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2007 research outputs found
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Analysis of GNSS-IMU Lidar Integration for Indoor Positioning Using Unscented Kalman Filter
No full textAccurate navigation systems are important in various vehicle applications, both indoors and outdoors. Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) are sensors that are often used in vehicle navigation systems. GNSS has the advantage of providing accurate position and speed information, IMU is able to make measurements without being affected by environmental conditions, and LiDAR sensors can model the environment; however, the limited signal on GNSS in indoor environments results in decreased position accuracy. The development of GNSS-IMU integration has been widely carried out, one of which is by adding a LiDAR sensor. In this study, an improvement will be made to the integration algorithm on Vision RTK2, which produces GNSS-IMU coordinate data, and Backpack Lidar, which can display 3D visualization on the traversed path using the Unscented Kalman Filter (UKF) method to improve navigation accuracy, especially in indoor environments. The results of the study showed that the UKF simulation and free outage conditions showed high accuracy with RMSE of 0.00308 m and 0.00175 m for the Easting and Northing positions and MAE of 0.00088 m and 0.00024 m. However, in outage conditions, the RMSE values were 4.0881 m and 8.6317 m, and MAE of 5.9871 m and 7.4182 m. The results of the 3D point cloud of the LiDAR model that had been georeferenced using the UKF fusion results and the KKH calculation results were validated using a rolling meter. Validation of point cloud processing from the 3D LiDAR model using a rolling meter and georeferencing with KKH calculations showed a small RMSE value, which was 0.3420 m, and 0.0354 m for the distance dimension with a rolling meter. 0.6358 m for georeferenced RMSE using UKF fusion data, and 0.0779 for distance dimension using roll meters. The small RMSE results indicate a high level of agreement between point cloud data and measurements using a rolling meter used as reference data. This study shows that the integration of GNSS-IMU sensors with LiDAR using the UKF method can improve the accuracy and reliability of indoor navigation systems
Numerical Analysis of Load-Bearing Capacity in Contaminated and Uncontaminated Soils Treated with Nanomaterials
No full textConstruction of load-bearing structures requires both a strong foundation and stable soil. For projects located on weak or contaminated soils, stabilization techniques are a prerequisite. Nanotechnology holds promise for improving soil strength and stability, offering innovative solutions for enhancing site conditions in geotechnical engineering. This numerical study explores the potential application of nano-clay (NC) and nano-silica (NS) in improving the overall load-bearing performance of a strip footing resting on clean and kerosene-contaminated soils. The objectives are to assess the impact of varying nanoparticle contents and curing durations on soil performance. Results suggested that adding NC and NS substantially enhances the bearing capacity ratio (BCR) up to a maximum of 4.76 and 4.33 at 1% NC and 1.5% NS, respectively, compared to untreated soil. Overdosing, however, resulted in reduced effectiveness, emphasizing the significance of optimal contents. Conversely, the BCR improvement was less noticeable in kerosene-contaminated soils until it peaked at 2.5% NS and 2% NC. However, results of both clean and contaminated soils revealed that nanomaterials negatively impact settlement behavior. Curing age was found to be a major factor affecting BCR, in which treated soils showed a consistent increase in BCR over time. These findings endorse the potential of nanomaterials for stabilizing soil used in geotechnical engineering. Careful selection of dosages and consideration of soil contamination are critical to optimizing performance in complex geotechnical conditions
Factors Affecting Properties of Cellular Lightweight Clay Improved with Fly Ash Geopolymer and Cement
No full textThis research investigated the unit weight and unconfined compressive strength (UCS) of cellular lightweight high-calcium fly ash geopolymer and cement-stabilized soft Bangkok clay (CLFAG-OPC stabilized SC) as potential lightweight embankments and backfill materials. The investigated parameters included the soft clay:fly ash (SC:FA) ratio (50:50 to 90:10), ordinary Portland cement (OPC) content (0%-3%), water content (1.5LL-3.0LL), liquid alkaline content (L) (0.6FA to 1.5FA), NS:NH ratio (0.5-3), NH concentration (8 M), air foam content (Ac) (0%-150% by SC volume), and curing time (7-90 days). The results indicated that the SC:FA ratio, OPC content, water content, NS:NH ratio, L content, and Ac significantly influenced both the unit weight and UCS of samples. Increasing water content, L content, and Ac generally reduced unit weight, except when influenced by FA content, OPC content, and the NS:NH ratio. The optimal composition for maximum UCS was achieved with an SC:FA ratio of 50:50, OPC content of 3%, water content of 2.0LL, NS:NH ratio of 1, L content of 0.6FA, and 0% Ac. A predictive equation for unit weight was proposed using phase diagrams. Additionally, mix design charts were shown to be valuable tools for calculating the unit weight and UCS, demonstrating their effectiveness for lightweight embankment and backfill applications
Leaching-Permeability Behavior of Collapsible Gypseous Soils Treated with Nano-Titanium Dioxide
