Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Cover of the book

2021 | OriginalPaper | Chapter

Artificial Intelligence Techniques for Smart City Applications

Authors : Daniel Luckey, Henrieke Fritz, Dmitrii Legatiuk, Kosmas Dragos, Kay Smarsly

Published in: Proceedings of the 18th International Conference on Computing in Civil and Building Engineering

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

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.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
next chapter Use of Artificial Intelligence in a Regulated Design Environment – A Beam Design Example
Literature
Acatech – National Academy of Science and Engineering: Industry 4.0, Urban development and German international development cooperation (Acatech position paper), Herbert Utz Verlag, Munich, Germany (2015)
Adadi, A., Berrada, M.: Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access 6(2018), 52138–52160 (2018)CrossRef
Abdeljaber, O., Avci, O., Kiranyaz, S., Boashash, B., Sodano, H., Inman, D.: 1-D CNNs for structural damage detection: verification on a structural health monitoring benchmark data. Neurocomputing 275(2018), 1308–1317 (2018)CrossRef
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.R., Samek, W.: On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS ONE 10(7), e0130140 (2015)CrossRef
Bao, Y., Chen, Z., Wei, S., Xu, Y., Tang, Z., Li, H.: The state of the art of data science and engineering in structural health monitoring. Engineering 5(2), 234–242 (2019)CrossRef
Barredo Arrieta, A., Diaz Rodriguez, N., Del Ser, J., Bennetot, A., Tabik, S., et al.: Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58(2020), 82–115 (2019)
Bilek, J., Mittrup, I., Smarsly, K., Hartmann, D.: Agent-based concepts for the holistic modeling of concurrent processes in structural engineering. In: Proceedings of the 10th ISPE International Conference on Concurrent Engineering: Research and Applications, Madeira, Portugal, 26 July 2003
Bisby, L.A., Briglio, M.B.: ISIS educational module 5: an introduction to structural health monitoring. SAMCO Final Report. Winnipeg, Manitoba, Canada: ISIS Canada (2006)
Burkov, A.: The Hundred-Page Machine Learning Book (2019). ISBN-13: 978-1999579500
Cha, Y.-J., Choi, W., Büyüköztürk, O.: Deep learning-based crack damage detection using convolutional neural networks. Comput. Aided Civil Infrastruct. Eng. 32(5), 361–378 (2017)CrossRef
Das, A., Suthar, D., Leung, C.: Machine learning based crack mode classification from unlabeled acoustic emission waveform features. Cem. Concr. Res. 121(2019), 42–57 (2019)CrossRef
Diez, A., Khoa, N.L.D., Makki Alamdari, M., Wang, Y., Chen, F., Runcie, P.: A clustering approach for structural health monitoring on bridges. J. Civil Struct. Health Monit. 6(2016), 1–17 (2016)
Dragos, K., Smarsly, K.: Distributed adaptive diagnosis of sensor faults using structural response data. Smart Mater. Struct. 25(10), 105019 (2016)CrossRef
Fritz, H.: An explainable artificial intelligence model coupling deep learning and blockchain technology. Bachelor thesis. Chair of Computing in Civil Engineering, Bauhaus University Weimar, Germany (2019)
Gardner, P., Barthorpe, R.J., Lord, C.: The development of a damage model for the use in machine learning driven SHM and comparison with conventional SHM methods. In: Proceedings of the International Conference on Noise and Vibration Engineering 2016 (ISMA 2016) and International Conference on Uncertainty in Structural Dynamics (USD 2016), Leuven, Belgium, 13 September 2016 (2016)
Ghiasi, R., Torkzadeh, P., Noori, M.: A machine-learning approach for structural damage detection using least square support vector machine based on a new combinational kernel function. Struct. Health Monit. 15(3), 302–316 (2016)CrossRef
Gunawan, F., Soewito, B., Surantha, N., Tuga, M.: One more reason to reject manuscript about machine learning for structural health monitoring. In: Proceedings of the 2018 Indonesian Association for Pattern Recognition (INAPR) International Conference, Jakarta, Indonesia, 7 September 2018 (2018)
Gui, G., Pan, H., Lin, Z., Li, Y., Yuan, Z.: Data-driven support vector machine with optimization techniques for structural health monitoring and damage detection. KSCE J. Civil Eng. 21(2), 523–534 (2017)CrossRef
Gunning, D., Aha, D.W.: DARPA’s explainable artificial intelligence program. AI Mag. 40(2), 44–58 (2019)
Guo, X., Shen, Z., Zhang, Y., Wu, T.: Review on the application of artificial intelligence in smart homes. Smart Cities 2(3), 402–420 (2019)CrossRef
