Damage Classification on Roads Using Machine Learning

2021 ◽  
Author(s):  
Fauzan Iraldi ◽  
Wikky Fawwaz Al Maki
Keyword(s):  
Machine Learning ◽  
Damage Classification

Rapid earthquake loss assessment based on machine learning and representative sampling

2021 ◽  
pp. 875529302110423
Author(s):  
Zoran Stojadinović ◽  
Miloš Kovačević ◽  
Dejan Marinković ◽  
Božidar Stojadinović
Keyword(s):  
Machine Learning ◽  
Classification Model ◽  
Local Network ◽  
Loss Assessment ◽  
Damage Classification ◽  
Sampling Algorithm ◽  
Representative Sampling ◽  
Random Forest Classification ◽  
Area Of Interest ◽  
Earthquake Loss

This article proposes a new framework for rapid earthquake loss assessment based on a machine learning damage classification model and a representative sampling algorithm. A random forest classification model predicts a damage probability distribution that, combined with an expert-defined repair cost matrix, enables the calculation of the expected repair costs for each building and, in aggregate, of direct losses in the earthquake-affected area. The proposed building representation does not include explicit information about the earthquake and the soil type. Instead, such information is implicitly contained in the spatial distribution of damage. To capture this distribution, a sampling algorithm, based on K-means clustering, is used to select a minimal number of buildings that represent the area of interest in terms of its seismic risk, independently of future earthquakes. To observe damage states in the representative set after an earthquake, the proposed framework utilizes a local network of trained damage assessors. The model is updated after each damage observation cycle, thus increasing the accuracy of the current loss assessment. The proposed framework is exemplified using the 2010 Kraljevo, Serbia earthquake dataset.

Building-damage detection method based on machine learning utilizing aerial photographs of the Kumamoto earthquake

2020 ◽  
Vol 36 (3) ◽  
pp. 1166-1187 ◽  
Author(s):  
Shohei Naito ◽  
Hiromitsu Tomozawa ◽  
Yuji Mori ◽  
Takeshi Nagata ◽  
Naokazu Monma ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Training Data ◽  
Aerial Photographs ◽  
Learning Models ◽  
Visual Interpretation ◽  
Damage Classification ◽  
Kumamoto Earthquake ◽  
Machine Learning Models

This article presents a method for detecting damaged buildings in the event of an earthquake using machine learning models and aerial photographs. We initially created training data for machine learning models using aerial photographs captured around the town of Mashiki immediately after the main shock of the 2016 Kumamoto earthquake. All buildings are classified into one of the four damage levels by visual interpretation. Subsequently, two damage discrimination models are developed: a bag-of-visual-words model and a model based on a convolutional neural network. Results are compared and validated in terms of accuracy, revealing that the latter model is preferable. Moreover, for the convolutional neural network model, the target areas are expanded and the recalls of damage classification at the four levels range approximately from 66% to 81%.

Damage Classification of Composites Using Machine Learning

2019 ◽  
Author(s):  
Shweta Dabetwar ◽  
Stephen Ekwaro-Osire ◽  
João Paulo Dias
Keyword(s):  
Machine Learning ◽  
Composite Materials ◽  
Random Forest ◽  
Condition Monitoring ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Damage Classification ◽  
Combining Data ◽  
Ultrasonic Measurements

Abstract Composite materials have tremendous and ever-increasing applications in complex engineering systems; thus, it is important to develop non-destructive and efficient condition monitoring methods to improve damage prediction, thereby avoiding catastrophic failures and reducing standby time. Nondestructive condition monitoring techniques when combined with machine learning applications can contribute towards the stated improvements. Thus, the research question taken into consideration for this paper is “Can machine learning techniques provide efficient damage classification of composite materials to improve condition monitoring using features extracted from acousto-ultrasonic measurements?” In order to answer this question, acoustic-ultrasonic signals in Carbon Fiber Reinforced Polymer (CFRP) composites for distinct damage levels were taken from NASA Ames prognostics data repository. Statistical condition indicators of the signals were used as features to train and test four traditional machine learning algorithms such as K-nearest neighbors, support vector machine, Decision Tree and Random Forest, and their performance was compared and discussed. Results showed higher accuracy for Random Forest with a strong dependency on the feature extraction/selection techniques employed. By combining data analysis from acoustic-ultrasonic measurements in composite materials with machine learning tools, this work contributes to the development of intelligent damage classification algorithms that can be applied to advanced online diagnostics and health management strategies of composite materials, operating under more complex working conditions.

scholarly journals A Machine Learning Framework for Assessing Seismic Hazard Safety of Reinforced Concrete Buildings

2020 ◽  
Vol 10 (20) ◽  
pp. 7153 ◽  
Author(s):  
Ehsan Harirchian ◽  
Vandana Kumari ◽  
Kirti Jadhav ◽  
Rohan Raj Das ◽  
Shahla Rasulzade ◽  
...  
Keyword(s):  
Machine Learning ◽  
Input Data ◽  
Safety Evaluation ◽  
Support Vector ◽  
Optimum Number ◽  
Damage Classification ◽  
Damage Prediction ◽  
Rapid Visual Screening ◽  
Damage Data ◽  
Screening Platform

