Abstract
This research focuses on the use of adaptive artificial neural network system for evaluating the skid resistance value (British Pendulum Number; BPN) of the glass fiber-reinforced tiling materials. During the creation of the neural model, four main factors were considered: fiber, calcium carbonate content, sand blasting, and polishing properties of the specimens. The model was trained, tested, and compared with the on-site test results. As per the comparison of the outcomes of the study, the analysis and on-site test results showed that there is a great potential for the prediction of BPN of glass fiber-reinforced tiling materials by using developed neural system.
Highlights
Many researches have been focused on the skid and slip resistance properties of the construction materials in order to lessen accident rates in developed countries. ese countries, especially the USA, where severe environmental conditions occur, have made huge expenses incurred by slipping accidents. e cost of the accidents in the USA is 37.3 and 64.41 billion dollars in 1985 and 1994, respectively [1]
It can be concluded that the Arti cial neural networks (ANNs)-2 model developed in this study can be used for the estimation of the British Pendulum Number (BPN) value and so for the determination of skid resistance and relevant measurements. e polishing property is found to be the most important input parameter followed by sand blasting based on the weight and the biases of the trained network
The BPN values were measured by changing the additive percentage, ber ratio, polishing, and sand blasting conditions and utilized in the simulation of the ANN and MR models. e input parameters used in the ANN and MR models are four common additive percentages (CC 3%, calcium carbonate (CC) 5%, CC 10%, and ber ratio) and two separate experiment conditions. e output parameter in both models is the measured British Pendulum Number (BPN)
Summary
Many researches have been focused on the skid and slip resistance properties of the construction materials in order to lessen accident rates in developed countries. ese countries, especially the USA, where severe environmental conditions occur, have made huge expenses incurred by slipping accidents. e cost of the accidents in the USA is 37.3 and 64.41 billion dollars in 1985 and 1994, respectively [1]. Ose test tools basically consist of a vertical load, a projected speed, and a friction measurement wheel On contrary to their simple structure, testing via this equipment sometimes becomes complex and expensive, if large-size construction materials are planned for testing. In construction material and geotechnical engineering problems, as with many areas of civil engineering, ANN has been used widely with high accuracy to predict and model the resistance values [15,16,17,18]. Within the scope of this study, the British pendulum tester was preferred for the determination of the skid resistance value of the materials for posing experimental results and the comparison with the neural network analysis results. Within the scope of this study, the British pendulum tester was preferred for the determination of the skid resistance value of the materials for posing experimental results and the comparison with the neural network analysis results. e prediction capability of the neural model has been studied
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