Geotechnology in the Age of AI: The Convergence of Geotechnical Data Analytics and Machine Learning
DOI:
https://doi.org/10.63995/ZOAF3555Keywords:
Geotechnical Engineering; Machine Learning; Data Analytics; Predictive Modeling; AI in Infrastructure; Risk Assessment; Sensor TechnologyAbstract
The integration of artificial intelligence (AI) technologies, particularly machine learning (ML), with geotechnical engineering is transforming the landscape of infrastructure development and maintenance. This abstract explores the confluence of geotechnical data analytics and machine learning, illustrating how this synergy enhances predictive modeling and risk assessment in geotechnical applications. Recent advancements in sensor technology and data acquisition methods have resulted in the generation of vast amounts of geotechnical data. These datasets, characterized by their volume, variety, and velocity, present a unique opportunity for the application of machine learning techniques. Machine learning algorithms, particularly deep learning, have demonstrated exceptional capability in identifying patterns and making predictions from large, complex datasets. When applied to geotechnical data, these algorithms can significantly improve the accuracy of predicting soil behavior, foundation performance, and potential geohazards. This paper reviews several case studies where machine learning models have been successfully implemented in geotechnical engineering. These include the use of convolutional neural networks for the classification of soil types, the application of recurrent neural networks for predicting landslide susceptibility, and reinforcement learning for optimizing the design of tunneling projects. The results from these studies indicate that machine learning not only enhances the efficiency and effectiveness of geotechnical investigations but also contributes to safer and more cost-effective engineering solutions. In conclusion, the convergence of geotechnical data analytics and machine learning heralds a new era in geotechnology. By harnessing the power of AI, geotechnical engineers can tackle complex challenges with greater precision, ultimately leading to more reliable and resilient infrastructure systems. This interdisciplinary approach not only pushes the boundaries of traditional geotechnical engineering but also sets a new standard for future research and practice in the field.
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Karim C Abbaspour, Jing Yang, Ivan Maximov, Rosi Siber, Konrad Bogner, Johanna Mieleitner, Juerg Zobrist, and Raghavan Srinivasan. “Modelling hydrology and water quality in the prealpine/alpine Thur watershed using SWAT”. In: Journal of hydrology 333.2-4 (2007), pp. 413– 430. DOI: https://doi.org/10.1016/j.jhydrol.2006.09.014
Ethem Alpaydin. Introduction to machine learning. MIT press, 2020.
Christopher M Bishop. “Pattern recognition and machine learning”. In: Springer google schola 2 (2006), pp. 1122–1128.
Bonaccorso Giuseppe. “Machine learning algorithms”. In: PacktPublishing Ltd (2017).
Leo Breiman. “Random forests”. In: Machine learning 45 (2001), pp. 5–32. DOI: https://doi.org/10.1023/A:1010933404324
F Necati Catbas and Masoud Malekzadeh. “A machine learning-based algorithm for processing massive data collected from the mechanical components of movable bridges”. In: Automation in Construction 72 (2016), pp. 269–278. DOI: https://doi.org/10.1016/j.autcon.2016.02.008
Jui-Sheng Chou, Chih-Fong Tsai, Anh-Duc Pham, and Yu-Hsin Lu. “Machine learning in concrete strength simulations: Multi-nation data analytics”. In: Construction and Building materials 73 (2014), pp. 771–780. DOI: https://doi.org/10.1016/j.conbuildmat.2014.09.054
Corinna Cortes and Vladimir Vapnik. “Support-vector networks”. In: Machine learning 20 (1995), pp. 273–297. DOI: https://doi.org/10.1007/BF00994018
Braja M Das and Nagaratnam Sivakugan. Principles of foundation engineering. Cengage learning, 2018.
Sarat Kumar Das and Prabir Kumar Basudhar. “Prediction of residual friction angle of clays using artificial neural network”. In: Engineering Geology 100.3-4 (2008), pp. 142–145. DOI: https://doi.org/10.1016/j.enggeo.2008.03.001
Kok-Kwang Phoon. Reliability-based design in geotechnical engineering: computations and applications. CRC Press, 2008. DOI: https://doi.org/10.1201/9781482265811
Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep learning. MIT press, 2016.
