Multiscale Regional Liquefaction Hazard Assessment and Mapping

Soil liquefaction is a major cause of damage during earthquakes that could trigger many kinds of ground failures such as ground settlement, lateral spreading, land slides, etc. These ground failures could cause damage to infrastructures such as buildings, bridges, and lifelines resulting in significant economic losses. Therefore it is of significant importance to assess liquefaction hazard. The triggering and consequencing ground failure of liquefaction have been well investigated in the past decades. Nowadays, the dominant approach that correlates the observed field behavior with various in-situ œindex tests is able to achieve considerably precise assessments for free field conditions at site-specific scale. Regional scale assessments of liquefaction hazard, however, are still underdeveloped. Issues such as cross-geologic units correlations are still not systematically investigated in regional liquefaction assessment. Therefore, the main objective of this dissertation is to develop a solution framework for reliable regional assessment of earthquake-induced liquefaction hazard. Another objective is to validate this framework by applying it to several earthquake-prone regions so that liquefaction hazard maps of these regions could be added to the literature and guide designers, engineers and researchers. Moreover, the dominant method of estimating liquefaction damages via empirical correlations are not capable for complex site conditions. Therefore another objective of this dissertation is to study alternative approaches for general estimation of liquefaction damages. To achieve these objectives, a multiscale modeling framework for better estimate of regional liquefaction hazard with material randomness and heterogeneity is developed. One advantage the developed methodology is the extension of conventional random field models to account for soil spatial variability at multiple scales and resolutions. The method allows selectively and adaptively generating random fields at smaller scales around critical areas or around areas where soil properties are known to a great detail from lab or field tests. The process is defined such that spatial correlation is consistent across length scales. Illustrative examples (Marina District in San Francisco, Alameda County in California, and Christchurch in New Zealand) are presented. Liquefaction hazard is evaluated at multi-scale. Compared with single scale analyses, multi-scale random fields provide more detailed information and higher-resolution soil properties around critical areas. This framework provides a new way to consistently incorporating small-scale local liquefaction analysis into large-scale liquefaction assessment mapping. Furthermore, finite element method is identified as a prominent alternative to traditional approach for liquefaction estimation via empirical correlations. A dynamic FEM model is built upon which an effective stress analysis is performed to estimate liquefaction-induced soil deformation at site-specific scale. It is shown the developed finite element model as a numerical tool can be used in predicting cyclic liquefaction in soils. This research is expected to shed light on the complete understanding of soil liquefaction during earthquakes in hoping of saving economic losses in the future

Application of an Artificial Neural Network for the CPT-based Soil Stratigraphy Classification

Subsurface soil profiling is an essential step in a site investigation. The traditional methods for in situ investigations, such as SPT borings and sampling, have been progressively replaced by CPT soundings since they are fast, repeatable, economical and provide continuous parameters of the mechanical behaviour of the soils. However, the derived CPT-based stratigraphy profiles might present noisy thin layers, and its soil type description might not reflect a textural-based classification (i.e. Universal Soil Classification System, USCS). Thus, this paper presents a straightforward artificial neural network (ANN) algorithm, to classify CPT soundings according to the USCS. Data for training the model have been retrieved from SPT-CPT pairs collected after the 2011 Christchurch earthquake in New Zealand. The application of the ANN to case studies show how the method is a cost-effective and time-efficient approach, but more input parameters and data are needed for increasing its performance

Analysis of Soil Liquefaction Potential Through Three Field Tests-Based Methods: A Case Study of Babol City

During earthquakes, ground failure is commonly caused by liquefaction. Thus, assessment of soil liquefaction potential in earthquake-prone regions is a crucial step towards reducing earthquake hazard. Since Babol city in Iran country is located in a high seismic area, estimation of soil liquefaction potential is of great importance in this city. For this purpose, in the present research, using field-based methods and geotechnical data of 60 available boreholes in Babol, three liquefaction microzonation maps were provided. Finally, one comprehensive liquefaction microzonation map was presented for soil of Babol city. The obtained results in this paper are well in line with the previous investigations. The results indicate that application of different field tests in evaluation of liquefaction is necessary

