ratio on the critical buckling load of the pile in liquefiable soils. Finally, comparing the calculated results of critical buckling load between the method presented in this paper and the method in the design code confirmed the limitation of the current design code. These combined analyses demonstrate that the critical buckling load of the pile decreases due to the action of inertial load, resulting in the pile being more susceptible to buckling failure. Therefore, designers have to consider the impact of inertial load during the seismic design of pile foundations in liquefiable soils.

Semi-Supervised Learning Method for the Augmentation of an Incomplete Image-Based Inventory of Earthquake-Induced Soil Liquefaction Surface Effects

Soil liquefaction often occurs as a secondary hazard during earthquakes and can lead to significant structural and infrastructure damage. Liquefaction is most often documented through field reconnaissance and recorded as point locations. Complete liquefaction inventories across the impacted area are rare but valuable for developing empirical liquefaction prediction models. Remote sensing analysis can be used to rapidly produce the full spatial extent of liquefaction ejecta after an event to inform and supplement field investigations. Visually labeling liquefaction ejecta from remotely sensed imagery is time-consuming and prone to human error and inconsistency. This study uses a partially labeled liquefaction inventory created from visual annotations by experts and proposes a pixel-based approach to detecting unlabeled liquefaction using advanced machine learning and image processing techniques, and to generating an augmented inventory of liquefaction ejecta with high spatial completeness. The proposed methodology is applied to aerial imagery taken from the 2011 Christchurch earthquake and considers the available partial liquefaction labels as high-certainty liquefaction features. This study consists of two specific comparative analyses. (1) To tackle the limited availability of labeled data and their spatial incompleteness, a semi-supervised self-training classification via Linear Discriminant Analysis is presented, and the performance of the semi-supervised learning approach is compared with supervised learning classification. (2) A post-event aerial image with RGB (red-green-blue) channels is used to extract color transformation bands, statistical indices, texture components, and dimensionality reduction outputs, and performances of the classification model with different combinations of selected features from these four groups are compared. Building footprints are also used as the only non-imagery geospatial information to improve classification accuracy by masking out building roofs from the classification process. To prepare the multi-class labeled data, regions of interest (ROIs) were drawn to collect samples of seven land cover and land use classes. The labeled samples of liquefaction were also clustered into two groups (dark and light) using the Fuzzy C-Means clustering algorithm to split the liquefaction pixels into two classes. A comparison of the generated maps with fully and manually labeled liquefaction data showed that the proposed semi-supervised method performs best when selected high-ranked features of the two groups of statistical indices (gradient weight and sum of the band squares) and dimensionality reduction outputs (first and second principal components) are used. It also outperforms supervised learning and can better augment the liquefaction labels across the image in terms of spatial completeness.

Joint analysis of macroscopic response and microfabric evolution of coral sand during earthquake-induced liquefaction

This study addresses the microfabric evolution of coral sand during earthquake-induced liquefaction using the joint tests of macroscopic shaking table and microscopic X-ray computed tomography. Based on the non-destructive scanning and image reconstruction in three-dimensional, the change in the grain–pore structure of sands under earthquakes is discussed. Furthermore, the special microstructural evolution of coral sand is studied by the contrast tests with Fujian sand. The results show that the coordination number of coral sand is larger than that of Fujian sand after liquefaction, which is consistent with that the reduction in acceleration of coral sand is smaller than that of the Fujian sand, and the coral sand foundation still has a larger shear strength due to the smaller excess pore pressure accumulation. Moreover, the contact index of Fujian sand and Reigate sand fluctuates in a similar range with the change of void ratio and coordination number compared with coral sand. This is due to the fact that the Fujian sand and Reigate sand are both general terrigenous sands (quartz sand), while coral sand has irregular particle shapes and larger surface friction caused by marine biogenesis. The test results deepen the understanding of the liquefaction mechanism of coral sand.

