111 research outputs found
Discrete and Distributed Error Assessment of UAS- SfM Point Clouds of Roadways
Perishable surveying, mapping, and post-disaster damage data typically require efficient and rapid field collection techniques. Such datasets permit highly detailed site investigation and characterization of civil infrastructure systems. One of the more common methods to collect, preserve, and reconstruct three-dimensional scenes digitally, is the use of an unpiloted aerial system (UAS), commonly known as a drone. Onboard photographic payloads permit scene reconstruction via structure-from-motion (SfM); however, such approaches often require direct site access and survey points for accurate and verified results, which may limit its efficiency. In this paper, the impact of the number and distribution of ground control points within a UAS SfM point cloud is evaluated in terms of error. This study is primarily motivated by the need to understand how the accuracy would vary if site access is not possible or limited. In this paper, the focus is on two remote sensing case studies, including a 0.75 by 0.50-km region of interest that contains a bridge structure, paved and gravel roadways, vegetation with a moderate elevation range of 24 m, and a low-volume gravel road of 1.0 km in length with a modest elevation range of 9 m, which represent two different site geometries. While other studies have focused primarily on the accuracy at discrete locations via checkpoints, this study examines the distributed errors throughout the region of interest via complementary light detection and ranging (lidar) datasets collected at the same time. Moreover, the international roughness index (IRI), a professional roadway surface standard, is quantified to demonstrate the impact of errors on roadway quality parameters. Via quantification and comparison of the differences, guidance is provided on the optimal number of ground control points required for a time-efficient remote UAS survey
Deep Learning Classification of 2D Orthomosaic Images and 3D Point Clouds for Post-Event Structural Damage Assessment
Efficient and rapid data collection techniques are necessary to obtain transitory information in the aftermath of natural hazards, which is not only useful for post-event management and planning, but also for post-event structural damage assessment. Aerial imaging from unpiloted (gender-neutral, but also known as unmanned) aerial systems (UASs) or drones permits highly detailed site characterization, in particular in the aftermath of extreme events with minimal ground support, to document current conditions of the region of interest. However, aerial imaging results in a massive amount of data in the form of two-dimensional (2D) orthomosaic images and three-dimensional (3D) point clouds. Both types of datasets require effective and efficient data processing workflows to identify various damage states of structures. This manuscript aims to introduce two deep learning models based on both 2D and 3D convolutional neural networks to process the orthomosaic images and point clouds, for post windstorm classification. In detail, 2D convolutional neural networks (2D CNN) are developed based on transfer learning from two well-known networks AlexNet and VGGNet. In contrast, a 3D fully convolutional network (3DFCN) with skip connections was developed and trained based on the available point cloud data. Within this study, the datasets were created based on data from the aftermath of Hurricanes Harvey (Texas) and Maria (Puerto Rico). The developed 2DCNN and 3DFCN models were compared quantitatively based on the performance measures, and it was observed that the 3DFCN was more robust in detecting the various classes. This demonstrates the value and importance of 3D datasets, particularly the depth information, to distinguish between instances that represent different damage states in structures
Deep Learning Classification of 2D Orthomosaic Images and 3D Point Clouds for Post-Event Structural Damage Assessment
Efficient and rapid data collection techniques are necessary to obtain transitory information in the aftermath of natural hazards, which is not only useful for post-event management and planning, but also for post-event structural damage assessment. Aerial imaging from unpiloted (gender-neutral, but also known as unmanned) aerial systems (UASs) or drones permits highly detailed site characterization, in particular in the aftermath of extreme events with minimal ground support, to document current conditions of the region of interest. However, aerial imaging results in a massive amount of data in the form of two-dimensional (2D) orthomosaic images and three-dimensional (3D) point clouds. Both types of datasets require effective and efficient data processing workflows to identify various damage states of structures. This manuscript aims to introduce two deep learning models based on both 2D and 3D convolutional neural networks to process the orthomosaic images and point clouds, for post windstorm classification. In detail, 2D convolutional neural networks (2D CNN) are developed based on transfer learning from two well-known networks AlexNet and VGGNet. In contrast, a 3D fully convolutional network (3DFCN) with skip connections was developed and trained based on the available point cloud data. Within this study, the datasets were created based on data from the aftermath of Hurricanes Harvey (Texas) and Maria (Puerto Rico). The developed 2DCNN and 3DFCN models were compared quantitatively based on the performance measures, and it was observed that the 3DFCN was more robust in detecting the various classes. This demonstrates the value and importance of 3D datasets, particularly the depth information, to distinguish between instances that represent different damage states in structures
Design of the Reverse Logistics System for Medical Waste Recycling Part I: System Architecture, Classification & Monitoring Scheme, and Site Selection Algorithm
With social progress and the development of modern medical technology, the
amount of medical waste generated is increasing dramatically. The problem of
medical waste recycling and treatment has gradually drawn concerns from the
whole society. The sudden outbreak of the COVID-19 epidemic further brought new
challenges. To tackle the challenges, this study proposes a reverse logistics
system architecture with three modules, i.e., medical waste classification &
monitoring module, temporary storage & disposal site selection module, as well
as route optimization module. This overall solution design won the Grand Prize
of the "YUNFENG CUP" China National Contest on Green Supply and Reverse
