Explainable Domain-Specific Artificial Intelligence for Bridge Engineering and Design

The use of artificial intelligence (AI) is currently being discussed and evaluated across the board in research and pray tice in all industries. This paper first provides the necessary background knowledge on general AI and then elaborates the necessity of developing domain-specific AI as well as explainable AI (XAI) for the calculation and design practice in civil engineering. The role of the civil engineering domain at the interface with AI in terms of data and modeling and design philosophy is discussed. The three examples of (i) computation of Kirchhoff slabs with physics-informed neural networks, (ii) calibration of prior models for parametric bridge design from databases, and (iii) AI-assisted conceptual design of bridges illustrate the application of domain-specific machine and deep learning and XAI with special focus on concrete and bridge de. sign. In particular, physics-informed AI (piKI) represents an alternative to established numerical methods with special consideration of available simulation and experimental data and becomes explainable and comprehensible via the use of XAI. This paves the way for the use of piKI models in civil engineering as digital twins of a structure over its life cycles.