Publisher DOI: 10.1007/978-3-030-81716-9_16
Title: Explainable Artificial Intelligence to Advance Structural Health Monitoring
Language: English
Authors: Luckey, Daniel 
Fritz, Henrieke 
Legatiuk, Dmitrii 
Peralta Abadia, Jose 
Walther, Christian 
Smarsly, Kay 
Keywords: Artificial intelligence (AI); Explainable artificial intelligence (XAI); Machine learning (ML); Structural health monitoring (SHM)
Issue Date: 2022
Source: Structural Integrity 21: 331-346 (2022)
Abstract (english): 
In 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.
URI: http://hdl.handle.net/11420/10806
ISSN: 2522-560X
Journal: Structural integrity 
Institute: Digitales und autonomes Bauen B-1 
Document Type: Article
Project: BIM-basierte Informationsmodellierung zur semantischen Abbildung intelligenter Bauwerksmonitoringsysteme 
Datengestützte Analysemodelle für schlanke Bauwerke unter Nutzung von Explainable Artificial Intelligence 
Fehlertolerantes, drahtloses Bauwerksmonitoring basierend auf Frameanalyse und Deep Learning 
Semi-probabilistische, sensorbasierte Bemessungs- und Entwurfskonzepte für intelligente Bauwerke 
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