TC309 Machine Learning

Machine Learning and Big Data

Short name: Machine Learning (TC309)

  • Introduction

Most geomaterials exhibit complex and uncertain behaviour due to the complex natural processes associated with their formation and deposition. As the result there are many problems in geotechnical engineering that challenge accurate solution and/or prediction with conventional analytical and numerical methods. This is particularly the case when there are contributing factors from in situ geology or characteristics of a specific phenomenon that do not necessarily have physical relationship to each other. In these circumstances machine learning (ML) and artificial intelligence (AI) based methods can have a significant impact in providing meaningful solutions. Early works on the application of different ML and AI methods in geotechnics dates back to early 90’s. However, in recent years, with pervasive developments in computer hardware and software, application of ML and AI in soil mechanics and geotechnical engineering have gained particular pace, and with growing interest in their application, new hopes and horizons have emerged. To this end, TC309 of the ISSMGE has been formed in order to coordinate, organize and direct ISSMGE members’ efforts in this field.


  • Task Forces

This task force category focused on the technical activities related to investigation and applications in the area of machine learning and big data in geoscience. Task force 1) and 2) are attempting to make the accessible benchmark datasets in geoscience and the acquistion of geotechnical big data . Task forces 3), 4), and 5) are mainly associated with the different stages of a geotechnical engineering project from site investigation and soil property evaluation, design, and construction. Task force 6) is to standardize ML/AI and build a robust approach/framework to support industry-wide confidence in application of AI and ML in geosciences.

      1) Development of benchmark datasets (Task leader: Wengang Zhang)

Members: Wengang Zhang, Zijun Cao (to be updated, not limited to TC309 members, other contributors are also welcome )

Introduction: Currently there are quite a lot of papers relevant with the machine learning (ML) algorithms or soft computing (SC) techniques published. Each claims that the proposed methods work better than other approaches. However, this conclusion is arrived based on the different dataset adopted. Consequently, the benchmark dataset, especially dataset of big data, used for model calibration and verification is essential. The main aims of this TF are: (1) to compile or collect benchmark dataset for ML or SC model calibration, and (2) to facilitate the future TC304-TC309 student contest, (3) test new methods developed for geotechnical data analytics.

Requirement on dataset: The dataset should satisfy one or more of the following requirements: (1) monitoring data over a few years, (2) extensive site investigations, (3) multi-variate big data, (4) use standard forms to store and present the data, like TC304dB.

      2) Big data acquisition (Task leader: Zili Li)

Members: Zili Li, Dimitrios Zekkos, Linqing Luo, Mehdi Alhaddad (to be updated, not yet TC309 member, other contributors are welcome)

Introduction: Machine learning, as one of the most active subsets of artificial intelligence, has been widely employed in many fields to automatically perform a specific task relying on patterns and inference without using explicit human instructions. However, the application of machine learning in geoengineering still significantly lags behind e-commerce, social network and many other fields. One major challenge in geoengineering is the lack of big data to train, test and validate machine learning models, as the acquisition of big geodata usually relies on manual site investigation, expensive lab testing, time-consuming field monitoring and etc.  

Aims and Goals: Recent advance in Information and Communication Technologies (ICTs) have developed a series of innovative field monitoring technologies, including distributed fibre optic sensing, wireless sensor network, autonomous robotic inspection, smartphone sensing and etc., which enable to acquire large amount of geodata at lower cost of labour and time than previously available. Nevertheless, the Technology Readiness Level (TRL) of many emerging geotechnical monitoring tools still largely remains at relatively early levels far before systemically wide application in a large scale. This task force of Big data acquisition aims to develop and improve novel monitoring tools for big geodata acquisition together with the associated geodata processing methods. The TF will provide an geoengineering platform for international researchers and professionals from different disciplines, sectors and countries, working collaboratively to break down barriers on the path of big geodata acquisition. 

This TF welcomes contributions to big geodata acquisitions in the following areas and beyond:

  • Development of innovative geotechnical monitoring technologies
  • Application of innovative field monitoring tools / methods to subsurface engineering
  • Gathering and processing of big data and metadata in geoengineering
  • Big geodata mining, generation of big geodata by computational methods and etc.

