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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Hutter, M.: Universal Artificial Intelligence – Sequential Decisions Based on Algorithmic Probability. Springer-Verlag GmbH, Heidelberg (2005)
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)
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)
Langley, P.: The changing science of machine learning. Mach. Learn. 82(3), 275–279 (2011)
Legg, S., Hutter, M.: Universal intelligence: a definition of machine intelligence. Mind. Mach. 17(4), 391–444 (2007)
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)
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)
Mohapatra, B.: Machine learning applications to smart city. ACCENTS Trans. Image Proces. Comput. Vis. 5(14), 1–6 (2019)
Mohri, M., Rostamizadeh, A., Talwalkar, A.: Foundations of Machine Learning. MIT Press, Cambridge (2012)
Montavon, G., Samek, W., Müller, K.-R.: Methods for interpreting and understanding deep neural networks. Digit. Signal Proc. 73(2018), 1–15 (2018)
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)
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)
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)
Organisation for Economic Co-operation and Development (OECD): Enhancing the contribution of digitalisation to the smart cities of the future (2019). https://one.oecd.org/document/CFE/RDPC/URB(2019)1/REV1/en/pdf. Accessed 20 Jan 2020
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)
PricewaterhouseCoopers: Creating the smart cities of the future (2019). https://www.pwc.com/gx/en/sustainability/assets/creating-the-smart-cities-of-the-future.pdf. Accessed 21 Jan 2020
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)
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)
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)
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)
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)
Smarsly, K., Law, K.H.: A migration-based approach towards resource-efficient wireless structural health monitoring. Adv. Eng. Inform. 27(4), 625–635 (2013)
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)
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)
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)
United Nations Economic and Social Council: Smart cities and infrastructure (2016). https://unctad.org/meetings/en/SessionalDocuments/ecn162016d2_en.pdf. Accessed 25 Jan 2020
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)
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)
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)
Acknowledgments
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Luckey, D., Fritz, H., Legatiuk, D., Dragos, K., Smarsly, K. (2021). Artificial Intelligence Techniques for Smart City Applications. In: Toledo Santos, E., Scheer, S. (eds) Proceedings of the 18th International Conference on Computing in Civil and Building Engineering. ICCCBE 2020. Lecture Notes in Civil Engineering, vol 98. Springer, Cham. https://doi.org/10.1007/978-3-030-51295-8_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-51295-8_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-51294-1
Online ISBN: 978-3-030-51295-8
eBook Packages: EngineeringEngineering (R0)