3 results
Now showing1 - 3 of 3
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Explainable Artificial Intelligence to Advance Structural Health MonitoringIn recent years, structural health monitoring (SHM) applications have significantly been enhanced, driven by advancements in artificial intelligence (AI) and machine learning (ML), a subcategory of AI. Although ML algorithms allow detecting patterns and features in sensor data that would otherwise remain undetected, the generally opaque inner processes and black-box character of ML algorithms are limiting the application of ML to SHM. Incomprehensible decision-making processes often result in doubts and mistrust in ML algorithms, expressed by engineers and stakeholders. In an attempt to increase trust in ML algorithms, explainable artificial intelligence (XAI) aims to provide explanations of decisions made by black-box ML algorithms. However, there is a lack of XAI approaches that meet all requirements of SHM applications. This chapter provides a review of ML and XAI approaches relevant to SHM and proposes a conceptual XAI framework pertinent to SHM applications. First, ML algorithms relevant to SHM are categorized. Next, XAI approaches, such as transparent models and model-specific explanations, are presented and categorized to identify XAI approaches appropriate for being implemented in SHM applications. Finally, based on the categorization of ML algorithms and the presentation of XAI approaches, the conceptual XAI framework is introduced. It is expected that the proposed conceptual XAI framework will provide a basis for improving ML acceptance and transparency and therefore increase trust in ML algorithms implemented in SHM applications.Publicationtype: Journal Article151 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Artificial Intelligence Techniques for Smart City Applications(Springer, 2020-08-18); ; ; ; Recent developments in artificial intelligence (AI), in particular machine learning (ML), have been significantly advancing smart city applications. Smart infrastructure, which is an essential component of smart cities, is equipped with wireless sensor networks that autonomously collect, analyze, and communicate structural data, referred to as “smart monitoring”. AI algorithms provide abilities to process large amounts of data and to detect patterns and features that would remain undetected using traditional approaches. Despite these capabilities, the application of AI algorithms to smart monitoring is still limited due to mistrust expressed by engineers towards the generally opaque AI inner processes. To enhance confidence in AI, the “black-box” nature of AI algorithms for smart monitoring needs to be explained to the engineers, resulting in so-called “explainable artificial intelligence” (XAI). However, when aiming at improving the explainability of AI algorithms through XAI for smart monitoring, the variety of AI algorithms requires proper categorization. Therefore, this review paper first identifies objectives of smart monitoring, serving as a basis to categorize AI algorithms or, more precisely, ML algorithms for smart monitoring. ML algorithms for smart monitoring are then reviewed and categorized. As a result, an overview of ML algorithms used for smart monitoring is presented, providing an overview of categories of ML algorithms for smart monitoring that may be modified to achieve explainable artificial intelligence in civil engineering.Publicationtype: Conference Paper124 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , BIM-Based Concrete Printing(Springer, 2020-08); ; ; ; Extrusion-based additive manufacturing (AM), or three-dimensional (3D) printing, has matured into a set of advanced methods to automate the construction of large-scale concrete structures, while minimizing cost and material waste. However, current AM data models are inadequate for 3D concrete printing due to insufficient incorporation of information on the relationships between process, material, and geometry, which may cause redundancy, information loss, and inconsistencies. Aiming at improving AM data modeling for concrete printing, this paper proposes a metamodeling approach for AM of concrete structures, referred to as “printing information modeling”, which takes advantage of building information modeling (BIM). As will be shown in this paper, the BIM-based printing information model, serving as a metamodel, incorporates the digital data triplet of process, material, and geometry parameters to generate computer numerical control (CNC) commands that may readily be used for concrete printing. A validation test is performed, which instantiates the printing information model, using a BIM model, for generating CNC commands, enabling optimal digital data exchange from BIM models to concrete printers. As a result of this study, it is demonstrated that printing information modeling adequately defines the information required for AM of concrete structures using a BIM-based approach, showing promising potential to improve current AM data modeling efforts.Publicationtype: Conference Paper142