Saudi Cultural Missions Theses & Dissertations
Permanent URI for this communityhttps://drepo.sdl.edu.sa/handle/20.500.14154/10
Browse
4 results
Search Results
Item Restricted Deformation Monitoring of Retaining Walls using Terrestrial Laser Scanning(University of Nottingham, 2024) Algadhi, Ali Abdulaziz A; Psimoulis, Panagiotis; Neves, Luis Canhoto; Grizi, AthinaAlthough the replacement cost of retaining walls is relatively low compared to other infrastructure, leading to minor research interest in their inspection, the probability of failure and collapse in retaining walls is significantly higher than in other infrastructures. Before any failure occurs, certain defects tend to be observed in retaining walls. The common theme of the observed defects in many historical cases is a change in the geometry of the retaining walls; either on a global scale (e.g., tilt and lateral displacement) or locally (e.g., bulge and cracks). Terrestrial Laser Scanner (TLS) is one of the main contactless techniques that have been researched in monitoring the geometric deformations in retaining walls. Although the TLS often had a small error in monitoring geometric deformations (i.e., a few millimeters), some studies showed larger error (i.e., a few centimeters), suggesting that the tolerance of the TLS has not yet been fully investigated for detecting small geometric deformations, such as those for the serviceability limits of retaining structures. Factors such as the scanning range, angle of incidence, as well as the material and color of the scanned surface have already been studied regarding their influence on the accuracy of TLS measurements. However, their impact on the accuracy of deformation estimation between point-clouds at two epochs is yet to be investigated. The aim of this research is to propose a list of strategies to achieve an accuracy of 1 − 2mm in monitoring the global and local geometric deformations in retaining walls. Two experiments are conducted to simulate global and local geometric deformations using two prototypes of retaining walls. The experimental assessment focuses mainly on investigating the effect of (i) the type and amplitude of the deformation and (ii) scanning parameters (i.e., scanning distance and angle of incidence), and (iii) data analysis method on the deformation estimation using the TLS. This experimental work is done for two cases: (i) when the reference point-cloud was at the same location as the deformed cloud, and (ii) when the reference and deformed clouds are taken from different scanning positions. Furthermore, a case-study of monitoring the seasonal movement of retaining walls is conducted to examine the performance of TLS in monitoring small geometric deformations of retaining walls. The main contribution of this research lays on listing strategies and recommendations to achieve accuracy of within 1−2mmwhile using the TLS for monitoring small geometric deformations in retaining walls. The thesis contributes also to knowledge of the behaviour of retaining structures through the analysis for the case-study. Although it was shown in previous research that many cases had collapsed because of the additional hydro-static pressure in the retained soil, the results in this research suggests that the solar radiation had larger impact on the seasonal deformation than the change in the hydro-static pressure caused by the fluctuation in water level of the canal. Additionally, it was observed that the parts of the sheet piles that were exposed to the sunlight for longer period and/or at the peak of the solar radiation had larger seasonal deformation.35 0Item Restricted Drone-Assisted Stockpile Volume Estimation in Open and Confined Spaces(The University of Manchester, 2024-05-06) Alsayed, Ahmad A; Nabawy, MostafaThis thesis aims to develop unmanned aerial system solutions for stockpile volume estimation in both open and confined spaces. It starts with a comprehensive literature review that examines both traditional and recent stockpile volume estimation techniques employed in various environments. It was found that recently emerging aerial methods, such as drone-borne LiDAR sensors, can enable notable advantages including speed, safety, occlusion elimination, and enhanced accuracy compared to current typical industrial solutions. However, there is still a notable gap in research represented in the underdevelopment of cost-effective aerial solutions for safe and precise volume estimation within confined spaces. The research in this thesis starts with a detailed investigation of the state-of-the-art in utilising drones for operations within treacherous conditions, particularly within industrial confined spaces. It was found that existing studies have not thoroughly examined