      3) Site investigation and geo-materials behavior (Task leader: Mohammad Rezania)

Members: Mohammad Rezania, Guotao Ma (to be updated, not yet TC309 member, other contributors are welcome)

Introduction: Geotechnical engineering is inherently a challenging discipline as soils are natural deposits, often with variation in their characteristics even within small area and short distances. In practice where relevant/sufficient data are not available, then very conservative design assumptions are used. During recent years advanced field investigation and accurate modelling of natural geomaterials have gained pace due to increasing demand in the industry for models that support less conservative designs. However, their utilization requires sufficient field monitoring or laboratory test data for model validation and calibration. There is enormous amount of data in the literature that are disjointed, fragmented and/or incoherent. To this end, the primary objectives of this TF are to: (1) collect, compile and classify the existing geotechnical monitoring and testing datasets from the literature and/or industrial records, and (2) provide a comprehensive and unified online database of geotechnical experimental resources for relevant ML model development/calibration to facilitate future practical and research applications.

      4) Design (Task leader: Bruno Stuyts)

Members: Bruno Stuyts

Introduction: Increasing amounts of digital geotechnical data are enabling researchers and practitioners to make use of data-driven methods for the design of foundations, slopes and underground structures. In order to develop machine learning pipelines for geotechnical design, a number of requirements are imposed on both the data (quality, structure, geospatial coverage, ...), the tools used for data processing and the foundation design algorithms.

Aims and scopes: In this task force, the necessary steps for enabling digital/automated geotechnical design will be studied and best practices will be suggested to allow end users to build data-driven workflows which rely on growing datasets. Special attention will be devoted to interpretable ML and feature engineering, to allow these workflows to capture engineering knowledge and underlying physical principles.


      5) Construction, Maintenance (Task leader: Dongming Zhang)

Members: Lisa Jinhui Li, Xu Li, Bin Liu, Kok-Kwang Phoon, Mingliang Zhou, Hongwei Huang, Huiming Wu and Jingya Yan

Introduction: The data from construction and maintenance of geotechnical engineering have the characteristics of continuum, heterogeneity and time-dependent. The analysis of those data could benefit the quality and safety of the construction and maintenance. Nowadays, as the sensors becomes smarter than ever before, the large amount of data from smart sensors are forcing the engineers to re-think the way to fully use such kind of new “oil” as huge information within these data could be dig out rationally and efficiently. As the machine learning develops in a rocket speed in other disciplines, how it will re-shape the construction and maintenance of the geotechnical engineering? This is the main job of this task force.

Aims and scopes:

1. Construction: The construction of geotechnical engineering is quite versatile both in terms of methodology and tools.  Quite often the mechanical based methodology such as TBM tunneling, shield machine tunneling, or even automatic driven of NATM tunneling could produce large amount of data. How to re-structure these data in a uniform pattern, to deeply analyze these data by machine learning algorithms, to instruct the construction parameters automatically in an unmanned manner, and finally to optimize the cost and enhance the safety of the geotechnical construction, should be the aim and the scope of this TF.

2. Maintenance: The monitoring and inspection for critical underground infrastructures such as metro systems, pipeline systems are one of important issues in maintenance of geo-structures and will produce large amount of time-dependent data of structural performance as well. How to automatically capture the disruption of monitored geo-infrastructures, to rationally determine the timing of the repair and maintenance work, and to enhance the resilience of the geo-infrastructures in a great service state, should be the aim and scope of this TF.

Things to kick-off recently:

-- Brainstorming of use of ML for construction and maintenance.

-- Setting up the criteria and formatting of the data collected from construction and maintenance

-- Choosing the most appropriate ML algorithms for specific data structures

-- Decision making for effective construction and maintenance

      6) Interact with industry, standards and guidelines (Task leader: Byron Quan Luna)

Introduction: Uses of artificial intelligence (AI), machine learning (ML) and other data­driven techniques have become increasingly widespread in recent years. Many now seek to capitalize on the potential such techniques offer to do things better, do things faster, and/or do things that were previously impossible. A data­driven model is a computational unit / program / function which makes predictions, and whose configuration is determined by a training operation on data. Data­driven application contains one or more data­driven models and uses the predictions of the model for some specific purpose.

Data­driven techniques are being used in a variety of applications including:

— early detection of failures (before they happen) and maintenance

— semi­ and fully automated technical verification

— prediction of unwanted events (geo-risks or other hazards incidents)

— automatic classification of maintenance logs and inspection findings

— detection of features (i.e. cracks)

The amount of responsibility humans are willing to hand over to any data­driven application depends on the criticality of the task it will perform, and the level of trust they have in the application. Most data­driven applications in use today have low criticality: their use is restricted either to low consequence scenarios, or to scenarios in which the application provides decision support for a human end user. But there is growing interest from vendors, consumers, industry and regulatory bodies in widening the scope in which data­driven applications can be used, to perform tasks with higher criticality and/or to move from decision support to decision taking.