Although averting a seismic disturbance and its physical, social, and economic disruption is practically impossible, using the advancements in computational science and numerical modeling shall equip humanity to predict its severity, understand the outcomes, and equip for post-disaster management. Many buildings exist amidst the developed metropolitan areas, which are senile and still in service. These buildings were also designed before establishing national seismic codes or without the introduction of construction regulations. In that case, risk reduction is significant for developing alternatives and designing suitable models to enhance the existing structure’s performance. Such models will be able to classify risks and casualties related to possible earthquakes through emergency preparation. Thus, it is crucial to recognize structures that are susceptible to earthquake vibrations and need to be prioritized for retrofitting. However, each building’s behavior under seismic actions cannot be studied through performing structural analysis, as it might be unrealistic because of the rigorous computations, long period, and substantial expenditure. Therefore, it calls for a simple, reliable, and accurate process known as Rapid Visual Screening (RVS), which serves as a primary screening platform, including an optimum number of seismic parameters and predetermined performance damage conditions for structures. In this study, the damage classification technique was studied, and the efficacy of the Machine Learning (ML) method in damage prediction via a Support Vector Machine (SVM) model was explored. The ML model is trained and tested separately on damage data from four different earthquakes, namely Ecuador, Haiti, Nepal, and South Korea. Each dataset consists of varying numbers of input data and eight performance modifiers. Based on the study and the results, the ML model using SVM classifies the given input data into the belonging classes and accomplishes the performance on hazard safety evaluation of buildings.

scholarly journals Application of Support Vector Machine Modeling for the Rapid Seismic Hazard Safety Evaluation of Existing Buildings

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3340 ◽  
Author(s):  
Ehsan Harirchian ◽  
Tom Lahmer ◽  
Vandana Kumari ◽  
Kirti Jadhav
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Risk Mitigation ◽  
Safety Evaluation ◽  
Supervised Machine Learning ◽  
Computational Time ◽  
Economic Losses ◽  
Support Vector ◽  
Optimum Number ◽  
Damage Classification

The economic losses from earthquakes tend to hit the national economy considerably; therefore, models that are capable of estimating the vulnerability and losses of future earthquakes are highly consequential for emergency planners with the purpose of risk mitigation. This demands a mass prioritization filtering of structures to identify vulnerable buildings for retrofitting purposes. The application of advanced structural analysis on each building to study the earthquake response is impractical due to complex calculations, long computational time, and exorbitant cost. This exhibits the need for a fast, reliable, and rapid method, commonly known as Rapid Visual Screening (RVS). The method serves as a preliminary screening platform, using an optimum number of seismic parameters of the structure and predefined output damage states. In this study, the efficacy of the Machine Learning (ML) application in damage prediction through a Support Vector Machine (SVM) model as the damage classification technique has been investigated. The developed model was trained and examined based on damage data from the 1999 Düzce Earthquake in Turkey, where the building’s data consists of 22 performance modifiers that have been implemented with supervised machine learning.

Non-linear Damage Classification based on Machine Learning and Damage Indices

2017 ◽  
Author(s):  
DIEGO TIBADUIZA ◽  
MIGUEL ANGEL TORRES ◽  
JAIME VITOLA ◽  
MARIBEL ANAYA ◽  
FRANCESCO POZO
Keyword(s):  
Machine Learning ◽  
Damage Classification ◽  
Damage Indices ◽  
Non Linear

scholarly journals 3D point cloud-based assessment of detailed building damage through a combination of machine learning, crowdsourcing and earthquake engineering

2021 ◽  
Author(s):  
Vivien Zahs ◽  
Benjamin Herfort ◽  
Julia Kohns ◽  
Tahira Ullah ◽  
Katharina Anders ◽  
...  
Keyword(s):  
Machine Learning ◽  
Damage Assessment ◽  
Point Cloud ◽  
Image Data ◽  
Building Design ◽  
3D Point Cloud ◽  
Visual Interpretation ◽  
Damage Classification ◽  
Damage Characteristics ◽  
Geographic Regions