Estimation of historical earthquake-induced liquefaction in Fraser River delta using NBCC 2020 GMPEs in deterministic and probabilistic frameworks

Paleo-liquefaction features of sand dykes and sand blows were identified in the 1990s at multiple host sediments in the Fraser River delta in southern British Columbia all younger than 3500 BP. These paleo-liquefaction sites could be linked to Cascadia subduction earthquakes. Empirical magnitude-bound relationships are often used to estimate paleo-earthquake magnitudes. To determine the lower bound magnitude of Cascadia interface earthquakes that could have generated the paleo-liquefaction features, we use ground motion prediction equations for interface earthquakes from the sixth Canadian seismic hazard model of the 2020 National Building Code of Canada. We estimate the minimum M and its peak ground acceleration ( amax) of an interface earthquake required to initiate paleo-liquefaction in the study region. Starting with three full-rupture deterministic scenarios of varying source-to-site distance, we determine the minimum M required from Cascadia subduction zone interface mega-thrust earthquakes to induce liquefaction in the Fraser River delta spans 8.0–8.9 with a corresponding amax range of 0.09–0.13 g. We also perform a back-calculation paleo-liquefaction analysis in a probabilistic framework to incorporate aleatory uncertainties (cone penetration resistance, groundwater table, and amax) and epistemic uncertainties (liquefaction simplified model) via the Monte Carlo simulation. The developed probabilistic methodology is also applicable to a forward liquefaction assessment and other liquefaction sites globally. The median M from this probabilistic paleo-liquefaction for the four investigated sites lies between 8.8 and 9.0. Our probabilistic results also reveal that Cascadia interface earthquakes with M > 8.9 lead to a 31%–57% probability of liquefaction triggering in the Fraser River delta. In addition, we developed deterministic and probabilistic magnitude-bound curves specific to Cascadia interface earthquakes and representative site class E conditions. These curves provide more accurate magnitude estimations for predicting seismic-induced liquefaction from Cascadia interface earthquakes for sites in the Pacific Northwest than empirical bound curves.

Probabilistic-based seismic fragility assessment of earthquake-induced site liquefaction

As a very complex and uncertain problem, seismic liquefaction is affected by many factors, such as earthquake intensity, site conditions, soil properties, etc. During the past half a century, the state-of-the-art in analysis and evaluation of earthquake-induced soil liquefaction and its consequences as well as the state-of-the-practice in improving seismic stability to resist liquefaction have been made much progress. However, the fragility analysis and assessment for seismic liquefaction of sites have not been paid enough attention to. In this paper, the concept of seismic liquefaction fragility for building sites is proposed. The soil resistance to liquefaction and liquefaction possibility are expressed as a fragility function of ground motion intensity, which can be used to describe minor, moderate and severe liquefaction potential under different ground motion intensity. Combined with the existing research results of seismic liquefaction evaluation, the analysis method of liquefaction fragility is given. Taking a specific site as an example for liquefaction fragility analysis, it is found that taking the calculated exceedance probability of different liquefaction levels as the observation value, the liquefaction fragility function follows both lognormal distribution and log-logistic distribution. With the increase of liquefaction level, the median value of liquefaction fragility function increases, while the site liquefaction fragility curve moves to the right, which shows that the ability of the site to resist more severe liquefaction is increasing. When convolved with the analysis results of liquefaction triggering and hazard, the liquefaction fragility function can be a much efficient tool for seismic liquefaction risk analysis of specific sites.

Liquefaction triggering and post-triggering behavior of biocemented loose sand

Biocementation is a biomediated ground improvement technique that can improve the engineering behavior of granular soils. The process has received significant attention as an earthquake-induced liquefaction mitigation technique; however, critical gaps have remained in our understanding of how liquefaction behaviors may shift with differences in loading magnitudes and cementation levels. In this study, direct simple shear tests were performed to examine the undrained shearing behaviors of biocemented loose Ottawa F-65 sand prepared to varying cementation levels corresponding to Vs increases up to 523 m/s. Significant increases in liquefaction triggering resistances were observed with added cementation across a broad range of loading magnitudes (CSR = 0.1–1.75) and exceeded improvements obtainable through densification alone. Following triggering, modest improvements in post-triggering strain accumulation and reconsolidation behaviors were observed that could be primarily attributed to the densification of specimens from added mineral solids at low cementation levels (Δ Vs < 150 m/s). At higher cementation magnitudes, however, post-triggering behavioral enhancements exceeded those that would be expected from densification alone. Outcomes from this study improve our understanding of the liquefaction behaviors of biocemented soils, the metrics by which these behaviors can be effectively characterized, and the mechanisms responsible for behavioral enhancements, ultimately furthering our understanding of how the technology may be employed for liquefaction mitigation.