However, difficulties remain in establishing trust that a data­driven application will operate as required, safely and reliably. The complexity of the data and the training algorithms, coupled with the lack of any standard approach to establishing trust in such applications, lead many to take a conservative approach and simply refuse to adopt such technologies until the field has matured.Even though it is possible to enable trust in data­driven applications through a systematic and data science­oriented consideration of risk.

To date no widely­recognized standard exists for assessment / assurance of data­driven geotechnical applications. In this context, the TF in TC309 will aim to fill that gap by combining the domain experience and inspection capabilities with digital analytics expertise, and also collaborating with end-users; to build a robust approach/framework to support industry-wide confidence in AI and ML.

  • Contact me

Chair:            Dr. Zhongqiang Liu, Norway, [email protected]

Vice-Chair:    Dr. Mohammad Rezania, UK, [email protected]

Secretary:      Dr. Dongming Zhang, China, [email protected]

  • Other Social Media Link

GeoWorld Link:

Linkedin Link:

ISSMGE TC309 Technical Forum of Young Scholars on Data-driven Modelling of Soil Behaviours with Geotechnical Applications

The Hong Kong Polytechnic University, in cooperation with ISSMGE TC309, organises a half day Technical Forum of young scholars on Data-driven Modelling of Soil Behaviours with Geotechnical Applications on 25 November 2022. The event will be conducted online.

Machine learning a

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Fifth TC309/TC304 Student Contest (groundwater time-series forecasting) in MLRA2021

MLRA2021 groundwater time-series forecasting

Machine learning prediction event for the international conference in "Machine learning & Risk assessment in geoengineering"

The machine learning competition is organized as an event at the MLRA2021 (Machine Learnin

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Reference List for Machine Learning and its Applications in Geotechnical Engineering - PART IV: PERFORMANCE COMPARISON OF MACHINE LEARNING ALGORITHMS USING THE SAME DATASET

1 Performance comparison of ML algorithms
1.1 Landslide

[1] Yao, X., Tham, L. G., and Dai, F. C. (2008). Landslide susceptibility mapping based on support vector machine: a case study on natural slopes of Hong Kong, China. Geomorphology, 101(4), 572-582. (One-class support

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Reference List for Machine Learning and its Applications in Geotechnical Engineering - PART III: APPLICATIONS IN GEOTECHNICAL ENGINEERING

1    Artificial neural networks
[1]    Shahin, M. A., Jaksa, M. B., and Maier, H. R. (2001). Artificial Neural Network Applications in Geotechnical Engineering. Australian Geomechanics, 36(1), 4962.
[2]    Shahin, M. A., Jaksa, M. B.

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Reference List for Machine Learning and its Applications in Geotechnical Engineering - PART II: MACHINE LEARNING ALGORITHMS

1  Supervised learning
1.1 Decision tree learning

[1] Breiman, Leo, Friedman, J. H., Olshen, R. A. and Stone, C. J. (1984). Classification and regression trees. Monterey, CA: Wadsworth and Brooks/Cole Advanced Books and Software. (Google citation: 37373)
[2] Dattatreya, G

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Reference List for Machine Learning and its Applications in Geotechnical Engineering - PART I: NEED TO KNOW KNOWLEDGE

1 Probability Theory
[1] Jeffreys, H. (1983). Theory of Probability. Oxford University Press, Oxford, New York.
[2] Sivia, D.S., and Skilling, J. (2006). Data Analysis: A Bayesian Tutorial. Oxford University Press, New York.
[3] Ang, A.H.-S., and Tang, W.H. (2007). Probability

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TC309 Student Contest - Kaggle-contest

MLRA2021 groundwater time-series forecasting

Machine learning prediction event for the international conference in "Machine learning & Risk assessment in geoengineering"


Machine learning as a technique is increasingly used as a "tool"

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TC304/TC309 student contests

 Aug 7-9 2020, Chongqing University, China

Participation form (download)

Contest Question (download); sounding data (download)

Feb 15 2020: Submission of participation form

June 30 2020: Submission of full length paper

Aug 7-9 15 2020: Student Contest


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TC304/TC309 student contests

Oct 4-7 2020, Tokyo, Japan

Organizer: Andy YF Leung, Zijun Cao, Lei Wang, Takayuki Shuku

Award Committee: Jianye Ching, Zhongqiang Liu, Shinichi Nishimura, Andy YF Leung

Contest question: (download) (Data: download)

