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Creating software that is easy for users to navigate and incorporates the findings of an artificial neural network is a viable approach for applying the benefits of machine learning in practical situations. Machine learning-based software can provide a more user-friendly interface, allowing users without extensive knowledge of machine learning to leverage its power. Moreover, ML-based software can facilitate the deployment of machine learning models in real-world scenarios, making it easier to integrate with other systems and technologies. The approach of practical implementation of ML in real problems enables engineers to obtain precise results from ANN without having to conduct numerous experiments or perform complicated mathematical calculations. The proposed ML-based software in this study was developed based on C# which is a general-purpose, high-level programming language supporting multiple paradigms. Users can obtain the estimated damage level in a bridge based on the location, material, structural system of a bridge, AADT, ignition source, combustible type, and face of a bridge to which fire is exposed.<\/p>\n
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