Construction of stochastic simulation metamodels with segmented polynomials

Metamodels are an important tool in simulation analysis as they can provide insight about the behavior of the simulation response. Modeling the response with low-degree polynomial segments allows the identification of different behavior zones and the parameters still have relation with the physical world. The purpose of this paper is to extend the use of segmented polynomial functions for simulation metamodeling, where the segments have at most identical value and slope at the breaks. Our approach is to build segmented polynomials metamodels where the hypothesis of degree and continuity of splines are less exigent, allowing more flexibility of the approximation. When breaks are known, constrained least squares are used for metamodel estimation, taking into account the linear formulation of the problem. If breaks have to be estimated, the unconstrained nonlinear regression theory is used, when it can be applied. Otherwise, the estimation is performed using an iterative algorithm which is applied repeatedly in a cyclic manner for estimating the breaks, and jackknifing yields the confidence intervals.

A Vs-Based Logistic Regression Method for Liquefaction Evaluation

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.

Assimilating sparse data in glaciological inverse problems

<p>Most of the existing work on solving inverse problems in glaciology has assumed that the observational data used to constrain the model are spatially dense. This assumption is very convenient because it means that the model-data misfit term in the objective functional can be written as an integral. In many scenarios, however, the computational mesh can locally be much finer than the observational grid, or the observations can have large patches of missing data. Moreover, pretending as if the observations are a globally-defined continuous field obscures valuable information about the number of independent measurements we have. It is then impossible to apply a posteriori sanity checks on the expected model-data misfit from regression theory. Here we'll describe some recent work we've done on assimilating sparse point data into ice flow models and how this allows us to be more rigorous about the statistical interpretation of our results. For now we are focusing on the kinds of inverse problems that have been solved in the glaciology literature for a long time -- inferring rheology and basal friction from surface velocities. But these developments open up the possibility of assimilating new sources of data, such as measurements from strain gauges or ice cores.</p>

Using regression theory to solve some problems regarding ship repairing and maintenance activities in shipyards

The owners will always try to reduce the ships maintenance works timeframe clearly aimed at reducing the period during which the ship has no income. In turn, the shipyard also will try to reduce the timeframe for the maintenace works carried out on board of the drydocked ships and alongside berths, clearly aimed at revenue maximizing by contracting a larger number of ships for drydocking. The objective was to find a method (using regression theory) for the ships drydocking and/or berthed maintenance works carried out in the shiprepairs shipyards time frame that exploits the information held by the shipyard in their maintenance and repairs works portfolio performed for various customers over time.