The detection of insulation failures in buildings could potentially conserve energy supplies and improve future designs. Improvements to thermal insulation in buildings include the development of models to assess fabric gain -heat flux through exterior walls in the building- and heating processes. Thermal insulation standards are now contractual obligations in new buildings, and the energy efficiency of buildings constructed prior to these regulations has yet to be determined. The main assumption is that it will be based on heat flux and conductivity measurement. Diagnostic systems to detect thermal insulation failures should recognize anomalous situations in a building that relate to insulation, heating and ventilation. This highly relevant issue in the construction sector today is approached through a novel intelligent procedure that can be programmed according to local building and heating system regulations and the specific features of a given climate zone. It is based on the following phases. Firstly, the dynamic thermal performance of dif\-ferent variables is specifically modeled. Secondly, an exploratory projection pursuit method called Cooperative Maximum-Likelihood Hebbian Learning extracts the relevant features. Finally, a supervised neural model and identification techniques constitute the model for the diagnosis of thermal insulation failures in building due to the heat flux through exterior walls, using relevant features of the data set. The reliability of the proposed method is validated with real datasets from several Spanish cities in winter time.
The paper reports on the determination of basic mechanical material parameters of several concrete and alkali activated concrete and fly ash mixtures intended for the construction of segmental lining used in TBM tunneling. The results of an extensive experimental program are discussed first. The principal attention is accorded to the experimental determination of specific fracture energy from a load-deflection curve, which, when compared to numerical simulations, shows certain inconsistency with the measurements of other material data. This is supported by teh derivation of the data from inverse analysis employing the elements of soft-computing. Dynamic simulation of crack propagation experiments is suggested to reconcile the essential differences and to identify the most important impacts affecting the results of experimental measurements. and Obsahuje seznam literatury