Penggunaan artificial neural network (ANN) untuk memprediksi nilai demand capacity ratio (DCR) pada struktur atas jembatan rangka bina marga kelas A bentang 45 meter

Abstract

Demand capacity ratio (DCR) is the ratio between the number of demand to the capacity available in a system in a given period of time. If the DCR exceeds 1, it means that the bridge has exceeded its capacity and has the potential to collapse. In order to avoid the risk of accidents on bridges, it is very important to know the DCR value of a bridge. However, the DCR calculation process is too detailed because it includes many variables such as vehicle weight, speed, wind, and other environmental factors. This is quite time-consuming because it requires a process of trial and error for each step. This study aims to approach using artificial neural networks (ANN) to predict DCR so that the process is shorter. ANN is used to predict the DCR value on the upper structure of the class A steel truss bridge of Highways with a span of 45 meters. As input data, the ultimate stress, span length, and steel profile area are used, while the output is the DCR value. Input and output data were obtained from SAP 2000 modelling results by varying bridge dimensions, material properties, and profile types after a combination of loading on the bridge. The result obtained is that the ANN model made is able to predict the DCR value of the upper structure of the truss bridge and does not experience overfitting. But the value of the referenced accuracy parameter is still large so that the resulting predictions are not good and require further research by increasing the amount of data and trying other ANN architectures.