An analytical approach to probabilistic modeling of liquefaction based on shear wave velocity

This is the author accepted manuscript. The final version is available from Springer via the DOI in this recordEvaluation of liquefaction potential of soils is an important step in many geotechnical investigations in regions susceptible to earthquake. For this purpose, the use of site shear wave velocity (Vs) provides a promising approach. The safety factors in the deterministic analysis of liquefaction potential are often difficult to interpret because of uncertainties in the soil and earthquake parameters. To deal with the uncertainties, probabilistic approaches have been employed. In this research, the Jointly Distributed Random Variables (JDRV) method is used as an analytical method for probabilistic assessment of liquefaction potential based on measurement of site shear wave velocity. The selected stochastic parameters are stress-corrected shear-wave velocity and stress reduction factor, which are modeled using a truncated normal probability density function and the peak horizontal earthquake acceleration ratio and earthquake magnitude, which are considered to have a truncated exponential probability density function. Comparison of the results with those of Monte Carlo Simulation (MCS) indicates very good performance of the proposed method in assessment of reliability. Comparison of the results of the proposed model and a Standard Penetration Test (SPT)-based model developed using JDRV shows that shear wave velocity (Vs)- based model provides a more conservative prediction of liquefaction potential than the SPT-base model

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

Evaluation of liquefaction potential of soil using genetic programming

Out of the various seismic hazards, soil liquefaction is a major cause of both loss of life and damage to infrastructures and lifeline systems. Soil liquefaction phenomena have been noticed in many historical earthquakes after first large scale observations of damage caused by liquefaction in the 1964 Niigata, Japan and 1964 Alaska, USA, earthquakes. Due to difficulty in obtaining high quality undisturbed samples and cost involved therein, in-situ tests, standard penetration test (SPT) and cone penetration test (CPT), are being preferred by geotechnical engineers for liquefaction potential evaluation with limited use of other in-situ tests like shear wave velocity tests and Baker penetration tests. The liquefaction evaluation in the deterministic framework is preferred by the geotechnical engineering professionals because of its simple mathematical approach with minimum requirement of data, time and effort. However, for important life line structures, there is a need of probabilistic and reliability methods for taking risk based design decisions. In recent years, soft computing techniques such as artificial neural network (ANN), support vector machine (SVM) and relevance vector machine (RVM) have been successfully implemented for evaluation liquefaction potential with better accuracy compared to available statistical methods. In the recent past, evolutionary soft computing technique genetic programming (GP) based on Darwinian theory of natural selection is being used as an alternate soft computing technique.The objective of the present research is to develop deterministic, probabilistic and reliability-based models to evaluate the liquefaction potential of soil using multi-gene genetic programming (MGGP) based on post liquefaction SPT and CPT database. Here, the liquefaction potential is evaluated and expressed in terms of liquefaction field performance indicator, referred as a liquefaction index (LI) and factor of safety against the occurrence of liquefaction (Fs). Further, the developed LIp models have been used to develop both SPT and CPT-based CRR models. These developed CRR models in conjunction with the widely used CSR7.5 model, form the proposed MGGP-based deterministic methods. The efficiency of both the developed SPT and CPT-based iv deterministic models has been compared with that of available statistical and ANN-based models on the basis of independent databas

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

Numerical Study of Concrete

Concrete is one of the most widely used construction material in the word today. The research in concrete follows the environment impact, economy, population and advanced technology. This special issue presents the recent numerical study for research in concrete. The research topic includes the finite element analysis, digital concrete, reinforcement technique without rebars and 3D printing

Green Low-Carbon Technology for Metalliferous Minerals

Metalliferous minerals play a central role in the global economy. They will continue to provide the raw materials we need for industrial processes. Significant challenges will likely emerge if the climate-driven green and low-carbon development transition of metalliferous mineral exploitation is not managed responsibly and sustainably. Green low-carbon technology is vital to promote the development of metalliferous mineral resources shifting from extensive and destructive mining to clean and energy-saving mining in future decades. Global mining scientists and engineers have conducted a lot of research in related fields, such as green mining, ecological mining, energy-saving mining, and mining solid waste recycling, and have achieved a great deal of innovative progress and achievements. This Special Issue intends to collect the latest developments in the green low-carbon mining field, written by well-known researchers who have contributed to the innovation of new technologies, process optimization methods, or energy-saving techniques in metalliferous minerals development

Prediction of the Effectiveness of Rolling Dynamic Compaction Using Artificial Intelligence Techniques and In Situ Soil Test Data

The research presented in this thesis focuses on developing predictive tools to forecast the effectiveness of rolling dynamic compaction (RDC) in different ground conditions. Among many other soil compaction methods, RDC is a widespread technique, which involves impacting the ground with a heavy (6–12 tonnes) non-circular (3-, 4- and 5- sided) module. It provides the construction industry with an improved ground compaction capability, especially with respect to a greater influence depth and a higher speed of compaction, resulting in increased productivity when compared with traditional compaction equipment. However, to date, no rational means are available for obtaining a priori estimation of the degree of densification or the extent of the influence depth by RDC in different ground conditions. In addressing this knowledge gap, the research presented in this thesis develops robust predictive models to forecast the performance of RDC by means of the artificial intelligence (AI) techniques in the form of artificial neural networks (ANNs) and linear genetic programming (LGP), which have already been proven to be successful in a wide variety of forecasting applications in geotechnical engineering aspects. This study is focussed solely on the 4-sided, 8 tonne impact roller (BH-1300) and the AI-based models incorporate comprehensive databases consisting of in situ soil test data; specifically cone penetration test (CPT) and dynamic cone penetration (DCP) test data obtained from many ground improvement projects involving RDC. Thus, altogether, two distinct sets of optimal models: two involving ANNs – one for the CPT and the other for the DCP; and two LGP models – again, one for the CPT and the other for the DCP – are presented. The accuracy and the reliability of the optimal model predictions are assessed by subjecting them to various performance measures. Furthermore, each of the selected optimal models are examined in a parametric study, by which the generalisation ability and the robustness of the models are confirmed. In addition, the performance of the optimal ANN and LGP-based models, as well as other aspects, are compared with each other in order to assess the suitability and shortcomings of each. Consequently, a recommendation has been made of the most feasible approach for predicting the effectiveness of RDC in different ground conditions with respect to CPT and DCP test data. The models have also been disseminated via a series of mathematical formulae and/or programming code to facilitate their application in practice. It is demonstrated that the developed optimal models are accurate and reliable over a range of soil types, and thus, have been recommended with confidence. As such, the developed models provide preliminary estimates of the density improvement in the ground based on the subsurface conditions and the number of roller passes. Therefore, it is considered that the models are beneficial during the pre-planning stages, and may replace, or at the very least augment, the necessity for RDC field trials prior to fullscale construction. In addition, the analyses demonstrate that the AI techniques provide a feasible approach for non-linear modelling involving many parameters, which in turn, further encourages future applications in the broader geotechnical engineering context. Finally, a comprehensive set of guidelines for each of the AI techniques employed in this research, i.e. ANN and LGP, is provided, with the intention of assisting potential and current users of these techniques.Thesis (Ph.D.) -- University of Adelaide, School of Civil, Environmental and Mining Engineering, 201