time-history analysis is defined as a kind of dynamic analysis increasingly used in design of structures and evaluation of existing ones. one of the important issues in the time-history analysis is selecting earthquake records. in this case, seismic design provisions states that time histories shall have similar source mechanisms, geological and seismological features with region under study. a...

this study aims to develop seismic fragility curves for a typical pile-supported wharf. fragility curve is one of the popular tools in seismic performance evaluation of a structure. the software flac2d was used to simulate the seismic performance of the wharf structure. using eight time history records, occurred in past, as seismic loading, incremental dynamic analysis (ida) was applied for sei...

recently, two of the authors have introduced the collapse capacity spectrummethodology, which allows the assessment of the collapse capacity of highly inelastic p-deltasensitive regular frame structures without performing non-linear time-history analyses. the mainingredient of this method is the collapse capacity spectrum, which represents the seismic collapsecapacity of an inelastic non-deteri...

Dynamic programming (DP) is a useful tool for solving many control problems, but for its complexity in computation, traditional DP control algorithms are not satisfactory in fact. So we must look for a new method which not only has the advantages of DP but also is easier in computation. In this paper, approximate dynamic programming (ADP) based controller system has been used to solve a ship he...

Tactical planning in hospitals involves elective patient admission planning and the allocation of hospital resource capacities. We propose a method to develop a tactical resource allocation and patient admission plan that takes stochastic elements into consideration, thereby providing robust plans. Our method is developed in an Approximate Dynamic Programming (ADP) framework and copes with mult...

This paper studies the problem of dynamic scheduling of multi-mode sensor resources for the problem of classification of multiple unknown objects. Because of the uncertain nature of the object types, the problem is formulated as a partially observed Markov decision problem with a large state space. The paper describes a hierarchical algorithm approach for efficient solution of sensor scheduling...

Linear Methods are often used to compute approximate solutions to dynamic models, as these models often cannot be solved analytically. Linear methods are very popular, as they can easily be implemented. Also, they provide a useful starting point for understanding more elaborate numerical methods. It shall be described here first for the example of a simple real business cycle model, including h...

We present an approximation method for inventory management under multiple suppliers and stochastic lead times. The method is based on parametric dynamic programming using convex piecewise linear approximations of the differential cost function in approximate relative value iteration steps and works for a range of stochastic control problems with linear dynamics and convex piecewise linear imme...

In this chapter, a new ADP algorithm integrating (1) systematic basis function construction, (2) a linear programming (LP) approach in DP, (3) adaptive basis function selection and (4) bootstrapping, is developed and applied to oil production problems. The procedure requires the solution of a large-scale dynamic system, which is accomplished using a subsurface flow simulator, for function evalu...

Problems of optimal structural design for minimum deflection, maximum fundamental frequency and maximum stability are considered in this note. The optimality conditions for these problems consist of nonlinear differential equations which usually require a numerical solution [1-16]. An alternate approach based on the Ritz method is presented here, in which one works with an approximate expressio...