drone missions under operational constraints such as absence of GPS signals, dust-laden air, and poor/lack of visibility. These limitations defined the way for a relatively novel application where drones could be deployed to enhance inspection while augmenting safety measures. Following the establishment of this fresh perspective, mission planning, instrument development, and implementation of control and navigation strategies were assessed across diverse confined spaces and for various stockpile volume estimation missions in both simulated and real-world scenarios. A thorough cost-benefit analysis elucidated that drone-based solutions for stockpile volume estimation within confined spaces achieve a high Cost-Benefit Priority Factor (CPF) of 133-200. Moreover, this approach surpasses traditional industrial fixed sensor systems in flexibility, initial cost savings, and ability to serve multiple sites. Advancing further, a low-cost, yet effective approach was proposed that relies on actuating a single-point light detecting and ranging (1D LiDAR) sensor using a micro servo motor onboard of a drone. The collected LiDAR ranges were converted to a point cloud that allows the reconstruction of 3D stockpiles. The proposed approach was assessed via simulations of a wide range of mission operating conditions. The influences from modulating the drone flight trajectory, servo motion waveform, flight speed, and yawing speed on the mapping performance were all investigated. Comparing the volumetric error values, the average error from the proposed actuated 1D LiDAR system was 0.9% as opposed to 1% and 0.8% from the 2D and 3D LiDAR options, respectively. That said, compared to 2D and 3D LiDARs, the proposed system requires less scanning speed for data acquisition, is much lighter, and allows a substantial reduction in cost. Experimental tests on drone-based solutions for scanning a reference stockpile were conducted with either single or multiple drones equipped with 1D LiDAR sensors, achieving an average volumetric error of 2%. In contrast, the actuated single-point LiDAR system exhibited a higher volumetric error of 5% due to the significant number of outlier points involved. Finally, as the previously presented solutions required an external localisation system for their operation within confined spaces, this thesis paved the way to get rid of such requirement via applying an ICP (Iterative Closest Point) algorithm that can operate independently of such systems. The proposed algorithm uniquely employed a low-rate, low-dense LiDAR scan, specifically focusing on the horizontal layer of a 3D LiDAR for localisation and scan matching. Furthermore, a wall-following navigation strategy was employed for indoor navigation and path-planning to further streamline the mapping process. It was shown that the estimated volume of the reconstructed stockpiles has an average volumetric error of 3.7%, but this figure was enhanced to 0.4% when applying loop closure. Moreover, mapping using an actuated single-point LiDAR approach was processed using the ICP localisation method, resulting in a 1.4% volumetric error.31 0Item Restricted ANALYZING HYDROMORPHODYNAMICS AND SEDIMENTATION VARIATIONS IN THE LOWER APALACHICOLA RIVER SYSTEM(University of Florida, 2024-05-02) Alruzuq, Ali Rashed; Mossa, JoannRivers in the environment have been vulnerable to human impact since the first settlement, as humans relied on them for their services. Industrialization, urbanization, and agriculture, including irrigation, dredging, and damming, have been practiced for centuries. Those practices induced alterations in water flows and led to continuous impacts on ecosystem services. This research focuses on one of the larger rivers in the southeastern United States, the Apalachicola River, which has historically been a site of problematic sedimentation but needs more scientific investigation. Consequently, this dissertation examines the hydromorphodynamic and sedimentation variations of the Lower Apalachicola River. We used bathymetry survey data from 1960 and 2010 and conducted Digital Elevation Model (DEM) of Differences (DoD) analysis from the 50-year period when the Navigation Project was conducted. It included dredging and disposal, artificial cutoffs, snag removal by the United States Army Corps of Engineers. The river's net sediment gain and loss patterns were assessed using high-resolution DEMs and geostatistical approaches. It was noted that the DoD map showed an unusual elevation change, suggesting a pool on the riverbed between RM 29 and 27 when the Chipola River joins with the Apalachicola River. The cumulative sediment volume change and gross change (cumulative absolute change) per river mile of the lower Apalachicola River between 