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The decision making of a suitable heating, ventilating and air conditioning system's set-point temperature is an energy and environmental challenge in our society. In the present paper, a general framework to define such temperature based on a dynamic adaptive comfort algorithm is proposed. Due to the fact that the thermal comfort of the occupants of a building has different ranges of acceptability, this method is applied to learn such comfort temperature with respect to the running mean temperature and therefore to decide the suitable range of indoor temperature. It is demonstrated that this solution allows to dynamically build an adaptive comfort algorithm, an algorithm based on the human being's thermal adaptability, without applying the traditional theory. The proposed methodology based on the K-Nearest-Neighbour algorithm was tested and compared with data from an experimental thermal comfort field study carried out in a mixed mode building in the south-western area of Spain and with the Support Vector Machine method. The results show that K-Nearest-Neighbour algorithm represents the pattern of thermal comfort data better than the traditional solution and that it is a suitable method to learn the thermal comfort area of a building and to define the set-point temperature for a heating, ventilating and air-conditioning system. [ABSTRACT FROM AUTHOR]

This paper discusses a new Sequential Linear Programming (SLP) algorithm denoted as LESLP (Linearization Error Sequential Linear Programming). The new algorithm implements an advanced strategy to choose the move limits which are defined by limiting the difference between the original nonlinear problem and its linearized counterpart. Besides, LESLP includes a trust region model to increase the design freedom and to improve the overall efficiency of the optimization process. LESLP is tested in five lay-out optimization problems of skeletal structures (i.e. bar trusses and frames) where the objective is to minimize the weight of the structure. It is obviously intended that the objective function does not require structural analysis. The new algorithm is compared to other SLP based techniques and globally convergent optimization methods like Sequential Quadratic Programming (SQP). Results indicate that LESLP is competitive with recently published algorithms and commercial softwares. Also, the new algorithm is robust with respect to starting designs. [ABSTRACT FROM AUTHOR]

A new method named as Statistical Average Algorithm is developed for solving the structural dynamic compound inverse problem, which means to identify structural parameters with unknown input or to inverse input time history with unknown structural parameters. By taking the mechanical characteristic of the ground motion as an additional condition, an iterative algorithm based on the least-squares technique is developed so that the input process and structural parameters can be correctly determined using only output measurements. The procedure of the proposed method is discussed in detail, and a mathematical proof is presented as well in the appendix. A practical input situation such as earthquake, ambient vibration is considered in the numerical examples to verify the accuracy, reliability and robustness of the proposed algorithm. Considering the background of the practical application, both of the noise-free and noise-included output responses are considered in the numerical examples. In all cases, the proposed method identifies the structural parameters and reconstructs the input process rationally. [ABSTRACT FROM AUTHOR]