Automated Analysis of Mobile LiDAR Data for Component-Level Damage Assessment of Building Structures during Large Coastal Storm Events

Coastal communities are vulnerable to floods from storm events which are further exacerbated by storm surges. Additionally, coastal towns provide specific challenges during flood events as many coastal communities are peninsular and vulnerable to inundation of road access points. Publicly available lidar data has been used to model areas of inundation and resulting flood impacts on road networks. However, these models may overestimate areas that are inaccessible as they rely on publicly available Digital Terrain Models. Through incorporation of Digital Surface Models to estimate bridge height, a more accurate model of flood impacts on rural coastal residents can be estimated.

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EXTRACTING DIMENSIONS AND LOCATIONS OF DOORS, WINDOWS, AND DOOR THRESHOLDS OUT OF MOBILE LIDAR DATA USING OBJECT DETECTION TO ESTIMATE THE IMPACT OF FLOODS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-w8-429-2019 ◽

2019 ◽

Vol XLII-3/W8 ◽

pp. 429-436 ◽

Cited By ~ 1

Author(s):

S. Van Ackere ◽

J. Verbeurgt ◽

L. De Sloover ◽

A. De Wulf ◽

N. Van de Weghe ◽

...

Keyword(s):

Object Detection ◽

Economic Impact ◽

Flood Disaster ◽

Lidar Data ◽

Building Structures ◽

Flood Events ◽

Urban Flood ◽

Impervious Area ◽

Socio Economic Impact ◽

The Impact

<p><strong>Abstract.</strong> Increasing urbanisation, changes in land use (e.g., more impervious area) and climate change have all led to an increasing frequency and severity of flood events and increased socio-economic impact. In order to deploy an urban flood disaster and risk management system, it is necessary to know what the consequences of a specific urban flood event are to adapt to a potential event and prepare for its impact. Therefore, an accurate socio-economic impact assessment must be conducted. Unfortunately, until now, there has been a lack of data regarding the design and construction of flood-prone building structures (e.g., locations and dimensions of doors and door thresholds and presence and dimensions of basement ventilation holes) to consider when calculating the flood impact on buildings. We propose a pipeline to detect the dimension and location of doors and windows based on mobile LiDAR data and 360° images. This paper reports on the current state of research in the domain of object detection and instance segmentation of images to detect doors and windows in mobile LiDAR data. The use and improvement of this algorithm can greatly enhance the accuracy of socio-economic impact of urban flood events and, therefore, can be of great importance for flood disaster management.</p>

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Damage Localization in a Building Structure during Seismic Excitation

Shock and Vibration ◽

10.1155/2020/8859527 ◽

2020 ◽

Vol 2020 ◽

pp. 1-17

Author(s):

Jesús Morales-Valdez ◽

Luis Alvarez-Icaza ◽

José A. Escobar

Keyword(s):

Structural Health Monitoring ◽

Real Time ◽

Health Monitoring ◽

Damage Assessment ◽

Early Stage ◽

Detection Methods ◽

Identification System ◽

Building Structures ◽

Simple Method ◽

Structural Health

Aging of buildings during their service life has attracted the attention of researchers on structural health monitoring (SHM). This paper is related with detecting damage in building structures at the earliest possible stage during seismic activity to facilitate decision-making on evacuation before physical inspection is possible. For this, a simple method for damage assessment is introduced to identify the damage story of multistory buildings from acceleration measurements under a wave propagation approach. In this work, damage is assumed as reduction in shear wave velocities and changes in damping ratios that are directly related with stiffness loss. Most damage detection methods are off-line processes; this is not the case with this method. First, a real-time identification system is introduced to estimate the current parameters to be compared with nominal values to detect any changes in the characteristics that may indicate damage in the building. In addition, this identification system is robust to constant disturbances and measurement noise. The time needed to complete parameter identification is shorter compared to the typically wave method, and the damage assessment can keep up with the data flow in real time. Finally, using a robust threshold, postprocess of the compared signal is performed to find the location of the possible damage. The performance of the proposed method is demonstrated through experiments on a reduced-scale five-story building, showing the ability of the proposed method to improve early stage structural health monitoring.

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A DEEP LEARNING FRAMEWORK FOR ROADS NETWORK DAMAGE ASSESSMENT USING POST-EARTHQUAKE LIDAR DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w18-955-2019 ◽

2019 ◽

Vol XLII-4/W18 ◽

pp. 955-961 ◽

Cited By ~ 2

Author(s):

S. T. Seydi ◽

H. Rastiveis

Keyword(s):

Neural Network ◽

Deep Learning ◽

Damage Assessment ◽

Point Cloud ◽

High Performance ◽

Damage Index ◽

Lidar Data ◽

The Road ◽

The Status ◽

Road Segments

Abstract. Roads network are the most important parts of urban infrastructures, which can cause difficulty to the city whenever they undergo a problem. This paper aims to provide and implement a deep learning-based method to determine the status of the streets network after an earthquake using LiDAR point cloud. The proposed framework composes of three main phases: (1) Deep features of LiDAR data are extracted using a Convolutional Neural Network (CNN). (2) The extracted features are used in a multilayer perceptron (MLP) neural network in which debris areas inside the road network are detected. (3) The amount of debris in each road is applied to damage index for classifying the road segments into blocked or un-blocked. To evaluate the efficiency of the proposed framework, LiDAR point cloud of the Port-au-Prince, Haiti after the 2010 Haiti earthquake was used. The overall accuracy of more than 97% proved the high performance of this framework for debris detection. Moreover, analyzing damage assessment of 37 road segments based on the detected debris and comparing to a visually generated damaged map, 31 of the road segments were correctly labelled as either blocked or un-blocked.

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