Deterministic and Probabilistic Analysis of Liquefaction for Different Regions in Bihar

Umar, Sujeet Kumar; Samui, Pijush; Kumari, Sudesh

doi:10.1007/s10706-018-0498-7

Cited by 16 publications

(4 citation statements)

References 19 publications

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“…To develop the ANFIS models 238 datasets were taken and incorporated for the development of fuzzy neural network models. The dataset is obtained from the paper of Umar et al (2018). This work is based on the datasets of SPT and bore.…”

Section: Continued On Next Pagementioning

confidence: 99%

“…Shahri (2016), Kumar & Rawat (2017) assessed and predicted the liquefaction potential using different artificial neural network models. Umar et al(2018) have been employed the deterministic and probabilistic study of liquefaction for many areas in Bihar. The objective of this paper is to propose some model for soil-liquefaction potential classification.…”

Section: Introductionmentioning

confidence: 99%

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A Liquefaction Study Using ENN, CA, and Biogeography Optimized-Based ANFIS Technique

Umar

Kumari

Samui

et al. 2021

International Journal of Applied Metaheuristic Computing

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In any construction projects,assessment of liquefaction potential induced due to seismic excitation during earthquake is a critical concern.The objective of present model development is to classify and assess liquefaction potential of soil.This paper addresses Emotional Neural Network(ENN), Cultural Algorithm(CA) and biogeography optimized(BBO) based adaptive neuro-fuzzy inference system (ANFIS) for liquefaction study.The performance of neural emotional network and cultural algorithm has been also discussed. BBO-ANFIS combines the biogeography features to optimize the ANFIS parameters to achieve higher prediction accuracy.The model is trained with case history of liquefaction databases.Two parameters are used as input such as the cyclic stress ratio and standard penetration test (SPT) value.The performance of these models was assessed using different indexes i.e. sensitivity, specificity, FNR, FPR and accuracy rate.The performance of all models is compared.Among the models, the BBO-ANFIS model has been outperformed and can be adopted as new reliable technique for liquefaction study.

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Section: Continued On Next Pagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Liquefaction Study Using ENN, CA, and Biogeography Optimized-Based ANFIS Technique

Umar

Kumari

Samui

et al. 2021

International Journal of Applied Metaheuristic Computing

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“…Umar et al [34] pointed out that earthquake magnitude, M w , plays an important role in the assessment of liquefaction. To consider the influence of M w on f M w �7.5 and facilitate comparison f M w �7.5 of sites from different earthquakes, f is obtained as follows:…”

Section: E Eoretical Basismentioning

confidence: 99%

A V_s‐Based Logistic Regression Method for Liquefaction Evaluation

Yao

Liu

Wang

et al. 2021

Advances in Civil Engineering

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The current liquefaction evaluation methods mainly focus on the success rate for liquefied sites so that the evaluation result tends to be conservative at different seismic intensities. Therefore, a new formula about liquefaction evaluation by introducing logistic regression theory is proposed to solve the deficiencies of the current evaluation method, which is based on 225 sets of shear wave velocity data reported by Andrus. The reliability of the new formula is verified based on 336 sets of Vs data collected from the Kayen database. The performance of the new formula on liquefaction evaluation is compared with existing liquefaction evaluation methods including the Andrus method and the Chinese code method. Compared with the Andrus method and Chinese code method, the success rates of liquefaction evaluation given by the new formula under different seismic intensities are more balanced between liquefied site and nonliquefied site. The new formula at 50% probability of liquefaction is more adaptable for a wide range of seismic intensities, ground water table, and sand buried depth. In addition, the new formula at different probabilistic levels of liquefaction can be adopted based on the importance of the engineering site in risk analysis.

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“…Since its introduction to the geotechnical engineering field the cone penetration test (CPT) has been applied to numerous engineering tasks, such as the estimation of bearing capacity, design of shallow and deep foundations, walls and embankments, as well as the assessment of ground improvement measures and the site resistance to liquefaction (Umar et al 2018;Kumar et al 2021). Nevertheless, estimating soil parameters and site characterization may be regarded as the main purposes for carrying out CPTs (Lunne et al 1997;Schnaid 2009;Niazi 2022).…”

Section: Introductionmentioning

confidence: 99%

A Case Study on Advanced CPT Data Interpretation: From Stratification to Soil Parameters

Marzouk,

Granitzer,

Rauter

et al. 2024

Geotech Geol Eng

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The cone penetration test (CPT) is considered as one of the most reliable in-situ tests and has found numerous applications in the geotechnical engineering field. Traditional CPT interpretation includes, but are not limited to the identification of the soil stratification and the determination of soil parameters. This paper presents a case study concerning a test site located in Salzburg, Austria, in which we focus on the interpretation of CPTs from different perspectives. The manuscript is divided into three main sections dealing with three different aspects of CPT interpretation, namely stratification, ground variability and soil parameters. The first strategy introduces a machine learning based stratification identification strategy to detect soil layer boundaries from CPT measurements. A comparison with reference solutions demonstrates relative merits of this approach to classical filter algorithms based on empirical CPT classifications. The second strategy introduces an intuitive approach to evaluate the ground variability. This is achieved by calculating the level of fluctuation on the basis of CPT measurements and could be used as a data-driven decision-making tool for the improved design of CPT investigation layouts. The third strategy is embedded in an ongoing research project that aims to determine constitutive model parameters from in-situ tests using a graph-based methodology. In the present work, the developed automated parameter determination framework is applied to evaluate the soil parameters of one selected soil layer identified from the CPT interpretations. Potential lines of research in the context of CPT interpretation are explored throughout this work and may serve as valuable reference in future research.

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Deterministic and Probabilistic Analysis of Liquefaction for Different Regions in Bihar

Cited by 16 publications

References 19 publications

A Liquefaction Study Using ENN, CA, and Biogeography Optimized-Based ANFIS Technique

A Liquefaction Study Using ENN, CA, and Biogeography Optimized-Based ANFIS Technique

A V_s‐Based Logistic Regression Method for Liquefaction Evaluation

A Case Study on Advanced CPT Data Interpretation: From Stratification to Soil Parameters

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Deterministic and Probabilistic Analysis of Liquefaction for Different Regions in Bihar

Cited by 16 publications

References 19 publications

A Liquefaction Study Using ENN, CA, and Biogeography Optimized-Based ANFIS Technique

A Liquefaction Study Using ENN, CA, and Biogeography Optimized-Based ANFIS Technique

A Vs‐Based Logistic Regression Method for Liquefaction Evaluation

A Case Study on Advanced CPT Data Interpretation: From Stratification to Soil Parameters

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A V_s‐Based Logistic Regression Method for Liquefaction Evaluation