Program of TC304 Student Contest: (download)


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TC304/TC309 student contests

Sep 22 2019, Hannover, Germany

Organizer: Giovanna Vessia, Wojciech Pula

Award Committee: Jianye Ching, K.K. Phoon, Wojciech Pula, Giovanna Vessia

Contest question: (download)

Program of TC304 Student Contest: (download)

TC304/TC309 Student Contest 1st Place Award

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TC304/TC309 student contests

Aug 18 2018, Harbin Institute of Technology, China

Organizer: Dagang Lv, Hongwei Huang, Jie Zhang

Award Committee: KK Phoon (Chair), Jianye Ching, Limin Zhang

Contest question: (download)

Program of TC304 Student Contest: (download)

TC304 Student Contest Award pape

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Disseminate knowledge and practice within the TC’s subject area to the membership of the ISSMGE:

TC309 aims to provide a forum for all interested members of ISSMGE to explore the use of machine learning (ML) techniques to solve problems relevant to soil mechanics and geotechnical engineering. To disseminate and develop knowledge and practice within the area of ML in geotechnical engineering, TC309 will deal with the following important technical issues:

  1. The development of accurate, robust and efficient predictive tools based on ML methods, such as Support Vector Machine (SVM), Deep Learning (DL), Reinforcement Learning (RL) and Case-based Reasoning (CBR).
  2. The development of webinars for global training use;
  3. Producing a widely distributed newsletter for general dissemination and communication on ML;
  4. The organization of symposia and workshops with the aim to promote cooperation and exchange of information concerning research and developments in using ML in geotechnical practice;
  5. Advancing the collaboration between ML techniques and complicated geotechnical problems by showing the advantages of popular and more advanced ML methods, and by demonstrating the efficiency of these techniques applied to geotechnical engineering via organizing prediction events.

To establish guidelines and technical recommendations within the TC’s subject area:

TC309 will focus on the following actions:

  1. To set up a reference list for ML research work and books recommended by members of TC309;
  2. To prepare a State-of-the-Art paper on the use of ML in geotechnical engineering;
  3. To cooperate with other TCs to compile useful databases for determining relationships within the data, and computing parameters for analytical models that apply those relationships to the use case at hand;
  4. To establish or maintain contact with TCs having close interests such as TC304, TC103 and TC205.

Assist with technical programs of international and regional conferences organized by the ISSMGE:

  1. Organize a session on ML in the International Conference on Soil Mechanics and Geotechnical Engineering (ICSMGE) in Sydney, Australia, in 2021.
  2. Organize a joint TC309/TC304 ML workshop in 6th International Symposium on Reliability Engineering and Risk Management (ISRERM), 31 May-1 June 2018, Singapore and in 7th International Symposium on Geotechnical Safety and Risk (ISGSR), 12-13 December 2019, Taipei, Taiwan.
  3. Encourage the active participation (papers, lectures, workshops) of TC309 members at regional conferences, For example, 9th European Conference on Numerical Methods in Geotechnical Engineering 25-27 June 2018, Porto, Portugal, 17th European Conference on Soil Mechanics and Geotechnical Engineering 1-6 September 2019, Reykjavik, Iceland, and 16th Asian Regional Conference on Soil Mechanics and Geotechnical Engineering (16ARC) 14-18 October 2019, Taipei, Taiwan.  

Develop various schemes to draw the active participation of ISSMGE members. Typical examples of these include online survey of typical software packages used for their research/work, challenging problems/difficulties they have encountered or meet in their work when using ML techniques.

Interact with industry and overlapping organizations working in areas related to the TC’s specialist area:

TC309 will make efforts to reduce the gap existing between the State-of-the-Art and the State-of-the-Practice in the field of using ML techniques. TC309 will invite experienced practicing engineers to join the technical committee. They will be encouraged to organize sessions with practice-oriented topics and discussion workshops involving also academics.

TC309 will also actively seek collaborative opportunities with other ISSMGE TC's as well as other professional societies to promote the advance of applying ML techniques in geotechnical engineering. This also will involve close liaison with other ISSMGE Technical Committees.