Hartmann, D., Smarsly, K., Law, K.H.: Coupling sensor-based structural health monitoring with finite element model updating for probabilistic lifetime estimation of wind energy converter structures. In: Proceedings of the 8th International Workshop on Structural Health Monitoring, Stanford, CA, USA, 13 September 2011 (2011)
Haugeland, J.: Artificial Intelligence. The Very Idea. MIT Press, Cambridge (1987)
Hoang, N.-D., Liao, K.-W., Tran, X.-L.: Estimation of scour depth at bridges with complex pier foundations using support vector regression integrated with feature selection. J. Civil Struct. Health Monit. 8(3), 431–442 (2018)CrossRef
Hutter, M.: Universal Artificial Intelligence – Sequential Decisions Based on Algorithmic Probability. Springer-Verlag GmbH, Heidelberg (2005)MATH
Johnson, P., Robinson, P., Philpot, S.: Type, tweet, tap, and pass: how smart city technology is creating a transactional citizen. Gov. Inf. Q. 37(1), 101414 (2019)CrossRef
Joshuva, A., Sugumaran, V.: A study of various blade fault conditions on a wind turbine using vibration signals through histogram features. J. Eng. Sci. Technol. 13(1), 102–121 (2018)
Joshuva, A., Aslesh, A., Sugumaran, V.: State of the art of structural health monitoring of wind turbines. Int. J. Mech. Sci. 9(5), 95–112 (2019)
Kabalci, E., Kabalci, Y.: From Smart Grid to Internet of Energy, 1st edn. Academic Press, London (2019)
Kelley, T.: Symbolic and sub-symbolic representations in computational models of human cognition: what can be learned from biology? Theor. Psychol. 13(6), 847–860 (2003)CrossRef
Langley, P.: The changing science of machine learning. Mach. Learn. 82(3), 275–279 (2011)MATHCrossRef
Legg, S., Hutter, M.: Universal intelligence: a definition of machine intelligence. Mind. Mach. 17(4), 391–444 (2007)CrossRef
Li, R., Gu, H., Hu, B., She, Z.: Multi-feature fusion and damage identification of large generator stator insulation based on Lamb wave detection and SVM method. Sensors 19(7), 3733 (2019)CrossRef
Martins, J.: Towards smart city innovation under the perspective of software-defined networking, artificial intelligence and big data. RTIC – Revista de tecnologia da informação e comunicação 8(2), 1–7 (2018)
Mittrup, I., Smarsly, K., Hartmann, D., Bettzieche, V.: An agent-based approach to dam monitoring. In: Proceedings of the 20th CIB W78 Conference on Information Technology in Construction, Auckland, New Zealand, 23 April 2003 (2003)
Mohanty, S.: Everything you wanted to know about smart cities. IEEE Consum. Electron. Mag. 5(3), 60–70 (2016)CrossRef
Mohapatra, B.: Machine learning applications to smart city. ACCENTS Trans. Image Proces. Comput. Vis. 5(14), 1–6 (2019)CrossRef
Mohri, M., Rostamizadeh, A., Talwalkar, A.: Foundations of Machine Learning. MIT Press, Cambridge (2012)MATH
Montavon, G., Samek, W., Müller, K.-R.: Methods for interpreting and understanding deep neural networks. Digit. Signal Proc. 73(2018), 1–15 (2018)MathSciNet
Nazarian, E., Taylor, T., Weifeng, T., Ansari, F.: Machine-learning-based approach for post event assessment of damage in a turn-of-the-century building structure. J. Civil Struct. Health Monit. 8(2), 237–251 (2018)CrossRef
Nguyen, V.V., Smarsly, K., Hartmann, D.: A computational steering approach towards sensor placement optimization for structural health monitoring using multi-agent technology and evolutionary algorithms. In: Proceedings of the 6th International Workshop on Structural Health Monitoring, Stanford, CA, USA, 11 September 2007 (2007)
Nomura, Y., Shigemura, K.: Development of real-time screening system for structural surface damage using object detection and generative model based on deep learning. J. Soc. Mater. Sci. 68(3), 250–257 (2019)CrossRef
Nosratabadi, S., Mosavi, A., Keivani, R., Ardabili, S., Aram, F.: State of the art survey of deep learning and machine learning models for smart cities and urban sustainability. In: Proceedings of the 18th International Conference on Global Research and Education Inter-Academia, Budapest, Hungary, 4 September 2019 (2019)
Ogie, R.I., Perez, P., Dignum, V.: Smart infrastructure: an emerging frontier for multidisciplinary research. ICE Smart Infrastruct. Constr. 170(1), 8–16 (2017)
Pan, H., Azimi, M., Lin, Z., Yan, F.: Time-frequency based data-driven structural diagnosis and damage detection for cable-stayed bridges. Journal of Bridge Engineering 23(6), 04018033 (2018)CrossRef
Ribeiro, M.T., Singh, S., Guestrin, C.: Why should I trust you?: Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 13 Aug 2016 (2016)
Russel, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn. Pearson Education Ltd., Harlow (2014)
Salehi, H., Burgueno, R.: Emerging artificial intelligence methods in structural engineering. Eng. Struct. 171(2018), 170–189 (2018)CrossRef
