Intelligent prediction model based on genetic algorithm and support vector machine for evaluation of mining-induced building damage

Značajke čimbenika koji utječu na štetu nastalu na zgradama zbog iskapanja zemlje su različite, nelinearne i multi linearne. Za bolji opis tih čimbenika razvijen je inteligentni model zasnovan na potpornom vektorskom stroju (SVM) kojim se može predvidjeti šteta na zgradama nastala podzemnim iskapanjem. Na temelju opsežnog razmatranja geoloških, rudarskih i građevnih faktora, 10 ih je pažljivo odabrano. Posebice je, kao glavna ulazna varijabla u predloženom modelu, upotrebljen stupanj oštećenja građevine od opeke i betona, nastao podzemnim iskapanjem. Stupanj oštećenja i najšira pukotina građevinske konstrukcije od opeke i betona izabrani su kao izlazne varijable u predloženom modelu. Ukupno su odabrana 32 tipična slučaja oštećenja zgrada u Kini zbog iskapanja zemlje te upotrebljena kao podaci za uvježbavanje (training data). Funkcija radijalne baze (radial basis function – RBF) upotrebljena je za SVM klasifikaciju i primjenu modela regresije s najvećom širinom pukotine. Kako bi primjena modela bila što šira i njegova sposobnost predviđanja što veća, za izbor učinkovitih parametara za SVM model upotrebljen je genetski algoritam (GA), i tada je izvršena odgovarajuća identifikacija šest grupa uzoraka. Rezultati klasifikacije i regresije pokazuju da se predloženim modelom, koji koristi GA-SVM, može predvidjeti šteta na konstrukciji od opeke i betona, nastala iskapanjem zemlje, a rezultati procjene u skladu su s praćenim podacima. To navodi na praktičnost primjene predloženog modela u rješavanju različitih inženjerskih problema.Characteristics of factors influencing mining-induced building damage are diverse, nonlinear, and multi-linear. For a better description of these factors, an intelligent prediction model for building damage induced by underground mining is developed based on the support vector machine (SVM). Based on a comprehensive consideration of geological, mining, and building factors, 10 factors are carefully selected. In particular, the mining-induced damage grade of the brick-concrete building structure is used as the main input variable in the proposed model. The damage grade and largest crack width of the brick-concrete building structure are selected as output variables in the proposed model. A total of 32 typical cases of mining-induced building damage in China are collected and used as training data. The radial basis function (RBF) is used for SVM classification and the application of the largest-crack-width regression model. To improve the model’s generalizability and predictive capacity, the genetic algorithm (GA) is adopted to select effective parameters for the SVM model, and then the corresponding identification of six group samples is performed. The classification and regression results show that the proposed prediction model using GA-SVM can predict the mining-induced damage of a brick-concrete building structure, and the evaluation results show good agreement with monitored data. This suggests the practicality of the proposed model in a wide range of engineering problems

Mechanisms of motor learning: by humans, for robots

Whenever we perform a movement and interact with objects in our environment, our central nervous system (CNS) adapts and controls the redundant system of muscles actuating our limbs to produce suitable forces and impedance for the interaction. As modern robots are increasingly used to interact with objects, humans and other robots, they too require to continuously adapt the interaction forces and impedance to the situation. This thesis investigated the motor mechanisms in humans through a series of technical developments and experiments, and utilized the result to implement biomimetic motor behaviours on a robot. Original tools were first developed, which enabled two novel motor imaging experiments using functional magnetic resonance imaging (fMRI). The first experiment investigated the neural correlates of force and impedance control to understand the control structure employed by the human brain. The second experiment developed a regressor free technique to detect dynamic changes in brain activations during learning, and applied this technique to investigate changes in neural activity during adaptation to force fields and visuomotor rotations. In parallel, a psychophysical experiment investigated motor optimization in humans in a task characterized by multiple error-effort optima. Finally a computational model derived from some of these results was implemented to exhibit human like control and adaptation of force, impedance and movement trajectory in a robot

Mean field method for the support vector machine regression

This paper deals with two subjects. First, we will show how support vector machine (SVM) regression problem can be solved as the maximum a posteriori prediction in the Bayesian framework. The second part describes an approximation technique that is useful in performing calculations for SVMs based on the mean field algorithm which was originally proposed in Statistical Physics of disordered systems. One advantage is that it handle posterior averages for Gaussian process which are not analytically tractable