The evaluation of probabilistic constraints in the probabilistic structural design optimization (PSDO) problem can be carried out using either the conventional reliability index approach (RIA) or the more recently proposed performance measure approach (PMA).
Depending on the temporal discretization of the contact constraints, this may require an iterative solution process involving the evaluation of the constraints at predictor configurations.
Diehl et al. [ 35] presented another SQP algorithm which does not require the evaluation of the exact constraint Jacobian matrix.
In particular, the evaluation of the importance sampling and particle update equations is prohibitive in the presence of a code constraint on the symbols.
Because the evaluation of the probabilistic constraints in a sub-optimization level causes non-convergence behavior for some problems as indicated in the literature, an alternative formulation based on one-level is developed considering the optimality conditions of the β-computation by which the probabilistic constraint appears in the first level reliability-based design optimization formulation.
Developing a typology constitutes an essential step in any realistic evaluation of constraints and opportunities that farmers face and helps forwarding appropriate technological solutions, policy interventions (Ganpat and Bekele [2001], Timothy [1994]; Vanclay [2005]), and comprehensive environmental assessment (Andersen et al. [2009]).
In order to facilitate the evaluation of material distribution, these functions are classified into component function and constraint function.
The evaluation of bite force in craniodental FE models is often done by sampling reaction forces of nodal constraints at the bite points; however, the range of variation in bite force magnitude estimated by a single node constraint versus constraint distributed over an area is unknown.
Like the reliability-based design optimization (RBDO), the NRBDO application for practical engineering structure is hindered by the huge computational effort involved in the repeated evaluation of non-probabilistic constraints.
(2007) clearly stated (in the opening paragraph of our paper) that the ability to predict the most efficient action to achieve a goal within the constraints of a given situation does not require the attribution of mental states to others, but may rely simply on the evaluation of observable facts: the action, the goal state and the situational constraints.
