Kapilesh Bhargava

Prediction of Blast-Induced Vibration Parameters for Soil Sites

AbstractIn the recent past, the topic of blast loads on structures has received considerable attention from researchers. Site-specific empirical models for blast-induced vibration parameters like peak particle velocity (PPV), peak pressure (PP), peak particle displacement (PPD), and pore pressure ratio (PPR) are commonly used for blast-resistant designs. However, these empirical models do not consider the variation in soil properties, e.g., the degree of saturation and uncertain in situ conditions. Hence, in this paper, a total of 120 pieces of blast data from various soil sites have been collected and used to propose a generalized empirical model for estimating blast-induced vibration parameters by considering three basic soil properties, namely, unit weight, degree of saturation, and Young’s modulus. Standard errors and coefficients of correlation for the prediction of blast-induced vibration parameters by various empirical models are obtained with respect to the observed soil field data. The present em...

Models for Corrosion-Induced Bond Strength Degradation in Reinforced Concrete

The assessment of progressive degradation of bond between concrete and reinforcing steel is of great importance in evaluating the residual strength of the reinforced concrete (RC) structural members with corroded reinforcements. Simple empirical and analytical models are proposed to demonstrate the effect of reinforcement corrosion on the reduction of bond strength. The empirical models are proposed by considering a wide range of published experimental investigations related to the bond strength degradation as a result of reinforcement corrosion. An analytical model for bond strength of corroded reinforcement has been adopted in which the estimation of various bond strength parameters is proposed by the authors. These parameters include corrosion pressure due to expansive action of corrosion products, modeling of tensile behavior of cracked concrete, and adhesion and friction coefficient between the corroded bar and cracked concrete. The performance of the proposed empirical and analytical bond strength models is then investigated through their ability to reproduce the available experimental trends. It has been found that the proposed models are capable of providing the estimates of predicted bond strength of corroded reinforcement that are in reasonably good agreement with the experimentally observed values and are also in agreement with those of the other reported published data on analytical and empirical predictions.

Evaluation of Seismic Fragility of Structures - A Case Study

The present paper attempts to evaluate the seismic fragility for a typical elevated water-retaining structure. The structure is analysed for two cases: (i) empty tank; and (ii) tank filled with water. The various parameters that could affect the seismic structural response include material strength of concrete and reinforcing steel, effective prestress available in the tank, ductility ratio and structural damping available within the structure, normalised ground motion response spectral shape, foundation and surrounding soil parameters and the total height of water available in the tank. Based on this case study, the seismic fragility of the structure is developed. The results are presented as families of conditional probability curves plotted against peak ground acceleration (PGA) at two critical locations. The procedure adopted, incorporates the various randomness and uncertainty associated with the parameters under consideration.

A combined numerical and neural technique for short term prediction of ocean currents in the Indian Ocean

AbstractBackgroundShort term current prediction for operational purposes is commonly carried out with the help of numerical ocean circulation models. The numerical models have advantage that they are based on the physics of the underlying process. However because of their spatial nature they may not be so accurate while making station-specific predictions. In such cases data-driven approaches like artificial neural network (ANN)’s trained on the basis of location-specific data may work better. In this paper an attempt is made to do daily predictions of ocean currents by combination of a numerical model and ANNs.ResultsThe difference in the current velocity estimated by the numerical model and actual observations at a given time was calculated and corresponding error time series was formed based on all past numerical estimations and observations. An ANN was trained over such time series to predict errors for future, which were added to the numerical estimation so as to predict daily current velocities over multiple days in future.ConclusionsThe suggested approach, implemented at two locations in Indian Ocean, was found to perform satisfactory current predictions up to a lead time of 5 days, as ascertained through various error statistics. The standalone networks once trained using the numerical outcome can reproduce such output well over future time without using variety of data and computational resources required for running the numerical model on a continuous basis.

Review of Evaluation of Uncertainty in Soil Property Estimates from Geotechnical Investigation

Geotechnical investigations are carried out for important projects in nuclear industries. Investigations involve estimation of soil properties by various laboratory and in situ tests. Estimation of accurate geotechnical parameters is needed in design process. The uncertainties involved in soil property estimates have necessitated reliability analysis in addition to conventional analysis. Unavailability of a large set of sample data and mathematical sophistication for carrying out uncertainty analysis are areas of concern. Reliability based design is done considering individual sources of uncertainties. Quantitative analysis of uncertainty is carried out. Standard deviations (SD) of various parameters need to be evaluated based on the data. There is degree of uncertainties in the calculation of parameters. Varying degree of uncertainties has been observed for different parameters. For some parameters, it is low and for some, it is high which need to be considered in reliability analysis. When sufficient data is available, SD is calculated directly from data. When sufficient data is not available, published value of Values of Coefficient of Variation (COV) is used which when multiplied with mean value gives standard deviation. This paper presents review of evaluation of uncertainties in soil property estimates.