Logistics Design ranking 1st. This paper focuses on the description of
architectural design and the first two modules, especially the module on site
selection. Specifically, regarding the medical waste classification &
monitoring module, three main entities, i.e., relevant government departments,
hospitals, and logistics companies, are identified, which are involved in the
five management functions of this module. Detailed data flow diagrams are
provided to illustrate the information flow and the responsibilities of each
entity. Regarding the site selection module, a multi-objective optimization
model is developed, and considering different types of waste collection sites
(i.e., prioritized large collection sites and common collection sites), a
hierarchical solution method is developed employing linear programming and
K-means clustering algorithms sequentially. The proposed site selection method
is verified with a case study and compared with the baseline, it can immensely
reduce the daily operational costs and working time. Limited by length,
detailed descriptions of the whole system and the remaining route optimization
module can be found at https://shorturl.at/cdY59.Comment: 8 pages, 6 figures, submitted to and under review by the IEEE
Intelligent Vehicles Symposium (IV 2023
Design of the Reverse Logistics System for Medical Waste Recycling Part II: Route Optimization with Case Study under COVID-19 Pandemic
Medical waste recycling and treatment has gradually drawn concerns from the
whole society, as the amount of medical waste generated is increasing
dramatically, especially during the pandemic of COVID-19. To tackle the
emerging challenges, this study designs a reverse logistics system architecture
with three modules, i.e., medical waste classification & monitoring module,
temporary storage & disposal site (disposal site for short) selection module,
as well as route optimization module. This overall solution design won the
Grand Prize of the "YUNFENG CUP" China National Contest on Green Supply and
Reverse Logistics Design ranking 1st. This paper focuses on the design of the
route optimization module. In this module, a route optimization problem is
designed considering transportation costs and multiple risk costs (e.g.,
environment risk, population risk, property risk, and other accident-related
risks). The Analytic Hierarchy Process is employed to determine the weights for
each risk element, and a customized genetic algorithm is developed to solve the
route optimization problem. A case study under the COVID-19 pandemic is further
provided to verify the proposed model. Limited by length, detailed descriptions
of the whole system and the other modules can be found at
https://shorturl.at/cdY59.Comment: 6 pages, 4 figures, under review by the 26th IEEE International
Conference on Intelligent Transportation Systems (ITSC 2023
Improvement of Low Traffic Volume Gravel Roads in Nebraska
In the state of Nebraska, over one-third of roadways are unpaved, and consequently require a significant amount of financial and operational resources to maintain their operation. Undesired behavior of surface gravel aggregates and the road surfaces can include rutting, corrugation, and ponding that may lead to reduced driving safety, speed or network efficiency, and fuel economy. This study evaluates the parameters that characterize the performance and condition of gravel roads overtime period related to various aggregate mix designs. The parameters, including width, slope, and crown profiles, are examples of performance criteria. As remote sensing technologies have advanced in the recent decade, various techniques have been introduced to collect high quality, accurate, and dense data efficiently that can be used for roadway performance assessments. Within this study, two remote sensing platforms, including an unpiloted aerial system (UAS) and ground-based lidar scanner, were used to collect point cloud data of selected roadway sites with various mix design constituents and further processed for digital assessments. Within the assessment process, statistical parameters such as standard deviation, mean value, and coefficient of variance are calculated for the extracted crown profiles. In addition, the study demonstrated that the point clouds obtained from both lidar scanners and UAS derived SfM can be used to characterize the roadway geometry accurately and extract critical information accurately
Damage Assessment of a Sixteen Story Building Following the 2017 Central Mexico Earthquake
The 2017 M7.1 Central Mexico Earthquake caused significant infrastructural damage in the Mexico City area. The earthquake contained a significant pulse in the long period, resulting in numerous buildings severely damaged or collapsed. This paper discusses a reinforced concrete building which was still partially occupied post-earthquake. The building’s interior walls were examined to have substantial damage, including some extensive cracking. In January 2018, the authors visited the structure and collected detailed assessment data. The data collection included ground-based lidar scans and recorded ambient vibrations of the damaged structure using accelerometers. Eleven scans were collected from the four exterior facades to create a three-dimensional point cloud of the building. The collected point cloud data were used to measure and quantify the permanent deformation of the structure at three corners as well as to generate depth maps of two parallel exterior walls. The measurements based on the lidar point cloud data are accurate with an error of 2 mm at 10 meters, enabling high resolute and accurate assessments. As for the accelerometers, one setup with sixty minutes of ambient vibrations data collection was performed. Twenty unidirectional accelerometers were installed on the basement, ground, second, fourth, eighth, tenth and roof floors at southwest and northeast corners to capture the torsional and translational acceleration structural response. The collected data can be used to perform system identification throughout operational modal analysis to demonstrate the dynamic and modal properties of the structures. Both the lidar and system identification sensing techniques provide essential input to establish and calibrate a detailed finite element model. The outputs are used to validate through the comparison of modal frequencies obtained in operational modal analysis method. Besides, the finite element model also provides a detailed response and insight to understand performance under future earthquakes
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