Santos, A., Figueiredo, E., Silva, M., Santos, R., Sales, C., Costa, J.: Genetic-based EM algorithm to improve the robustness of Gaussian mixture models for damage detection in bridges. Struct. Control Health Monit. 24(3), e1886 (2016)CrossRef
Senniappan, V., Subramanian, J., Papageorgiou, E., Mohan, S.: Application of fuzzy cognitive maps for crack categorization in columns of reinforced concrete structures. Neural Comput. Appl. 28(1), 107–117 (2016)
Shalev-Shwartz, S., Ben-David, S.: Understanding Machine Learning. From Theory to Algorithms. Cambridge University Press, New York (2014)MATHCrossRef
Sierra-Perez, J., Torres Arredondo, M.A., Alvarez-Montoya, J.: Damage detection methodology under variable load conditions based on strain field pattern recognition using FBGs, nonlinear principal component analysis, and clustering techniques. Smart Mater. Struct. 27(1), 015002 (2017)CrossRef
Smarsly, K., Lehner, K., Hartmann, D.: Structural health monitoring based on artificial intelligence techniques. In: Proceedings of the International Workshop on Computing in Civil Engineering, Pittsburgh, PA, USA, 24 July 2007 (2007)
Smarsly, K., Law, K.H., König, M.: Resource-efficient wireless monitoring based on mobile agent migration. In: Proceedings of the SPIE (Vol. 7984): Health Monitoring of Structural and Biological Systems 2011, San Diego, CA, USA, 6 March 2011 (2011)
Smarsly, K., Law, K.H., Hartmann, D.: A multiagent-based collaborative framework for a self-managing structural health monitoring system. ASCE J. Comput. Civil Eng. 26(1), 76–89 (2012)CrossRef
Smarsly, K., Law, K.H.: A migration-based approach towards resource-efficient wireless structural health monitoring. Adv. Eng. Inform. 27(4), 625–635 (2013)CrossRef
Smarsly, K., Dragos, K., Wiggenbrock, J.: Machine learning techniques for structural health monitoring. In: Proceedings of the 8th European Workshop on Structural Health Monitoring (EWSHM), Bilbao, Spain, 5 July 2016 (2016)
Soomro, K., Bhutta, M., Khan, Z., Tahir, M.: Smart city big data analytics: an advanced review. Wiley Interdisc. Rev. Data Min. Knowl. Discovery 9(5), e1319 (2019)
Steiner, M., Legatiuk, D., Smarsly, K.: A support vector regression-based approach towards decentralized fault diagnosis in wireless structural health monitoring systems. In: Proceedings of the 12th International Workshop on Structural Health Monitoring. Stanford, CA, USA, 10 September 2019 (2019)
Suleiman, A.R., Nehdi, M.L.: Modeling self-healing of concrete using hybrid genetic algorithm-artificial neural network. Materials 10(2), 135 (2017)CrossRef
Sysyn, M., Gerber, U., Nabochenko, O., Li, Y., Kovalchuk, V.: Indicators for common crossing structural health monitoring with track side inertial measurements. Acta Polytechnica 59(2), 170–181 (2019)CrossRef
Tibaduiza, D., Torres Arredondo, M.A., Oyaga, J., Anaya, M., Pozo, F.: A damage classification approach for structural health monitoring using machine learning. Complexity 2018, 5081283 (2018)CrossRef
Vashisht, R., Viji, H., Sundararajan, T., Mohankumar, D., Sarada, S.: Structural health monitoring of cantilever beam, a case study – using Bayesian neural network and deep learning. In: Proceedings of the 13th International Conference on Systems, Athens, Greece, 22 April 2018 (2018)
Vitola, J., Tibaduiza, D., Anaya, M., Pozo, F.: Structural damage detection and classification based on machine learning algorithms. In: Proceedings of the 8th European Workshop On Structural Health Monitoring (EWSHM), Bilbao, Spain, 5 July 2016 (2016)
Vitola, J., Pozo, F., Tibaduiza, D., Anaya, M.: A sensor data fusion system based on k-nearest neighbor pattern classification for structural health monitoring applications. Sensors 17(2), 417 (2017a)CrossRef
Vitola, J., Pozo, F., Tibaduiza, D., Anaya, M.: Distributed piezoelectric sensor system for damage identification in structures subjected to temperature changes. Sensors 17(6), 1252 (2017b)CrossRef
Zhao, Z., Yua, M., Dong, S.: Damage location detection of the CFRP composite plate based on neural network regression. In: Proceedings of the 7th Asia-Pacific Workshop on Structural Health Monitoring, Hong Kong, China, 12 November 2018 (2019)
Metadata
Title
Artificial Intelligence Techniques for Smart City Applications
Authors
Daniel Luckey
Henrieke Fritz
Dmitrii Legatiuk
Kosmas Dragos
Kay Smarsly
Copyright Year
2021
Book
Proceedings of the 18th International Conference on Computing in Civil and Building Engineering

Print ISBN: 978-3-030-51294-1

Electronic ISBN: 978-3-030-51295-8

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-51295-8_1