Prediction of ocean currents with artificial neural networks

The prediction of ocean currents on real time basis is a complex exercise due to the variability of coastal features like topography and bathymetry as well as uncertainty of driving forces and also interaction among various met-ocean parameters. Although numerical methods are commonly used for this purpose, they need large and detailed exogenous information along with high computational resources and at times can have less tolerance to noise and gaps in data. At this backdrop and when site-specific information is sought for, data-driven techniques like artificial neural network (ANN) might appear attractive. Although there are some past applications of ANN to online current forecasting, lower prediction accuracy at higher values and over longer prediction intervals together with unequal accuracy levels for zonal and meridional current components have remained as problems. This paper attempts to address these issues. At two locations in North Atlantic and North Pacific Oceans the ANN-based time series models have been developed to predict currents over time horizons of 1hr to 24 h. After considerable experimentation, it was found that if the input is pre-processed with the help of a carefully selected smoothing technique and if its sequence length is methodically selected, then together with an empirical correction the long interval and extreme predictions significantly improve in case of both meridional and zonal current components.

Recent Development in Modeling, Analysis, and Design of Foundation Systems Subjected to Blast Loading Considering Uncertainties

Traditional design approaches simplify the problem by considering the uncertain parameters to be deterministic, and they use lumped factors of safety (empirical, based on past experience) to account for the uncertainties propagating in the design decisions. To evaluate response of foundation to blast loads, there are uncertainties involved in loading conditions, inherent spatial variability of soil properties, presence of geologic anomaly, uncertainty associated with selection of an appropriate analytical model, testing and measurements errors, and human errors. These have necessitated reliability analysis in addition to conventional analysis. As far as possible, uncertainties should be avoided, and if those are unavoidable, then those should be eliminated, and if elimination is not possible, those should be adapted in planning, design, and usage of foundation throughout its life. Unavailability of a large set of sample data and mathematical sophistication for carrying out uncertainty analysis are areas of concern.

Correlations of Uniaxial Compressive Strength of Rock Mass with Conventional Strength Properties through Random Number Generation

AbstractThere has been a lot of interest in the relationship between uniaxial compressive strength (UCS) and other properties of rock. Evaluation of rock parameters such as cohesion, Young’s modulus, angle of friction, and Poisson’s ratio is required for numerical modeling of rock. Finding empirical relations between these parameters with UCS has been focused. On the basis of the ranges of UCS available in the literature for different types of rocks, 14 empirical relations were developed on the basis of a random number–generation technique, and the relations were validated with available experimental data. The proposed relations were compared with available empirical equations in the literature. The proposed relations can be used to solve various problems in rocky sites by numerical modeling with acceptable accuracy. These results will also be useful in making practical decisions at the stage of preliminary site investigation works.

Interpolation of the gaps in current maps generated by high-frequency radar

ABSTRACT High-frequency (HF) radars sense the surface of ocean using electromagnetic waves and provide data of current vectors over a large spatial domain in the form of current maps at short intervals of time. Due to reasons such as failure of hardware or software, vandalism, or environmental issues, small or large gaps can always be found in collected data. In-filling of such missing information calls for special procedures owing to the typical sensing and reporting style of these current maps. In this article HF radar observations made at five different locations in India are analysed with the aim of providing consistent and synthesized information after noticing that there were significant missing values. This task is accomplished by selecting the most appropriate and state of the art methods of spatial data interpolation. An exhaustive experimentation in this regard showed that the statistical method of ‘inverse distance weighting’ or the soft computing technique of artificial neural network can work satisfactorily for this purpose.

Variability and Statistical Distribution of Ocean Current Observations

The ocean currents are created and influenced by various forces like winds, waves, Coriolis force, temperature gradients, salinity differences, tides, etc., and near the shoreline, the local bathymetry interacts and changes the patterns. The variability of ocean currents includes the ones arising in observation, the noise and gaps in data, the causal forces, due to interaction with waves, shoreline and bathymetry, among others. In this chapter, a study of the variability of the ocean currents on different temporal scales is presented. The recorded hourly mean speed and direction of the current and the meridional and zonal velocities calculated from the same exhibit different variability. The spatial resolution of hydrodynamic ocean models, at the present state of development, is large, and the evaluation of current by such models is generally underestimated. Thus, occurrence of extreme current events evaluated from these models is also underestimated. Representation of the general distribution of ocean currents by some well-known statistical distribution would enable estimation of realistic parameters of the distribution using recorded values from a relatively short period. Thus, it might be possible to evaluate the properties of the current field and occurrence of extreme events from the limited data. In this chapter, fitting some standard distribution to observed current records is also explored.