No full textAs a result of the limited studies that have been conducted on the utilization of nano titanium dioxide as a nanomaterial for stabilizing gypseous soils in geotechnical works, this study is directed to predict the changes in the coefficient of permeability k, the leaching strain, the total dissolved salts TDS, and the pH values with the changes in the percentages of nano titanium dioxide NTD. The gypseous soil samples were obtained from three sites located north of Baghdad, the capital of Iraq, with different gypsum contents of about 34%, 50%, and 60%. Tests have identified the mechanical and physical characteristics of the studied gypseous soils. In addition, oedometer permeability leaching tests were conducted using an oedometer cell apparatus. The results of the tested gypseous soils presented a significant effect of NTD on reducing the coefficient of permeability k and cost-effectively, especially at 0.3 and 0.5% for the three tested soils. For S1 tested soil, the reduction percentages of the k values were 79.02% and 80.0% when treated with 0.3% and 0.5% of NTD, respectively. While for S2 tested gypseous soil, the reduction percentages were 75.9% and 79.1%, and 66.04% and 73.6% for S3 tested gypseous soil when treated with 0.3% and 0.5% of NTD, respectively. The treated gypseous soils are exposed to less gypsum dissolution, as the NTD material forms an impermeable layer to prevent direct contact between water and gypsum. This reduces gypsum dissolution and, thus, reduces leaching strain. For S1 tested soil, the percentage of reduction of the leaching strain was 90.5%, while for S2 and S3 tested soils, it was 91.2% and 89.9%, respectively, when 0.3% of NTD was applied. As the percentage of the NTD increased for S1, S2, and S3, the pH values decreased due to decreased TDS in the leached water, and it is clear that 0.3% of NTD gives a reliable pH value for the three tested soils. Considering these results, it appears that even small amounts of nano titanium dioxide have the potential to be an effective agent for reducing permeability and stabilizing collapsible gypseous soils in civil engineering projects, compared with other nano or traditional materials
Performance Characterization for Polymer Modified Bitumen Contained Newly Used Terpolymer
No full textPolymer-modified bitumen (PMB) plays a vital role in extending the service life of hot mix asphalt (HMA) used in flexible pavement construction. Several types of polymers have been used to produce PMB, among which styrene–butadiene–styrene (SBS) is the most widely used. However, the use of SBS in PMB production presents several limitations, including storage stability issues, high mixing temperatures, and the requirement for a relatively high modifier content. The present research investigated the use of a new terpolymer, EVA-GMA (LOTADER® AX8670T), for PMB production and compared the resulting PMB with PMB produced using 4% SBS polymer. Rheological, performance, and chemical composition tests were conducted on neat bitumen as well as PMB modified with EVA-GMA and SBS. The results indicated that the optimal LOTADER® AX8670T content required to produce PMB was 2.5%. In addition, storage stability increased by 11% compared to 4% SBS-modified PMB. The viscosity was found to be 50% higher than that of asphalt modified with 4% SBS-PMB and 100% higher than that of unmodified asphalt. The performance grade (PG) was determined to be PG 82-10 for both PMB types, while unmodified bitumen exhibited a PG of 76-10. Based on these results, it can be concluded that PMB produced with LOTADER® AX8670T can perform comparably to SBS-modified PMB while requiring a lower modifier content, lower mixing temperatures, and offering improved storage stability, thereby enhancing economic, production, and environmental aspects
Using the Kalman Filter with Satellite Altimetry to Estimate the Water Level of Inland Water
No full textThe Euphrates River extends for approximately 2,700 km, making it the longest river in Southwest Asia. Reliable water level measurements are obtained through the integration of an advanced outlier rejection system with Kalman filter technology. This study employs water level data from the Database for Hydrological Time Series over Inland Waters (DAHITI) and validates them using in situ measurements collected from gauging stations along the Euphrates River. To improve the accuracy of water level time series across the study area (Lat: 31.9676, Lon: 44.9306 to Lat: 31.0955, Lon: 46.0942), the research incorporates multibeam altimetry data from Envisat, Jason-2, and Sentinel-3A/B/B. Validation of the altimetry techniques is carried out by comparing DAHITI water level records with in situ measurements and other satellite-based datasets. Both the Kalman filter and Hydroweb methods yield Unbiased Root Mean Square Difference (ubRMSD) values ranging between 0.2961–0.3922 cm and 0.536–0.577 cm, respectively. The Nash-Sutcliffe Efficiency coefficient for DAHITI-derived water levels varies between 0.5971 and 0.9831, while Hydroweb produces values from –0.871 to 0.567. Overall, DAHITI-based altimetry height estimates demonstrate superior accuracy compared to other altimeter datasets in most parts of the Euphrates River, with precision strongly influenced by river topography. The application of Kalman filtering further enhances water level monitoring, particularly in regions characterized by complex inland water structures
Robust Ensemble Machine Learning for Flash Flood Susceptibility Mapping Across Semiarid Regions