Study on liquefaction of soil

Liquefaction is the phenomena when there is loss of strength in saturated and cohesion-less soils because of increased pore water pressures and hence reduced effective stresses due to dynamic loading. It is a phenomenon in which the strength and stiffness of a soil is reduced by earthquake shaking or other rapid loading. In this paper the field datas of two major earthquakes, namely Chi-Chi, Taiwan earthquake (magnitude Mw =7.6) and Kocaeli, Turkey earthquake (magnitude Mw = 7.4) in 1999,a study of the SPT and CPT case datas has been undertaken. In this paper, some methods have been studied namely, Semi-empirical method of evaluating soil liquefaction potential, Practical reliability based method for assessing soil liquefaction, Robertson method, Olsen method and Juang method. A comparative study has been done using all the above mentioned methods and the error percentages have been calculated for each of them with respect to the actual on field test results to conclude which of the models is better for both SPT and CPT case datas

CPT-BASED SIMPLIFIED LIQUEFACTION ASSESSMENT BY USING FUZZY-NEURAL NETWORK

ABSTRACT Due to the difficulty and the cost of obtaining high quality undisturbed samples, simplified methods based on in-situ tests such as the standard penetration test (SPT) and the cone penetration test (CPT) are preferred by geotechnical engineers for evaluation of earthquake induced liquefaction potential of soils. Because of the increasing popularity worldwide of the CPT for site characterization, significant progress on the CPTbased methods has been made. In most existing CPT-based methods, empirically determined curves are used to predict liquefaction and non-liquefaction. These empirical curves are generally relied on engineering judgment and are essentially performance functions that were established based on field observations of soil performance during earthquakes at sites where in-situ CPT data are available. The performance functions can be referred to as the limit state functions and the empirical curves are generally limit state functions such that the curve are generally limit state curve. The limit state for liquefaction evaluation is defined by CRR being equal to CSR, in which CRR is liquefaction resistance of a soil that is generally expressed as cyclic resistance ratio, and CSR is the cyclic stress ratio, i.e., the seismic load that causes liquefaction. In this study, a fuzzy-neural network with 466 CPT field observations is developed first to evaluate liquefaction potential of soils. Then a search procedure is presented to locate data points on the limit state function. Finally, regression is used to determine a simple formula of limit state curve that can easily evaluate cyclic liquefaction potential of soils

Evaluation of liquefaction susceptibility of soil using genetic programming and multivariate adaptive regression spline

Liquefaction of soil can be considered as one of the most disastrous seismic hazards and evaluation of liquefaction susceptibility is an important aspect of geotechnical engineering. For evaluation of liquefaction potential of soil generally two variables are required, such as: (i) the seismic demand on a soil layer expressed in terms of CSR, (ii) the capacity of the soil to resist liquefaction expressed in terms of CRR. The method for evaluation of CRR is to test undisturbed soil specimens in the laboratory. The various field tests used for the liquefaction resistance of the soil are (i) Standard Penetration Test (SPT), (ii) Cone Penetration Test (CPT) , (iii) Shear Wave velocity Measurements and (iv) Becker Penetration test (BPT). Artificial intelligent techniques such as ANN, SVM, RVM are used to develop liquefaction prediction models based on in-situ database, which are found to be more efficient as compared to statistical methods. However, these techniques will not produce a comprehensive relationship between the inputs and output, and are also called as ‘black box’ system. In the present study an attempt has been made to predict the liquefaction potential of soil based post liquefaction cone penetration test (CPT) , standard penetration test (SPT) and shear wave velocity (V_s) data using multivariate adaptive regression splines (MARS) and genetic programming (GP). A comparative analysis is made among the existing methods and the proposed MARS and GP model for prediction of liquefied and non-liquefied cases in terms of percentage success rate with respect to the field manifestations. It is observed that the prediction as per MARS and GP model is more accurate towards field manifestation in comparison to other existing methods