Kasthurirangan Gopalakrishnan. “Deep learning in data-driven pavement image analysis and automated distress detection: A review”. In: Data 3.3 (2018), p. 28. DOI: https://doi.org/10.3390/data3030028
TY Lin, N Cercone, Jitender S Deogun, Vijay V Raghavan, Amartya Sarkar, and Hayri Sever. Data mining: Trends in research and development. Springer, 1997, pp. 9–45. DOI: https://doi.org/10.1007/978-1-4613-1461-5_2
Seyed Jamalaldin Seyed Hakim, Jamaloddin Noorzaei, MS Jaafar, Mohammed Jameel, and Mohammad Mohammadhassani. “Application of artificial neural networks to predict compressive strength of high strength concrete”. In: Int. J. Phys. Sci 6.5 (2011), pp. 975–981.
Lijian Yang, Zhujun Wang, Songwei Gao, Meng Shi, and Bangli Liu. “Magnetic flux leakage image classification method for pipeline weld based on optimized convolution kernel”. In: Neurocomputing 365 (2019), pp. 229–238. DOI: https://doi.org/10.1016/j.neucom.2019.07.083
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, et al. An introduction to statistical learning. Vol. 112. Springer, 2013. DOI: https://doi.org/10.1007/978-1-4614-7138-7
Chaopeng Shen, Eric Laloy, Amin Elshorbagy, Adrian Albert, Jerad Bales, Fi-John Chang, Sangram Ganguly, Kuo-Lin Hsu, Daniel Kifer, Zheng Fang, et al. “HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community”. In:Hydrology and Earth System Sciences 22.11 (2018), pp. 5639–5656. DOI: https://doi.org/10.5194/hess-22-5639-2018
Michael I Jordan and Tom M Mitchell. “Machine learning: Trends, perspectives, and prospects”. In: Science 349.6245 (2015), pp. 255–260. DOI: https://doi.org/10.1126/science.aaa8415
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. “Deep learning”. In: nature 521.7553 (2015), pp. 436–444. DOI: https://doi.org/10.1038/nature14539
Rana Acharyya, Arindam Dey, and Bimlesh Kumar. “Finite element and ANN-based prediction of bearing capacity of square footing resting on the crest of c-φ soil slope”. In: International Journal of Geotechnical Engineering (2018). DOI: https://doi.org/10.1080/19386362.2018.1435022
Wei-Yin Loh. “Classification and regression trees”. In: Wiley interdisciplinary reviews: data mining and knowledge discovery 1.1 (2011), pp. 14–23. DOI: https://doi.org/10.1002/widm.8
Tom M Mitchell. “Artificial neural networks”. In: Machine learning 45.81 (1997), p. 127.
Ming-Syan Chen, Jiawei Han, and Philip S. Yu. “Data mining: an overview from a database perspective”. In: IEEE Transactions on Knowledge and data Engineering 8.6 (1996), pp. 866–883. DOI: https://doi.org/10.1109/69.553155
Mahesh Pal. “Random forest classifier for remote sensing classification”. In: International journal of remote sensing 26.1 (2005), pp. 217–222. DOI: https://doi.org/10.1080/01431160412331269698
Kok-Kwang Phoon and Jianye Ching. Risk and reliability in geotechnical engineering. CRC Press Boca Raton, FL, USA, 2015.
Stuart J Russell and Peter Norvig. Artificial intelligence: a modern approach. Pearson, 2016.
