BARIUM CONCENTRATIONS IN ROCK SALT BY LASER INDUCED BREAKDOWN SPECTROSCOPY

Time-REsolved Laser Induced Breakdown Spectroscopy (TRELIBS) was used to determine the elemental concentration of barium in Texas Dome rock salt. TRELIBS allows for an efficient and in situ concentration analysis technique that detects a wide range of elements with no sample preparation. TRELIBS measurements were made in the 545nm to 594nm wavelength range. The proximity of a strong barium emission line (553.5481 nm) to the sodium doublet (588.9950 nm and 589.5924 nm) allowed for measurement within a single frame of the spectrograph. This barium emission line was compared to the sodium doublet for relative intensity. A homemade calibration sample containing known amounts of barium and sodium was used to determine the relative concentrations. By approximating the sodium content in the rock salt as 50%, we determined the absolute concentration of barium in the salt to be (0.13±0.03)%.

SAMPLING BIAS IN EVALUATING THE PROBABILITY OF SEISMICALLY INDUCED SOIL LIQUEFACTION WITH SPT & CPT CASE HISTORIES

Several deterministic and probabilistic methods are used to evaluate the probability of seismically induced liquefaction of a soil. The probabilistic models usually possess some uncertainty in that model and uncertainties in the parameters used to develop that model. These model uncertainties vary from one statistical model to another. Most of the model uncertainties are epistemic, and can be addressed through appropriate knowledge of the statistical model. One such epistemic model uncertainty in evaluating liquefaction potential using a probabilistic model such as logistic regression is sampling bias. Sampling bias is the difference between the class distribution in the sample used for developing the statistical model and the true population distribution of liquefaction and non-liquefaction instances. Recent studies have shown that sampling bias can significantly affect the predicted probability using a statistical model. To address this epistemic uncertainty, a new approach was developed for evaluating the probability of seismically-induced soil liquefaction, in which a logistic regression model in combination with Hosmer-Lemeshow statistic was used. This approach was used to estimate the population (true) distribution of liquefaction to non-liquefaction instances of standard penetration test (SPT) and cone penetration test (CPT) based most updated case histories. Apart from this, other model uncertainties such as distribution of explanatory variables and significance of explanatory variables were also addressed using KS test and Wald statistic respectively. Moreover, based on estimated population distribution, logistic regression equations were proposed to calculate the probability of liquefaction for both SPT and CPT based case history. Additionally, the proposed probability curves were compared with existing probability curves based on SPT and CPT case histories.