• Energy Research

Structural health monitoring (SHM) allows the acquisition of information on the structural integrity of any mechanical system by processing data, measured through a set of sensors, in order to estimate relevant mechanical parameters and indicators of performance. Herein we present a method to perform the cost–benefit optimization of a sensor network by defining the density, type, and positioning of the sensors to be deployed. The effectiveness (benefit) of an SHM system may be quantified by means of information theory, namely through the expected Shannon information gain provided by the measured data, which allows the inherent uncertainties of the experimental process (i.e., those associated with the prediction error and the parameters to be estimated) to be accounted for. In order to evaluate the computationally expensive Monte Carlo estimator of the objective function, a framework comprising surrogate models (polynomial chaos expansion), model order reduction methods (principal component analysis), and stochastic optimization methods is introduced. Two optimization strategies are proposed: the maximization of the information provided by the measured data, given the technological, identifiability, and budgetary constraints; and the maximization of the information–cost ratio. The application of the framework to a large-scale structural problem, the Pirelli tower in Milan, is presented, and the two comprehensive optimization methods are compared.

Structural health monitoring (SHM) allows the acquisition of information on the structural integrity of any mechanical system by processing data, measured through a set of sensors, in order to estimate relevant mechanical parameters and indicators of performance. Herein we present a method to perform the cost–benefit optimization of a sensor network by defining the density, type, and positioning of the sensors to be deployed. The effectiveness (benefit) of an SHM system may be quantified by means of information theory, namely through the expected Shannon information gain provided by the measured data, which allows the inherent uncertainties of the experimental process (i.e., those associated with the prediction error and the parameters to be estimated) to be accounted for. In order to evaluate the computationally expensive Monte Carlo estimator of the objective function, a framework comprising surrogate models (polynomial chaos expansion), model order reduction methods (principal component analysis), and stochastic optimization methods is introduced. Two optimization strategies are proposed: the maximization of the information provided by the measured data, given the technological, identifiability, and budgetary constraints; and the maximization of the information–cost ratio. The application of the framework to a large-scale structural problem, the Pirelli tower in Milan, is presented, and the two comprehensive optimization methods are compared.

Globally, buildings are responsible for a significant share in energy consumption and greenhouse gas emissions profiles. Various attempts are undertaken to increase the energy efficiency of buildings and reduce their environmental impact. In semi-continental climate conditions with very hot summers and extremely cold winters, buildings should be carefully designed to ensure efficient harnessing of solar energy and reducing energy loss due to poor insulation and inappropriate use of materials. Amidst the fast development of the construction industry, different façade systems are used in Kazakhstan. In several cases, the choice of the façade materials is defined not by performance but rather by economic aspects and physical appearance. This project aimed to investigate various types of façades adopted in the construction of residential buildings and assess their performance in terms of their impact on buildings’ energy consumption. The preliminary results indicate that there are five main types of façades widely used. Five different models were therefore built using energy simulation software and the respective energy consumption data were estimated. The results testify that buildings with brickwork (clay bricks) and stonework (travertine) façades were more energy efficient than those with brickwork (silica bricks), aluminum composite panels and decorated plaster façades.

Globally, buildings are responsible for a significant share in energy consumption and greenhouse gas emissions profiles. Various attempts are undertaken to increase the energy efficiency of buildings and reduce their environmental impact. In semi-continental climate conditions with very hot summers and extremely cold winters, buildings should be carefully designed to ensure efficient harnessing of solar energy and reducing energy loss due to poor insulation and inappropriate use of materials. Amidst the fast development of the construction industry, different façade systems are used in Kazakhstan. In several cases, the choice of the façade materials is defined not by performance but rather by economic aspects and physical appearance. This project aimed to investigate various types of façades adopted in the construction of residential buildings and assess their performance in terms of their impact on buildings’ energy consumption. The preliminary results indicate that there are five main types of façades widely used. Five different models were therefore built using energy simulation software and the respective energy consumption data were estimated. The results testify that buildings with brickwork (clay bricks) and stonework (travertine) façades were more energy efficient than those with brickwork (silica bricks), aluminum composite panels and decorated plaster façades.