# Type Full Name Country
1 Chair Zhongqiang Liu Norway
2 Vice Chair Mohammad Rezania United Kingdom
3 Secretary Dongming Zhang China
4 Nominated by TC Chair Kok Kwang Phoon Singapore
5 Nominated by TC Chair Zenon Medina-Cetina United States
6 Nominated by TC Chair Byron Quan Luna Norway
7 Nominated Member Yu Wang Hong Kong Special Administrative Region
8 Nominated Member Y.H. Wang Hong Kong Special Administrative Region
9 Corresponding Member Andy Leung Hong Kong Special Administrative Region
10 Nominated Member Ali Karrech Australia
11 Corresponding Member Edward Chu Hong Kong Special Administrative Region
12 Corresponding Member Te Xiao Hong Kong Special Administrative Region
13 Corresponding Member Stephen Suryasentana United Kingdom
14 Nominated Member Jinhyun Choo South Korea
15 Corresponding Member Jianwei Jia China
16 Nominated Member Faraz Sadeghi Tehrani Netherlands
17 Nominated Member Dimitrios Zekkos United States
18 Nominated Member Xiong (Bill) Yu United States
19 Corresponding Member Jinsong Huang Australia
20 Corresponding Member Xu Li China
21 Nominated Member Bas van Dijk Netherlands
22 Nominated Member Erdi Myftaraga Albania
23 Nominated Member Olsi Koreta Albania
24 Nominated Member Bruno Stuyts Belgium
25 Nominated Member Gustav Grimstad Norway
26 Nominated Member Sigurdur Már Valsson Norway
27 Corresponding Member Vikas Thakur Norway
28 Nominated Member Zili Li Ireland
29 Nominated Member Xiaohui Chen United Kingdom
30 Nominated Member Gilles Chapron France
31 Corresponding Member Philippe REIFFSTECK France
32 Corresponding Member Jean-François MOSSER France
33 Corresponding Member Adel ABDALLAH France
34 Corresponding Member Michel RISPAL France
35 Nominated Member Simon BUNIESKI France
36 Corresponding Member Lucy Wu United States
37 Nominated Member Carlos Acosta Quintas Singapore
38 Corresponding Member Sogol Fallah Ireland
39 Nominated Member Michele Calvello Italy
40 Nominated Member Lin Zhang Ireland
41 Nominated Member Binh Pham Thai Vietnam
42 Nominated Member MÁRCIO SANTOS Brazil
43 Nominated Member Manuel Parente Portugal
44 Nominated Member Chung Siung Choo Malaysia
45 Nominated Member Marco Uzielli Italy
46 Nominated Member Michael Mygind Denmark
47 Nominated Member Kirill Alexander Schmoor Germany
48 Corresponding Member Jonathan White United Kingdom
49 Nominated Member Huong Thi Thanh NGO Vietnam
50 Corresponding Member Haijia Wen China
51 Nominated Member Kuo-Lung Wang Chinese Taipei
52 Nominated Member An- Jui Li Chinese Taipei
53 Nominated Member Vic Kumaran New Zealand
54 Nominated Member Thomas Vergote Belgium
55 Nominated Member Peter Quinn Canada
56 Corresponding Member Mingliang Zhou China
57 Nominated Member Zijun Cao China
58 Corresponding Member Guotao Ma China
59 Nominated Member Charles MacRobert South Africa
60 Nominated Member PIJUSH SAMUI India
61 Corresponding Member Rafael Jiménez Spain
62 Nominated Member Franz Tschuchnigg Austria
63 Nominated Member María Megía Spain
64 Nominated Member Germán López Pineda Spain
65 Nominated Member Andre Luís Brasil Cavalcante Brazil
66 Nominated Member Christopher Rothschedl Austria
67 Nominated Member jinhui Li China
68 Corresponding Member Santiago Peña Spain
69 Corresponding Member Wengang Zhang China

TC-304 and TC-309s' contribution to the ISSMGE Time Capsule Project focuses on the review of Machine Learning and its Applications in Geotechnical Engineering. The review list comprises four parts four parts, as detailed below.


This part constains four sections: Probability Theory, Information Theory, Decision Theory, Introductory Materials on Machine Learning. The review list of these sections can be found at:


This part constains five sections: Supervised learning,Semi-supervised learning, Reinforcement learning, Unsupervised learning, Bayesian machine learning. The review list of these sections can be found at:


This part constains six sections: Artificial neural networks, Support vector machine, Clustering, Feature learning (Dimensionality reduction), Outlier detection, Bayesian machine learning. The review list of these sections can be found at:


This part constains one sections: Performance comparison of ML algorithms. The review list of these sections can be found at:


Contact Technical Committee : Machine Learning and Big Data

This message will be sent to TC309 Machine Learning officers