Thomas Glade. “Landslide hazard assessment and historical landslide data—an inseparable couple?” In: The use of historical data in natural hazard assessments. Springer, 2001, pp. 153–168. DOI: https://doi.org/10.1007/978-94-017-3490-5_12
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al. “Mastering the game of Go with deep neural networks and tree search”. In: nature 529.7587 (2016), pp. 484–489. DOI: https://doi.org/10.1038/nature16961
Hoon Sohn. “Effects of environmental and operational variability on structural health monitoring”. In: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 365.1851 (2007), pp. 539–560. DOI: https://doi.org/10.1098/rsta.2006.1935
Alex J Smola and Bernhard Schölkopf. “A tutorial on support vector regression”. In: Statistics and computing 14 (2004), pp. 199–222. DOI: https://doi.org/10.1023/B:STCO.0000035301.49549.88
Oliver Ava, Muhammad Oscar, and Tommy George. “The Impact and Prevention of Latch-up in CMOS in VLSI Design”. In: Fusion of Multidisciplinary Research, An International Journal (FMR) 1.1 (2020), pp. 1–13. DOI: https://doi.org/10.63995/VGRD5263
Ilya Sutskever, Oriol Vinyals, and Quoc V Le. “Sequence to sequence learning with neural networks”. In: Advances in neural information processing systems 27 (2014).
Vladimir Vapnik. The nature of statistical learning theory. Springer science & business media, 2013.
Rachel Cook, Jonathan Lapeyre, Hongyan Ma, and Aditya Kumar. “Prediction of compressive strength of concrete: critical comparison of performance of a hybrid machine learning model with standalone models”. In: Journal of Materials in Civil engineering 31.11 (2019), p. 04019255. DOI: https://doi.org/10.1061/(ASCE)MT.1943-5533.0002902
Renpeng Chen, Pin Zhang, Huaina Wu, Zhiteng Wang, and Zhiquan Zhong. “Prediction of shield tunneling-induced ground settlement using machine learning techniques”. In: Frontiers of Structural and Civil Engineering 13.6 (2019), pp. 1363–1378. DOI: https://doi.org/10.1007/s11709-019-0561-3
Haoyu Yuze and He Bo. “Microbiome Engineering: Role in Treating Human Diseases”. In: Fusion of Multidisciplinary Research, An International Journal (FMR) 1.1 (2020), pp. 14–24. DOI: https://doi.org/10.63995/GDYK1331
Issam El Naqa and Martin J Murphy. “What is machine learning?” In: (2015). DOI: https://doi.org/10.1007/978-3-319-18305-3_1
N Abramson, D Braverman, and G Sebestyen. “Pattern recognition and machine learning”. In: IEEE Transactions on Information Theory 9.4 (1963), pp. 257–261. DOI: https://doi.org/10.1109/TIT.1963.1057854
David Ruppert. The elements of statistical learning: data mining, inference, and prediction. Taylor & Francis, 2004. DOI: https://doi.org/10.1198/jasa.2004.s339
R Fernandes De Mello and M Antonelli Ponti. “Statistical learning theory”. In: Machine Learning 3 (2018). DOI: https://doi.org/10.1007/978-3-319-94989-5
Mirjam J Knol, Ingeborg van der Tweel, Diederick E Grobbee, Mattijs E Numans, and Mirjam I Geerlings. “Estimating interaction on an additive scale between continuous determinants in a logistic regression model”. In: International journal of epidemiology 36.5 (2007), pp. 1111–1118. DOI: https://doi.org/10.1093/ije/dym157
Peter C Austin. “A comparison of regression trees, logistic regression, generalized additive models, and multivariate adaptive regression splines for predicting AMI mortality”. In: Statistics in medicine 26.15 (2007), pp. 2937–2957. DOI: https://doi.org/10.1002/sim.2770