1960 and 2010 were quantified. The entire reach (RM ~45-0) has a loss of -8.36 million m3, a gain of 6.99 million m3, a net change of -1.37 million m3, and a gross change of 15.35 million m3. The upstream Chipola juncture has a loss of -6.41 million m3, a gain of 1.89 million m3, a net change of -4.52 million m3, and a gross change of 8.30. On the other hand, the downstream Chipola juncture has a loss of -1.95 million m3, a gain of 5.1 million m3, a net change of 3.14 million m3, and a gross change of 7.04 million m3. Comprehending the floodplain inundation of the Lower Apalachicola River can provide crucial knowledge for river ecosystem management. The second research topic of this dissertation used hydrodynamic modeling and remote sensing to improve the accuracy of inundation mapping. The LiDAR and multibeam sonar were fused to generate the DEM of the riverbed that was used to create inundation depth maps for low-flow (175 m3/s) and high-flow (4361 m3/s) conditions during the super-flood period of 2015 and 2016, through a comprehensive analysis of 2D hydraulic model HEC-RAS and Relative Elevation Model (REM). Satellite images were used for geospatial analysis to estimate the inundated areas and results were compared with the outcomes from the HEC-RAS and REM models. This research also identified and analyzed the natural and human factors responsible for the riverbed deformation using the DoD DEM and machine learning algorithms, including the random forest model and the XGBoost model. The research examined the factors that influenced riverbed chnages in the study region between 1960 and 2010, when the USACE conducted the Navigational Project. Using the comparative analysis of two machine learning regression models to determine the long-term riverbed change, we employed the random forest regression model and the Extreme Gradient Boosting regression model (XGBoost). The models were conducted with 10 factors for the given period, including neutral factors such as floodplain width, bank vegetation density, river curvature, Stream Power Index, Junctures, and Tidal and human factors such as Cutoffs, Dikes, Dredging and disposal, from 1960 to 2010. The study identified potential drivers of riverbed changes using machine learning algorithms. The random forest model outperformed the XGBoost model with the former having R-square values of 0.95 and 0.93 for the validation and testing sets, respectively, indicating high predictive accuracy. The XGBoost model was slightly less accurate, with R-square values of 0.75 and 0.74 for the validation and testing sets, respectively. In the random forest model, variables Floodplain Width, Dikes, and Junctures were the most influential factors on the riverbed, whereas Dredging was the most influential factor in the XGBoost model. The research provides decision-makers and local communities with the knowledge to prepare for the future of the river in the face of both natural and anthropogenic changes, mitigate potential dangers, and effectively manage land recovery.26 0Item Restricted Low Latency Transmission of Filtered LiDAR Point Cloud Data over RF (Radio Frequency) Channels: Enhancing Efficiency for Real-Time Applications(Sacred Heart University, 2024) Masrahi, Salem; Kaya, TolgaThis study presents a novel approach to enhancing teleoperation systems by integrating LiDAR point cloud data as a supplementary tool alongside traditional video streaming. Recognizing the limitations imposed by video transmission's heavy data requirements and latency issues, our research focuses on utilizing LiDAR technology not as a replacement but as a tactical supplement to camera systems. By applying algorithms that filter and transmit only essential points of the objects within specified degrees and distances, we aim to significantly reduce the data load. This method allows for the transmission of critical spatial information via RF (Radio Frequency) modems with a bandwidth as limited as 500kbps, typical of telemetry systems used in drones. The core of our investigation examines how heavily filtered LiDAR point cloud data can be effectively transmitted over these low-bandwidth channels, offering a potential breakthrough in remote sensing and communication for teleoperated applications. While acknowledging the utility of high-throughput RF modems that could, in certain scenarios, enable a reliance solely on LiDAR data, our research is particularly focused on optimizing data transmission within stringent bandwidth constraints. This approach promises substantial improvements in real-time data transmission efficiency and accuracy, addressing critical latency challenges in teleoperation and potentially transforming how robotic systems are remotely controlled and interacted with.12 0