Machine Learning Supported System for Performance Assessment of Steel Structures Under Extreme Operating Conditions and Management of Risk

July 1, 2022
In Progress
Share this project


Building sustainable cities require action and technologies that minimize the large lifecycle costs of infrastructure assets. These costs relate to the way infrastructure responds to operating conditions and are further exacerbated by laborious visual inspections aimed at ensuring structural safety. This is relevant for steel structures, which are commonly used for community-critical infrastructure. At the center of this problem lies the quantification of reserve load-bearing capacity of structures.

With the overarching goal of minimizing downtime and managing risks of the built infrastructure, the focus of this project is on the development of enabling data-driven methods, which can assist in the reliable rapid diagnosis of damage. One of the novel concepts at the core of the envisioned methodological framework is the direct mapping of deformed structural shapes to the associated reserve capacity by relying on visual deformations. To this end, validated nonlinear constitutive material models and computational workflows developed within the PI’s group will be leveraged to generate comprehensive training data for the development of machine learning (ML) tools. A further novelty lies in the use of a common data interface based on the 3D digital objects of deformed shapes, which allows for consistent treatment of physical and numerical data. This is key for the envisioned experimental validation and streamlined application of the methodology. Moreover, we propose a novel approach, based on engineering insight, for extraction of domain-specific features from the deformed steel members, which can be directly used as inputs to available ML tools.

The theoretical developments and associated computational tools of this project can be generalized and expanded for structures made of other construction materials by developing additional modules. These modules can be encapsulated in a software ecosystem to comprehensively support automated inspection of infrastructure.



SDSC Team:
Isinsu Katircioglu
Guillaume Obozinski

PI | Partners:

EPFL, Resilient Steel Structures Laboratory:

  • Prof. Dimitrios Lignos
  • Dr. Nenad Bijelic
  • Tianyu Gu

More info



This project focuses on steel structures commonly used in building and bridge construction, renowned for their ductility. Exposure to extreme conditions such as snowstorms, heavy traffic, or earthquakes results in inelastic deformation in these structures, diminishing their residual life. Traditional condition assessments heavily depend on periodic (human) visual inspections, a process prone to unreliability and risks for inspectors. The collaborative goal is to develop a novel, data-driven methodology for automated and reliable residual life (reserve capacity) prognosis of steel members in infrastructure systems based on visual data.

Proposed Approach / Solution

In this project, we explore various machine learning algorithms that rely solely on observed 3D deformations of steel members to assess their damage state, given that loading history in realistic settings is typically unavailable. To achieve this, we investigate different representations of a 3D digital object and feed the processed 3D point cloud of the deformed shape to the underlying machine learning model. The ground truth data is generated through high-fidelity numerical simulations of steel members, with a major focus on generalizing from the experimental laboratory environment to real-world settings. Our proposed approach involves slicing the digital 3D objects into structured grids, which can be treated in 2D/3D and used in combination with pointwise MLP methods, convolution-based methods, or graph neural networks to learn the stress and strain fields in the material, as well as the reserve capacity.

Figure 1: Overview of the ML-SPOCK framework: Our model estimates the forward and backward reserve capacity of steel structures using a 3D point cloud representing the deformed geometry of columns. The point cloud is obtained by laser scanning the damaged columns.
Figure 2: Cumulative drift (total horizontal displacement of the steel column) - normalized moment and reserve capacity relationships during the course of the deformation. The descending stage shows reduced normalized moment and reserve capacity after the column reaches maximum capacity, indicating deterioration. The update stage occurs when the displacement load reverses, adjusting the reserve capacity based on the preceding stage.


This project's scientific breakthrough is quantifying the remaining lifespan of steel structures based on visual data alone. This can greatly simplify costly inspections, support expert training, and reduce reliance on time-consuming manual inspections that pose risks to humans. Moreover, the framework developed in this project can be used in drones or robots for automated inspection in hazardous environments. Finally, early damage identification enables proper remediation, making the infrastructure more sustainable and decreases its carbon footprint.



Additional resources


  1. Spencer, B.F. et al (2019). Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring. Engineering 5(2):199-222.  
  2. Elkady, A. and Lignos, D.G. (2015). Analytical investigation of the cyclic behavior and plastic hinge formation in deep wide-flange steel beam-columns. Bulletin of Earthquake Engineering 13(4):1097–1118.  
  3. Elkady, A. and Lignos, D.G. (2018). Full-Scale Testing of Deep Wide-Flange Steel Columns under Multiaxis Cyclic Loading: Loading Sequence, Boundary Effects, and Lateral Stability Bracing Force Demands. Journal of Structural Engineering, 144(2): 04017189.  
  4. Suzuki, Y. and Lignos, D.G. (2021). Experimental Evaluation of Steel Columns under Seismic Hazard-Consistent Collapse Loading Protocols. Journal of Structural Engineering, 147(4):04021020.


Related Pages

More projects


In Progress
Interpretable and Robust Machine Learning for Mobility Analysis
No items found.


In Progress
Feature Learning for Bayesian Inference
No items found.


In Progress
Personalized epidural electrical stimulation of the lumbar spinal cord for clinically applicable therapy to restore mobility after paralyzing spinal cord injury
No items found.


In Progress
Directed Imitation During Vocal Learning
No items found.


Latest news

Smartair | An active learning algorithm for real-time acquisition and regression of flow field data
May 1, 2024

Smartair | An active learning algorithm for real-time acquisition and regression of flow field data

Smartair | An active learning algorithm for real-time acquisition and regression of flow field data

We’ve developed a smart solution for wind tunnel testing that learns as it works, providing accurate results faster. It provides an accurate mean flow field and turbulence field reconstruction while shortening the sampling time.
The Promise of AI in Pharmaceutical Manufacturing
April 22, 2024

The Promise of AI in Pharmaceutical Manufacturing

The Promise of AI in Pharmaceutical Manufacturing

Innovation in pharmaceutical manufacturing raises key questions: How will AI change our operations? What does this mean for the skills of our workforce? How will it reshape our collaborative efforts? And crucially, how can we fully leverage these changes?
Efficient and scalable graph generation through iterative local expansion
March 20, 2024

Efficient and scalable graph generation through iterative local expansion

Efficient and scalable graph generation through iterative local expansion

Have you ever considered the complexity of generating large-scale, intricate graphs akin to those that represent the vast relational structures of our world? Our research introduces a pioneering approach to graph generation that tackles the scalability and complexity of creating such expansive, real-world graphs.

Contact us

Let’s talk Data Science

Do you need our services or expertise?
Contact us for your next Data Science project!