# Postdisaster image-based damage detection and repair cost estimation of reinforced concrete buildings using dual convolutional neural networks

arXiv: https://arxiv.org/abs/2111.09862 · HTML: https://mlsanity.com/paper/2111.09862/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 61 references that are in the map.
- First posted on arXiv 16 Nov 2021.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [What makes for effective detection proposals?](https://mlsanity.com/paper/1502.05082/) (2015)
- [How good are detection proposals, really?](https://mlsanity.com/paper/1406.6962/) (2014)

## Built on by (most cited first, 6 of 6)

- [Large-scale Building Damage Assessment using a Novel Hierarchical Transformer Architecture on Satellite Images](https://mlsanity.com/paper/2208.02205/) (2022)
- [Autonomous damage assessment of structural columns using low-cost micro aerial vehicles and multi-view computer vision](https://mlsanity.com/paper/2308.16278/) (2023)
- [A reinforcement learning based construction material supply strategy using robotic crane and computer vision for building reconstruction after an earthquake](https://mlsanity.com/paper/2308.16280/) (2023)
- [3D vision-based structural masonry damage detection](https://mlsanity.com/paper/2308.16380/) (2023)
- [Deep Learning in Earthquake Engineering: A Comprehensive Review](https://mlsanity.com/paper/2405.09021/) (2024)
- [Post-disaster building indoor damage and survivor detection using autonomous path planning and deep learning with unmanned aerial vehicles](https://mlsanity.com/paper/2503.10027/) (2025)

## Abstract (arXiv)

Reinforced concrete buildings are commonly used around the world. With recent earthquakes worldwide, rapid structural damage inspection and repair cost evaluation are crucial for building owners and policy makers to make informed risk management decisions. To improve the efficiency of such inspection, advanced computer vision techniques based on convolution neural networks have been adopted in recent research to rapidly quantify the damage state of structures. In this paper, an advanced object detection neural network, named YOLO-v2, is implemented which achieves 98.2% and 84.5% average precision in training and testing, respectively. The proposed YOLO-v2 is used in combination with the classification neural network, which improves the identification accuracy for critical damage state of reinforced concrete structures by 7.5%. The improved classification procedures allow engineers to rapidly and more accurately quantify the damage states of the structure, and also localize the critical damage features. The identified damage state can then be integrated with the state-of-the-art performance evaluation framework to quantify the financial losses of critical reinforced concrete buildings. The results can be used by the building owners and decision makers to make informed risk management decisions immediately after the strong earthquake shaking. Hence, resources can be allocated rapidly to improve the resiliency of the community.