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Machine learning for design and analysis of steel frame halls The use of digital tools in design and construction processes increases efficiency, especially for recurring tasks and processes, such as the dimensioning of components or structures, which follow similar patterns. This paper develops an artificial intelligence (AI)‐based assistant for use in the pre‐design and design of steel frame halls as a prototypical co‐pilot. For this purpose, the necessary database of steel frame halls is first compiled from project archives of TRAGRAUM Partnerschaft Beratender Ingenieure mbB. This will be examined in the context of data analyses according to the decisive description parameters and similarities, so that finally suitable AI surrogate models for the prediction of the hall parameters are trained by means of regression analyses. Furthermore, an interface to the finite element program SOFiSTiK is implemented, which passes the predictions of the AI regarding the parameters of the steel frame halls to a parameterized input file and triggers the calculation and design. In the course of the calibration of the AI substitute models, in addition to the approximation qualities, possible safety margins to be provided are also discussed in the context of this article. This article shows that the prototypical co‐pilot for the preliminary design of steel frame halls could be successfully implemented. Furthermore the AI newly generated hall frames can be classified as stable with respect to the validation considerations. An outlook on future further developments of this approach as well as the transfer to other applications complete this paper. [ABSTRACT FROM AUTHOR]

KI/ML‐based Analysis and Interpretation of the IABSE‐Bridge Collapse Database Statistical analyses of bridge collapse data show that concrete bridges collapse significantly less frequently than bridges made of steel or wood. Since the main causes of bridge collapses worldwide are floods and associated fluvial processes, such as scouring, debris flows, etc. and impacts, it is reasonable to assume that the high dead load of concrete bridges leads to an overall more robust behavior in these events. This paper will examine whether the IABSE collapse database confirms this hypothesis and whether indications of further causes can be identified. For this purpose, the IABSE collapse database is examined using artificial intelligence and machine learning (AI/ML) methods. However, the AI/ML analysis does not confirm the previous thesis. The reasons for the rejection of the thesis, such as the representativeness of the data, are also discussed. An extension of the database for events with large numbers of collapses is recommended. [ABSTRACT FROM AUTHOR]

Parametrization in bridge construction for the application of artificial intelligence For the application of digital methods in the execution phase, the acquisition of exisiting structures or the construction progress with the aid of LiDAR is a suitable method to subsequently detect components as automatically as possible and e. g., to evaluate them for construction progress monitoring. Machine learning methods are suitable for component detection and are used for various classification problems. One problem is the semantic segmentation of point clouds, in which the individual points are assigned to specific component classes. To solve the segmentation with sufficient accuracy and to make predictions on real data, a large amount of training data is needed. This process is also called "Scan to BIM". In this paper, synthetic data for training a convolutionalneural network (CNN) is provided in a highly parameterized and fully automatic way. An approach for generating variations of bridge models based with the visual programming interface Dynamo in Autodesk Revit is proposed. At the beginning, guidelines for the definition of parameters are evaluated to subsequently use them in adaptive templates. Attributes are assigned to each component and grouped into a feature structure when exported to the IFC data format. This approach resulted in numerous variations of bridge models that can be used for semantic segmentation of point clouds and training of a CNN. [ABSTRACT FROM AUTHOR]

Great importance is attached to 'pressure‐volume records' for the execution, documentation and billing of rock grouting. In this context, special digital data management systems are now available which can provide data in a structured and consistent format that is also suitable for artificial intelligence (AI) approaches. Using datasets from a tunnel project in Scandinavia, this paper shows that artificial neural networks can be used to reliably predict the evolution of pressure‐volume records or the volume of grout injected at the end in the interests of construction site efficiency. Taking into account the technical feasibility of using AI to support tunnel grouting, we then show which contractual modifications would be required in order to make effective use of corresponding developments. [ABSTRACT FROM AUTHOR]

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 practice 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 design. 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. [ABSTRACT FROM AUTHOR]