Liquefaction hazard assessment in a seismically active region of Himalayas using geotechnical and geophysical investigations: a case study of the Jammu Region

Ansari, Abdullah; Zahoor, Falak; Rao, K. Seshagiri; Jain, Arvind K.

doi:10.1007/s10064-022-02852-3

Cited by 26 publications

(9 citation statements)

References 76 publications

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“…Lining spalling and portal collapses were the most visible seismic damage in multiple mountain tunnels during the 1999 Chi-Chi earthquake and the 2008 Wenchuan earthquake During these two earthquakes, most of the tunnels exhibit extensive lining deformation. The two principal portal failure types are stratum deformation and spandrel cracking (Ansari et al 2022a , b ). The most typical types of pavement damage seen in tunnels affected by large magnitude earthquakes are uplift, cracking, and groundwater leakage in construction joints and concrete lining (Yu et al 2016 ).…”

Section: Seismic Tunnel Damagesmentioning

confidence: 99%

“…The 2005 Kashmir earthquake (M w =7.6) was one of the worst, with over one lakh people killed and infrastructure projects including bridges, retaining walls, and dams destroyed (Durrani et al 2005 ). This area has witnessed mega infrastructural projects including bridge abutment, tunnelling, and expansion of railway tracks and national highways, which are going to help the overall economic development of the region (Ansari et al 2022a , b ). The Udhampur Srinagar Baramulla Rail Link (USBRL) project is a 345 km long mega rail project in the Himalayan terrain that is bordered by active seismic sources such as the Main Boundary Thrust (MBT) and Main Central Thrust (MCT).…”

Section: Introductionmentioning

confidence: 99%

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Deep learning model for predicting tunnel damages and track serviceability under seismic environment

Ansari

Rao

Jain

et al. 2022

Model. Earth Syst. Environ.

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Jammu and Kashmir in the northwestern part of the Himalayan region is frequently triggered with moderate to large magnitude earthquakes due to an active tectonic regime. In this study, a mathematical formulation-based Seismic Tunnel Damage Prediction (STDP) model is proposed using the deep learning (DL) approach. The pertinency of the DL model is validated using tunnel damage data from historical earthquakes such as the 1999 Chi-Chi earthquake, the 2004 Mid-Niigata earthquake, and the 2008 Wenchuan earthquake. Peak ground acceleration (PGA), source to site distance (SSD), overburden depth (OD), lining thickness (t), tunnel diameter (Ф), and geological strength index (GSI) were employed as inputs to train the Feedforward Neural Network (FNN) for damage state prediction. The performance evaluation results provided a clear indication for further use in a variety of risk assessment domains. When compared to models based on historical data, the proposed STDP model produces consistent results, demonstrating the robustness of the methodology used in this work. All models perform well during validation based on fitness metrics. The “STD multiple graphs” is also proposed which provide information on damage indexing, damage pattern, and crack predictive specifications. This can be used as a ready toolbox to check the vulnerability in post-seismic scenarios. The seismic design guidelines for tunnelling projects are also proposed, which discuss the damage pattern and suggest mitigation measures. The proposed STDP model, STD multiple graphs, and seismic design guidance are applicable to any earthquake-prone tunnelling project anywhere in the world.

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Section: Seismic Tunnel Damagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep learning model for predicting tunnel damages and track serviceability under seismic environment

Ansari

Rao

Jain

et al. 2022

Model. Earth Syst. Environ.

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“…Among these soil investigation data, SPT is the most used data. SPT data was also used in the study of liquefaction potential in the Jammu Region, India [18], Roorkee Region, India [19], Kathmandu Valley in Nepal [20], Imperial Valley, California [21], Surat City, India [22]. Currently, there are limited number of studies on liquefaction potential analysis in coastal areas in Indonesia.…”

Section: Introductionmentioning

confidence: 99%

Liquefaction potential analysis based on standard penetration test in coastal area (Case study: Loh Buaya, Rinca Island, Indonesia)

Rakhman,

Ismanti,

Aditya

2024

IOP Conf. Ser.: Earth Environ. Sci.

