Judgment model for identifying the type of electric molten mark in fire

Abstract

Various methods have been explored to differentiate these electric molten marks including primary molten marks (PMMs, the cause of fires) and secondary molten marks (SMMs, were caused by the fires). However, only a small number of specimens were tested by related methods and none of these methods are reproducible. In order to build an objective and developmental method for analyzing electric molten marks, many molten marks were made under different current and heat treatment temperature and time. And some descriptors were extracted from metallographic images of primary molten marks (PMMs) and secondary molten marks (SMMs) with help of digital image processing method. For purpose of identifying the type of electric molten mark, some judgment models were established with the aid of support vector machine (SVM). There were 380 molten marks from the experiment, 288 of them were PMM and 92 of them were SMM. All of them were used in training model, 5 PMMs and 5 SMMs were used to test these models. Then, these judgment models were selected and optimized by different methods. Thereby, an optimal model was found, the classification accuracy of this model was above 66.2%. Finally, this model was tested by actual fire for the forensic application, the accuracy was 100%.