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

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.Original Abstract: Znacajke cimbenika koji utjecu na stetu nastalu na zgradama zbog iskapanja zemlje su razlicite, nelinearne i multi linearne. Za bolji opis tih cimbenika razvijen je inteligentni model zasnovan na potpornom vektorskom stroju (SVM) kojim se moze predvidjeti steta na zgradama nastala podzemnim iskapanjem. Na temelju opseznog razmatranja geoloskih, rudarskih i gradevnih faktora, 10 ih je pazljivo odabrano. Posebice je, kao glavna ulazna varijabla u predlozenom modelu, upotrebljen stupanj ostecenja gradevine od opeke i betona, nastao podzemnim iskapanjem. Stupanj ostecenja i najsira pukotina gradevinske konstrukcije od opeke i betona izabrani su kao izlazne varijable u predlozenom modelu. Ukupno su odabrana 32 tipicna slucaja ostecenja zgrada u Kini zbog iskapanja zemlje te upotrebljena kao podaci za uvjezbavanje (training data). Funkcija radijalne baze (radial basis function - RBF) upotrebljena je za SVM klasifikaciju i primjenu modela regresije s najvecom sirinom pukoti