Kelvyn Jones and Neil Wrigley. “Generalized additive models, graphical diagnostics, and logistic regression”. In: Geographical Analysis 27.1 (1995), pp. 1–18. DOI: https://doi.org/10.1111/j.1538-4632.1995.tb00333.x
Chih-Chung Chang and Chih-Jen Lin. “LIBSVM: a library for support vector machines”. In: ACM transactions on intelligent systems and technology (TIST) 2.3 (2011), pp. 1–27. DOI: https://doi.org/10.1145/1961189.1961199
Madjid Tavana, Weiru Liu, Paul Elmore, Frederick E Petry, and Brian S Bourgeois. “A practical taxonomy of methods and literature for managing uncertain spatial data in geographic information systems”. In: Measurement 81 (2016), pp. 123–162. DOI: https://doi.org/10.1016/j.measurement.2015.12.007
Arvind K Mishra, Silao V Ramteke, Phalguni Sen, and Amit Kumar Verma. “Random forest tree based approach for blast design in surface mine”. In: Geotechnical and Geological Engineering 36 (2018), pp. 1647–1664. DOI: https://doi.org/10.1007/s10706-017-0420-8
Nesim Yilmaz, Tuncer Demir, Safak Kaplan, and Sevilin Demirci. “Demystifying Big Data Analytics in Cloud Computing”. In: Fusion of Multidisciplinary Research, An International Journal (FMR) 1.1 (2020), pp. 25–36. DOI: https://doi.org/10.63995/DOPV8398
Lei Zhu, Tianrui Li, and Shengdong Du. “Ta-stan: A deep spatial-temporal attention learning framework for regional traffic accident risk prediction”. In: 2019 international joint conference on neural networks (IJCNN). IEEE. 2019, pp. 1–8. DOI: https://doi.org/10.1109/IJCNN.2019.8852212
Ulrich Pavlique Etoughe Kongo. “Urban land cover classification from high resolution Geoeye1 imagery using a lidarbased digital surface model”. PhD thesis. Stellenbosch: Stellenbosch University, 2015.
Ebrahim Emami, Touqeer Ahmad, George Bebis, Ara Nefian, and Terry Fong. “Crater detection using unsupervised algorithms and convolutional neural networks”. In: IEEE Transactions on Geoscience and Remote Sensing 57.8 (2019), pp. 5373–5383. DOI: https://doi.org/10.1109/TGRS.2019.2899122
Selamat Riadi, Selamat Triono, Syahril Syahril, and Dicky Nofriansyah. “Effectiveness of Metcaognitive Learning’s Model in Engineering”. In: International Journal of Engineering and Advanced Technology (IJEAT) 9.1 (2019), pp. 4438–4443. DOI: https://doi.org/10.35940/ijeat.A1457.109119
Yuchu Qin, Shihua Li, Tuong-Thuy Vu, Zheng Niu, and Yifang Ban. “Synergistic application of geometric and radiometric features of LiDAR data for urban land cover mapping”. In: Optics express 23.11 (2015), pp. 13761–13775. DOI: https://doi.org/10.1364/OE.23.013761
Friedrich Stephanie and Louisa Karl. “Incorporating Renewable Energy Systems for a New Era of Grid Stability”. In: Fusion of Multidisciplinary Research, An International Journal (FMR) 1.1 (2020), pp. 37–49. DOI: https://doi.org/10.63995/UVPR3703
Mohammad Rezaee, Masoud Mahdianpari, Yun Zhang, and Bahram Salehi. “Deep convolutional neural network for complex wetland classification using optical remote sensing imagery”. In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11.9 (2018), pp. 3030–3039. DOI: https://doi.org/10.1109/JSTARS.2018.2846178
Rajib Kumar Bhattacharjya. “OPTIMIZATION TECHNIQUES IN SOLVING GROUNDWATER MANAGEMENT MODELS WITH SPECIAL EMPHASIS ON UR-BAN AQUIFERS”. In: Environmentally Sustainable Urban Ecosystems (2012), p. 69.
