Artificial Intelligence Techniques for Smart City Applications

First published in
Lecture notes in civil engineering  
Number in series
98 LNCE
Start Page
3
End Page
15
Citation
International ICCCBE and CIB W78 Joint Conference on Computing in Civil and Building Engineering 2020
Contribution to Conference
ICCCBE-CIBW78 2020  
Publisher DOI
10.1007/978-3-030-51295-8_1
Scopus ID
2-s2.0-85088469789
Publisher
Springer
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.
Subjects
Artificial intelligence (AI)
Explainable artificial intelligence (XAI)
Machine learning (ML)
Smart cities
Smart infrastructure
Smart monitoring
DDC Class
004: Informatik
600: Technik
620: Ingenieurwissenschaften
690: Hausbau, Bauhandwerk
720: Architektur
More Funding Information
The authors gratefully acknowledge the support offered by the German Research Foundation (DFG) under grants SM 281/9-1, SM 281/14-1, and SM 281/15-1. This research is also partially supported by the German Federal Ministry of Transport and Digital Infrastructure (BMVI) under grant VB18F1022A. Any opinions, findings, conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of DFG or BMVI.