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An area has liquefaction potential when it has a shallow groundwater level, loose sandy soil, and is prone to earthquakes. There are several areas with such criteria that have not been analysed for liquefaction potential. This study aims to analyse and plot the liquefaction potential in the coastal area of Loh Buaya, Rinca Island, East Nusa Tenggara Province. Soil investigation data, such as SPT, sieve analysis, and groundwater level, as well as earthquake history that occurred from 1922-2022, served as the main data for liquefaction potential analyses. The methods used were Ground Motion Equation Prediction (GMPE) to calculate Peak Ground Acceleration (PGA), Simplified Procedure, and Liquefaction Severity Index (LSI) to make a liquefaction hazard assessment. LSI scores were used to provide micro-zonation of liquefaction potential with Inverse Distance Weighted (IDW) interpolation in QGIS. The result obtained is very dense gravel has no liquefaction potential whereas loose sandy soil has very high in LSI classification because loose sandy soil has liquefaction potential up to 20 meters of depth. The applying of micro-zonation LSI by IDW interpolation method can estimate the potential level of liquefaction hazard on Loh Buaya, Rinca Island with limited soil investigation data.

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“…Liquefaction is one of the most devastating seismic hazards related to earthquakes in the world. It occurs when earthquake shaking and increased pore water pressure reduce the strength and stiffness of soil (Marcuson, 1978;Papathanassiou et al, 2011;Tehran et al, 2016;Bahari et al, 2020;Ansari et al, 2022). Therefore, the evaluation of the liquefaction susceptibility and its resistance is an important component of seismic hazard assessment in an earthquake-prone region.…”

Section: Introductionmentioning

confidence: 99%

Site response and liquefaction hazard analysis of Hawassa town, Main Ethiopian Rift

Ayele

Meten

Woldearegay

2022

Front. Built Environ.

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The study area is located in one of the most earthquake prone regions in southern Ethiopia, which is characterized by small-to-intermediate earthquake occurrences causing damage to buildings. Predicting liquefaction hazard potential and local site effects are imperative to manage earthquake hazard and reduce the damage to buildings and loss of lives. The objectives of this work were to perform the equivalent linear response analysis (ELA) and shear wave velocity (Vs.)-based liquefaction hazard analysis and classify the site into different seismic site classes based on the European and American codes. The SPT-N and Vs.30 values showed the site falls in the C and D classes based on the NEHRP (2015) code but falls in the B and C classes based on the EC8 (2003) code. The susceptibility of liquefaction was evaluated using grain size analysis curves. Moreover, peak ground acceleration (PGA), spectral acceleration (SA), and maximum strain (%), which are very critical to understanding the local site effects, were estimated by the DeepsoilV.7 program. The cyclic stress ratio and cyclic resistance ratio were used to calculate the factor of safety (FS). A liquefaction potential index (LPI), probability of liquefaction (PL), and probability of liquefaction induced ground failure (PG) were used to assess the probability of liquefaction. The peak ground acceleration (g) values ranged from 0.166 to 0.281 g, whereas spectral acceleration (g) was found to be high at 0.1–1s. The liquefaction susceptibility screening criteria revealed that the study area is highly susceptible to liquefaction. FS is < 1 for a liquefied site, but FS is > 1 for non-liquefied sites. In comparison to non-liquefied sites, the liquefaction forecast site has a liquefaction potential index value of 0–54.16, very likely high PL, and high PG. The findings will be helpful in the design of structures and in solving practical challenges in earthquake engineering.

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Liquefaction hazard assessment in a seismically active region of Himalayas using geotechnical and geophysical investigations: a case study of the Jammu Region

Cited by 26 publications

References 76 publications

Deep learning model for predicting tunnel damages and track serviceability under seismic environment

Deep learning model for predicting tunnel damages and track serviceability under seismic environment

Liquefaction potential analysis based on standard penetration test in coastal area (Case study: Loh Buaya, Rinca Island, Indonesia)

Site response and liquefaction hazard analysis of Hawassa town, Main Ethiopian Rift

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