No full textFlash floods cause severe environmental and socio-economic impacts in arid and semi-arid regions. This study aims to improve the accuracy of flash flood susceptibility mapping in southwestern Morocco’s Assaka watershed by using an ensemble of machine learning models. Four models, Logistic Regression (LR), Multivariate Discriminant Analysis (MDA), Naïve Bayes (NB), and Multilayer Perceptron (MLP), were trained on a flood inventory of over 1.5 million data points and 14 environmental factors (e.g., altitude, slope, land surface temperature, soil moisture index). Each model produced a susceptibility map, and a voting ensemble of the top-performing models (all above 80% accuracy) further improved reliability. The MLP achieved the highest predictive performance, followed closely by LR and MDA. Sensitivity analysis identified altitude, topographic position index, land surface temperature, and soil moisture index as the most influential factors. The ensemble susceptibility map highlights densely populated areas near the city of Guelmim and infrastructure along major rivers as most prone to flash flooding. These findings enable practical mitigation measures such as improved drainage, early warning systems, and better land-use planning in high-risk zones. Integrating multiple models in an ensemble is a novel approach that reduces uncertainty and provides a more robust tool for flash flood risk prediction
Damage Evolution and Failure Mechanism of Segmental Tunnel Lining
No full textThe prevention and treatment of damage in segmental tunnel lining structures are critical issues in maintaining tunnel integrity. Understanding the damage evolution and failure mechanisms of these structures is essential for their effective management. This study establishes refined numerical models for shield tunnel segmental linings, incorporating critical factors such as localized weakening around hand holes, multi-interface contact behavior, and embedded reinforcement. A total strain crack model is employed to accurately simulate the nonlinear behavior of concrete. The analysis focuses on the compression-bending failure behavior of segmental joints under positive bending moments and investigates the failure mechanisms of segmental linings subjected to surcharge loading. The results show that the deformation of segmental joints under bending moments can be divided into three stages: linear elasticity, elastoplasticity, and failure. The failure mechanism involves the progressive expansion and penetration of cracks in the core pressure-bearing area, leading to increased crack width, yielding of bolts and rebars, and eventual failure. The overall instability failure of segmental tunnel linings is caused by local failures in areas of low stiffness (joints, hand holes), exhibiting progressive failure characteristics. This study presents significant originality and practical value. A refined analytical model of shield tunnel structures is developed to capture the millimeter-scale cracking characteristics of segmental concrete linings. The model enables precise analysis of the mechanical response of shield tunnels under external construction-induced loading
Statistical Analyses of the Euphrates River Entry and Hydrological Drought Assessment (SDI)
No full textThe Euphrates River, a vital water resource in Iraq, has seen a marked decline in flow over the past two decades due to climate change and upstream interventions. The aim of this study is to investigate the impacts of changing rainfall patterns and temperature on the river's water balance, flow regime, and drought index. Results show an annual rainfall decline of 0.15 mm, while maximum and minimum temperatures increased annually by 0.086°C and 0.066°C, respectively, according to the Mann-Kendall trend and Sen’s slope tests. Monthly rainfall generally decreased, except for slight increases in April (0.32 mm) and October (0.018 mm). July 2017 and August 2003 saw peak temperatures of 45.1°C, while January 2008 recorded a minimum of -1.8°C. The box-and-whisker plot revealed high rainfall variability in November and February. River flow dropped by 41%, mainly due to the Turkish GAP project and climate impacts. HEC-DSS software analyzed flow duration over 32 years, and Pearson’s correlation showed low associations between flow rate and temperature (-0.36) and rainfall (0.29). The Drinc program was utilized to calculate the Standardized Drought Index, which identified that the water year 1987–1988 was very wet, while it detected severe droughts in 2014–2015 and 2021–2022. Overall, climate change and upstream dam construction have significantly reduced Euphrates River discharges, intensifying drought conditions in the region. The long-term changes in precipitation and air temperature in the study area support the observed streamflow trends. The findings of this study demonstrate that a cooperative approach to international water management between the riparian states is crucial
Sizing Optimization of Trusses Using Elitist Stepped Distribution Algorithm
No full textThis study investigates the efficiency of the recently developed Elitist Stepped Distribution Algorithm (ESDA) as a metaheuristic framework for truss sizing optimization. ESDA builds upon the Cross-Entropy Method by introducing an elitist stepped sampling strategy that improves the balance between exploration and exploitation during the search process. To evaluate its effectiveness, ESDA is applied to a comprehensive test suite comprising seven benchmark truss optimization problems that cover a wide range of sizes, design variables, loading conditions, and constraint types. In all cases, the objective is to minimize structural weight while satisfying stress, displacement, and stability requirements. Numerical experiments are conducted with the proposed method, and the results are compared with those algorithms reported in the literature. The findings show that ESDA attains new best or near-best solutions for large-scale problems such as the 117-bar cantilever, 130-bar transmission tower, 354-bar dome, and 942-bar tower trusses, while also producing competitive results for the 25-bar, 72-bar, and 200-bar structures with relatively modest computational effort. The novelty of this work lies in demonstrating the robustness, efficiency, and scalability of ESDA across diverse benchmarks, highlighting its potential for future structural optimization applications