<div> <p>Timely and reliable information on earthquake-induced building damage plays a critical role for the effective planning of rescue and remediation actions. Automatic damage assessment based on the analysis of 3D point cloud (e.g. from photogrammetry or LiDAR) or georeferenced image data can provide fast and objective information on the damage situation within few hours. So far, studies are often limited to the distinction of only two damage classes (e.g. damaged or not damaged) and to information provided by 2D image data. Beyond-binary assessment of multiple grades of damage is challenging, e.g. due to the variety of damage characteristics and the limited transferability of trained algorithms to unseen data and other geographic regions. The detailed damage assessment based on full 3D information is, however, required to enable efficient use and distribution of resources and for evaluation of structural stability of buildings. Further, the identification of slightly damaged buildings is essential to estimate the vulnerability for severe damage in potential aftershock events.</p> <p>In our work, we propose an interdisciplinary approach for timely and reliable assessment of multiple building-specific damage grades (0-5) from post- (and pre-) event UAV point clouds and images with high resolution (centimeter point spacing or pixel size). We combine expert knowledge of earthquake engineers with fully automatic damage classification and human visual interpretation from web-based crowdsourcing. While automatic approaches enable an objective and fast analysis of large 3D data, the ability of humans to visually interpret details in the data can be used as (1) validation of the automatic classification and (2) alternative method where the automatic approach showed high levels of uncertainty.</p> <p>We develop a damage catalogue that categorizes typical geometric and radiometric damage patterns for each damage grade. Therein, we consider influences of building material and region-specific building design on damage characteristics. Moreover, damage patterns include observations of previous earthquakes to ensure practical applicability. The catalogue serves as decision basis for the automatic classification of building-specific damage using machine learning, on the one hand. On the other hand, the catalogue is used to design quick and easy single damage mapping tasks that can be solved by volunteers within seconds (Micro-Mapping, Herfort et al. 2018). A further novelty of our approach consists in the combination of strengths of machine learning approaches for point cloud-based damage classification and visual interpretation by human contributors through Micro-Mapping tasks. The optimal combination of operation and weighted fusion of both methods is thereby dependent on event-specific conditions (e.g. data availability and quality, temporal constraints, spatial scale, extent of damage). </p> <p>By considering observations from previous earthquakes and influences of building design and structure on potential damage characteristics, our approach shall be applicable to events in different geographic regions. By the combination of automated and crowdsourcing methods, reliable and detailed damage information at the scale of large cities shall be provided within a few days. </p> </div><div> <p> </p> <div> <p>References</p> <p>Herfort, B., Höfle, B. & Klonner, C. (2018): 3D micro-mapping: Towards assessing the quality of crowdsourcing to support 3D point cloud analysis. ISPRS Journal of Photogrammetry and Remote Sensing. Vol. 137, pp. 73-83.</p> </div> </div>

scholarly journals A Damage Classification Approach for Structural Health Monitoring Using Machine Learning

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Diego Tibaduiza ◽  
Miguel Ángel Torres-Arredondo ◽  
Jaime Vitola ◽  
Maribel Anaya ◽  
Francesc Pozo
Keyword(s):  
Machine Learning ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Sandwich Structure ◽  
Damage Identification ◽  
Principal Component ◽  
Piezoelectric Sensor ◽  
Guided Wave ◽  
Damage Classification ◽  
Structural Health

Inspection strategies with guided wave-based approaches give to structural health monitoring (SHM) applications several advantages, among them, the possibility of the use of real data from the structure which enables continuous monitoring and online damage identification. These kinds of inspection strategies are based on the fact that these waves can propagate over relatively long distances and are able to interact sensitively with and uniquely with different types of defects. The principal goal for SHM is oriented to the development of efficient methodologies to process these data and provide results associated with the different levels of the damage identification process. As a contribution, this work presents a damage detection and classification methodology which includes the use of data collected from a structure under different structural states by means of a piezoelectric sensor network taking advantage of the use of guided waves, hierarchical nonlinear principal component analysis (h-NLPCA), and machine learning. The methodology is evaluated and tested in two structures: (i) a carbon fibre reinforced polymer (CFRP) sandwich structure with some damages on the multilayered composite sandwich structure and (ii) a CFRP composite plate. Damages in the structures were intentionally produced to simulate different damage mechanisms, that is, delamination and cracking of the skin.

Damage Classification of Composites Based On Analysis of Lamb Wave Signals Using Machine Learning

2020 ◽  
Author(s):  
Shweta Dabetwar ◽  
Stephen Ekwaro-Osire ◽  
Joao Paulo Dias
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Composite Materials ◽  
Fatigue Damage ◽  
Research Question ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Data Repository ◽  
Damage Classification

Abstract Composite materials have a myriad of applications in complex engineering systems, and multiple structural health monitoring strategies have been developed. However, these methods are challenging due to signal attenuation and excessive noise interference in composite materials. Signal processing can capture a small difference between the input-output signals associated with the severity of the damage in composites. Thus, the research question is "can signal processing techniques reduce the required number of features and assess the randomness of fatigue damage classification in composite materials using machine learning algorithms?" To answer this question, piezoelectric signals for carbon fiber reinforced polymer test specimens were taken from NASA Ames prognostics data repository. A framework based on a comparative analysis of signals was developed. For the first specific aim, the effectiveness of features based on statistical condition indicators of the sensor signals were evaluated. For the second specific aim, actuator-sensor signal pair were analyzed using cross-correlation to extract two features. These features were used to train and test four supervised machine learning (ML) algorithms for damage classification and their performance was discussed. For the third specific aim, randomness in the dataset of fatigue damage of the specimens was assessed. Results showed that by signal processing, the requirement of features for training ML was reduced with the improvement in the performance of ML. The randomness was captured by the utilization of two specimens from the same material. This work contributes to the improvement of intelligent damage classification of composite materials, operating under complex working conditions.

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