Yushi Chen, Zhouhan Lin, Xing Zhao, Gang Wang, and Yanfeng Gu. “Deep learning-based classification of hyperspectral data”. In: IEEE Journal of Selected topics in applied earth observations and remote sensing 7.6 (2014), pp. 2094–2107. DOI: https://doi.org/10.1109/JSTARS.2014.2329330
Jungrack Kim and Jan-Peter Muller. “Tree and building detection in dense urban environments using automated processing of IKONOS image and LiDAR data”. In: International Journal of Remote Sensing 32.8 (2011), pp. 2245–2273. DOI: https://doi.org/10.1080/01431161003698237
Fenghua Huang, Ying Yu, and Tinghao Feng. “Automatic extraction of urban impervious surfaces based on deep learning and multi-source remote sensing data”. In: Journal of Visual Communication and Image Representation 60 (2019), pp. 16–27. DOI: https://doi.org/10.1016/j.jvcir.2018.12.051
Nakamura Shuto, Akari Chiyo, Himari Ken, and Sato Tanaka. “Quantum Materials to the Pioneering Future of Computing and Communication”. In: Fusion of Multidisciplinary Research, An International Journal (FMR) 1.1 (2020), pp. 50–62.
Shiqing Wei, Shunping Ji, and Meng Lu. “Toward automatic building footprint delineation from aerial images using CNN and regularization”. In: IEEE Transactions on Geoscience and Remote Sensing 58.3 (2019), pp. 2178–2189. DOI: https://doi.org/10.1109/TGRS.2019.2954461
Jawan Singh Rawat and Ramesh Chandra Joshi. “Remote-sensing and GIS-based landslidesusceptibility zonation using the landslide index method in Igo River Basin, Eastern Himalaya, India”. In: International journal of remote sensing 33.12 (2012), pp. 3751–3767. DOI: https://doi.org/10.1080/01431161.2011.633121
Jinwei Dong, Xiangming Xiao, Michael A Menarguez, Geli Zhang, Yuanwei Qin, David Thau, Chandrashekhar Biradar, and Berrien Moore III. “Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine”. In: Remote sensing of environment 185 (2016), pp. 142–154. DOI: https://doi.org/10.1016/j.rse.2016.02.016
Omar Y Al-Jarrah, Paul D Yoo, Sami Muhaidat, George K Karagiannidis, and Kamal Taha. “Efficient machine learning for big data: A review”. In: Big Data Research 2.3 (2015), pp. 87–93. DOI: https://doi.org/10.1016/j.bdr.2015.04.001
Mohammad Haji Gholizadeh, Assefa M Melesse, and Lakshmi Reddi. “A comprehensive review on water quality parameters estimation using remote sensing techniques”. In: Sensors 16.8 (2016), p. 1298. DOI: https://doi.org/10.3390/s16081298
Viktor Slavkovikj, Steven Verstockt, Wesley De Neve, Sofie Van Hoecke, and Rik Van de Walle. “Hyperspectral image classification with convolutional neural networks”. In: Proceedings of the 23rd ACM international conference on Multimedia. 2015, pp. 1159–1162. DOI: https://doi.org/10.1145/2733373.2806306
Joseph J Erinjery, Mewa Singh, and Rafi Kent. “Mapping and assessment of vegetation types in the tropical rainforests of the Western Ghats using multispectral Sentinel-2 and SAR Sentinel1 satellite imagery”. In: Remote Sensing of Environment 216 (2018), pp. 345–354. DOI: https://doi.org/10.1016/j.rse.2018.07.006
Hassan Ghassemian. “A review of remote sensing image fusion methods”. In: Information Fusion 32 (2016), pp. 75–89. DOI: https://doi.org/10.1016/j.inffus.2016.03.003
LA Bressani, EB Simões, AT Ogura, IJ Boesing, DS Hanauer, Novo Hamburgo UpSensor, and G Balbinot. “A monitoring system for landslides and geotechnical works using statistical and artificial intelligence models”. In: Proceedings of 10th International Symposium on Field Measurements in Geomechanics, Rio de Janeiro, Brazil. 2018.
Azad Rasul, Heiko Balzter, Claire Smith, John Remedios, Bashir Adamu, José A Sobrino, Manat Srivanit, and Qihao Weng. “A review on remote sensing of urban heat and cool islands”. In: Land 6.2 (2017), p. 38. DOI: https://doi.org/10.3390/land6020038
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