Biomediated control of colloidal silica grouting using microbial fermentation

Colloidal silica grouting is a ground improvement technique capable of stabilizing weak problematic soils and achieving large reductions in soil hydraulic conductivities for applications including earthquake-induced liquefaction mitigation and groundwater flow control. In the conventional approach, chemical accelerants are added to colloidal silica suspensions that are introduced into soils targeted for improvement and the formation of a semi-solid silica gel occurs over time at a rate controlled by suspension chemistry and in situ geochemical conditions. Although the process has been extensively investigated, controlling the rate of gel formation in the presence of varying subsurface conditions and the limited ability of conventional methods to effectively monitor the gel formation process has posed practical challenges. In this study, a biomediated soil improvement process is proposed which utilizes enriched fermentative microorganisms to control the gelation of colloidal silica grouts through solution pH reductions and ionic strength increases. Four series of batch experiments were performed to investigate the ability of glucose fermenting microorganisms to be enriched in natural sands to induce geochemical changes capable of mediating silica gel formation and assess the effect of treatment solution composition on pH reduction behaviors. Complementary batch and soil column experiments were subsequently performed to upscale the process and explore the effectiveness of chemical, hydraulic, and geophysical methods to monitor microbial activity, gel formation, and engineering improvements. Results demonstrate that fermentative microorganisms can be successfully enriched and mediate gel formation in suspensions that would otherwise remain highly stable, thereby forgoing the need for chemical accelerants, increasing the reliability and control of colloidal silica grouting, enabling new monitoring approaches, and affording engineering enhancements comparable to conventional colloidal silica grouts.

Soft Computing to Predict Earthquake-Induced Soil Liquefaction via CPT Results

Earthquake-induced soil liquefaction (EISL) can cause significant damage to structures, facilities, and vital urban arteries. Thus, the accurate prediction of EISL is a challenge for geotechnical engineers in mitigating irreparable loss to buildings and human lives. This research aims to propose a binary classification model based on the hybrid method of a wavelet neural network (WNN) and particle swarm optimization (PSO) to predict EISL based on cone penetration test (CPT) results. To this end, a well-known dataset consisting of 109 datapoints has been used. The developed WNN-PSO model can predict liquefaction with an overall accuracy of 99.09% based on seven input variables, including total vertical stress (σv), effective vertical stress (σv′), mean grain size (D50), normalized peak horizontal acceleration at ground surface (αmax), cone resistance (qc), cyclic stress ratio (CSR), and earthquake magnitude (Mw). The results show that the proposed WNN-PSO model has superior performance against other computational intelligence models. The results of sensitivity analysis using the neighborhood component analysis (NCA) method reveal that among the seven input variables, qc has the highest degree of importance and Mw has the lowest degree of importance in predicting EISL.

A multi-phase biogeochemical model for mitigating earthquake-induced liquefaction via microbially induced desaturation and calcium carbonate precipitation

Abstract. A next-generation biogeochemical model was developed to explore the impact of the native water source on microbially induced desaturation and precipitation (MIDP) via denitrification. MIDP is a non-disruptive, nature-based ground improvement technique that offers the promise of cost-effective mitigation of earthquake-induced soil liquefaction under and adjacent to existing structures. MIDP leverages native soil bacteria to reduce the potential for liquefaction triggering in the short term through biogenic gas generation (treatment completed within hours to days) and over the longer term through calcium carbonate precipitation (treatment completed in weeks to months). This next-generation biogeochemical model expands earlier modeling to consider multi-phase speciation, bacterial competition, inhibition, and precipitation. The biogeochemical model was used to explore the impact of varying treatment recipes on MIDP products and by-products in a natural seawater environment. The case study presented herein demonstrates the importance of optimizing treatment recipes to minimize unwanted by-products (e.g., H2S production) or incomplete denitrification (e.g., nitrate and nitrite accumulation).