TAPEDA

Towards Automated Post-Earthquake Damage Assessment

Started
January 9, 2019
Status
Completed
Share this project

Abstract

After earthquakes, engineers assess the damaged state of buildings through visual inspection and judge whether they are safe to enter or not. Damage evaluation is therefore at present a very labor-intensive task, which requires a visual inspection of all potentially affected buildings. After the magnitude M=6.3 earthquake in L’Aquila in 2009, for example, 8’000 experts assessed 72’000 buildings over a period of five months spending in average 0.5 working days per building. In addition to this, the evaluation of the damage is very subjective in nature and therefore error-prone. The TAPEDA project addresses stone masonry buildings, which are among the most vulnerable buildings during earthquakes. First, we need to obtain a 3D abstraction of the building from its point cloud obtained through Structure from Motion (SFM) techniques, which helps setting up a finite element model. Second, we need detect damage to a building from images using computer vision techniques and place them on the 3D model. The capacity loss assessment may now be performed automatically using computational models. Even with some manual intervention, such a computer-assissted approach is expected to reduce the time of capacity loss from days to hours, using far fewer engineers, and be more objective in nature.

People

Collaborators

SDSC Team:
Radhakrishna Achanta

PI | Partners:

EPFL, Earthquake Engineering and Structural Dynamics Laboratory:

  • Prof. Katrin Beyer

More info

description

  • The TAPEDA project is concerned with the automatic assessment of safety of buildings in the aftermath of an earthquake, which, done manually, is arduous, time-consuming, and expensive.
  • To do this automatically, we need to take pictures of a building with a drone and obtain a wireframe model of the building. From this an equivalent Frame Model (EFM) can be obtained, which permits the computation of capacity loss of the building.
  • There are two possibilities in sight to obtain a wireframe. One requires us to create the point cloud and then do meshing. The other approach may be to bypass the creation of the point cloud altogether by creating the wireframes directly from 2D pictures.

Approach

To assess safety of damaged buildings automatically, we need to take pictures of a building with a drone and obtain a wireframe abstraction of the building. We have a pipeline now which nearly gets us to the end goal but requires minor manual interventions (see figure below). This pipeline requires us to create a point cloud using Structure-from-Motion (SfM) techniques, which is reduced to a planar abstraction by fitting 3D planes to the point cloud. Alongside, damages, in particular cracks, as well as opening, namely, windows and doors are detected using deep networks and then projected on to the 3D model of the building.

Gallery

Annexe

Additional resources

Bibliography

Publications

Related Pages

More projects

ML-L3DNDT

Completed
Robust and scalable Machine Learning algorithms for Laue 3-Dimensional Neutron Diffraction Tomography
Big Science Data

BioDetect

Completed
Deep Learning for Biodiversity Detection and Classification
Energy, Climate & Environment

IRMA

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

FLBI

In Progress
Feature Learning for Bayesian Inference
No items found.

News

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!