Resurfacing Research Articles

Optimal sand transport roadbed geometry structure of Taklamakan Desert highway

In order to obtain the optimal geometric structure for sand transport in desert roads, this study fully utilizes the natural sand transport capacity of the desert roads, aiming to reduce occurrences of road damage due to sand burial. The research integrates Computational Fluid Dynamics (CFD) with optimization analysis theory. Various typical geometric structures of desert highway roadbed were modeled using the Design Exploration module in CFD. Optimization analysis methods were employed to model and compute the sediment transport optimization design on both the roadbed and road surface. Then, the initial sample points are obtained by using the Design of Experiments (DOE), and the response surface is established by using the Kriging model to obtain the change trend of the input parameters and the objective function. Finally, the cross-section parameters of the sediment transport subgrade corresponding to different inflow conditions are obtained. The results show that the sediment transport performance of embankment, cutting and semi-filled uphill subgrade is negatively correlated with the height of subgrade. The relationship between slope gradient and sediment transport performance of subgrade depends on the height of subgrade and the type of subgrade section. For embankment and cutting, when the subgrade height is less than 0.5 m, the sediment transport performance of the subgrade is positively correlated with the subgrade slope; however, when the subgrade height exceeds 0.5 m, the sediment transport performance is negatively correlated with the subgrade slope. For the semi-filled uphill flow subgrade, the sediment transport performance of the subgrade is negatively correlated with the subgrade slope. Comprehensive analysis shows that semi-filled and excavated downhill subgrade has the best sand transport performance, embankment subgrade has the second highest sand transport capacity, then cutting subgrades, while semi-filled and excavated uphill subgrade has the worst sand transport performance. The research conclusions provide valuable scientific guidance for the design of sand control embankment structures tailored to local conditions for desert highways. This is of significant importance for enhancing the sediment transport capacity of desert highways and prolonging their service life.

Quality Evaluation of Road Surface Markings with Uncertainty Aware Regression and Progressive Pretraining

Maintaining high-quality road markings is essential for both safety and traffic flow. However, there has been limited research on automating the process of evaluating the quality of these markings and identifying degraded ones that need to be fixed. Our paper introduces a new approach that uses uncertainty aware (UA) regression to evaluate the quality of road surface markings. The approach is based on deep learning models and a unique training method called “progressive pretraining (PPT).” We used a dataset of RGB images which we converted to binary masks. These masks were then augmented and used to train convolutional neural networks models with a PPT strategy. The results showed that both the hybrid and UA models managed to outperform the baseline model in some metrics such as mean average error which was at 24.38% and accuracy with 81.27%. Additionally, each model showed unique strengths across various performance metrics, highlighting the efficacy of integrating uncertainty and progressive learning in quality assessment tasks. This study presents a solid proof of concept for the application of UA methods in quality evaluation tasks in general, and surface marking quality evaluation in particular.

Innovative road distress detection (IR-DD): an efficient and scalable deep learning approach

In the rapidly evolving landscape of transportation infrastructure, the quality and condition of road networks play a pivotal role in societal progress and economic growth. In the realm of road distress detection, traditional methods have long grappled with manual intervention and high costs, requiring trained observers for time-consuming and expensive data collection processes. The limitations of these approaches are compounded by challenges in adapting to diverse road surfaces and handling low-resolution data, particularly in early automated distress survey technologies. This article addresses the critical need for efficient road distress detection, a key component of ensuring safe and reliable transportation systems. Effectively addressing these challenges is crucial for enhancing the efficiency, accuracy, and safety of road distress detection systems. Leveraging advancements in object detection, we introduce the Innovative Road Distress Detection (IR-DD), a novel framework that integrates the YOLOv8 algorithm to enhance the accuracy and real-time capabilities of road distress detection, catering to applications such as smart cities and autonomous vehicles. Our approach incorporates bidirectional feature pyramid network (BiFPN) recursive feature fusion and bidirectional connections to optimize the utilization of multi-scale features, addressing challenges related to information loss and gradients encountered in traditional methods. Comprehensive experimental analysis demonstrates the superior performance, efficiency, and robustness of our integrated approach, positioning it as a cost-effective and compelling alternative to conventional road distress detection methods. Our findings demonstrate the superior performance of our approach compared to other state-of-the-art methods across various evaluation metrics, including precision, recall, F1 score, and mean average precision (mAP) at different intersection over union (IoU) thresholds. Specifically, our method achieves notable results with a precision of 0.666, F1 score of 0.630, mAP@0.5 of 0.650, all while operating at a speed of 86 frames per second (FPS). These outcomes underscore the effectiveness of our approach in real-time road distress detection. This article contributes to the ongoing innovation in object detection techniques, emphasizing the practicality and effectiveness of our proposed solution in advancing the field of road distress detection.

3D dynamic contact analysis of tyre internal deformation using 2D image sensor

Due to the increasing weight of electric vehicles, advanced tyre technology is urgently needed. In particular, there is a need for dynamic contact analysis to determine the energy interaction between tyres and road surfaces. However, the methods developed to date are limited in that they require either numerous contact patches inside the tyre or sensor devices on the ground. In this study, we propose a new method for dynamic tyre-ground contact analysis that overcomes these limitations. By installing a two-dimensional image sensor inside the tyre, deformations can be observed and quantified three-dimensionally during vehicle operation. This method advances tyre engineering through innovative analysis of tyre deformation during operation.

GGMNet: Pavement-Crack Detection Based on Global Context Awareness and Multi-Scale Fusion

Accurate and comprehensive detection of pavement cracks is important for maintaining road quality and ensuring traffic safety. However, the complexity of road surfaces and the diversity of cracks make it difficult for existing methods to accomplish this challenging task. This paper proposes a novel network named the global graph multiscale network (GGMNet) for automated pixel-level detection of pavement cracks. The GGMNet network has several innovations compared with the mainstream road crack detection network: (1) a global contextual Res-block (GC-Resblock) is proposed to guide the network to emphasize the identities of cracks while suppressing background noises; (2) a graph pyramid pooling module (GPPM) is designed to aggregate the multi-scale features and capture the long-range dependencies of cracks; (3) a multi-scale features fusion module (MFF) is established to efficiently represent and deeply fuse multi-scale features. We carried out extensive experiments on three pavement crack datasets. These were DeepCrack dataset, with complex background noises; the CrackTree260 dataset, with various crack structures; and the Aerial Track Detection dataset, with a drone’s perspective. The experimental results demonstrate that GGMNet has excellent performance, high accuracy, and strong robustness. In conclusion, this paper provides support for accurate and timely road maintenance and has important reference values and enlightening implications for further linear feature extraction research.

ЕКОЛОГІЧНІ РИЗИКИ ВПРОДОВЖ ЖИТТЄВОГО ЦИКЛУ АВТОМОБІЛЬНОЇ ДОРОГИ

Introduction. The conditions of economic development and social changes in Ukraine require accelerated development of road transport infrastructure, which includes the road industry, motor roads and facilities located on them. During the construction, operation and repair of motor roads, operating vehicles emit pollutants from fuel combustion products into the air, and generate noise and vibration. Pollution of soils, groundwater, and then water bodies can occur due to the deposition of pollutants and dust emitted from running car engines on the road surface, as well as dust from worn tires, road markings, car parts, the road surface itself, used industrial oil, oil leaks, and de-icing materials (in winter). Problem statement. Today, the country is faced with the task of actively working to modernize, expand and improve the entire road network. However, economic development and the scale of natural resource use should not be a hazard to ecological balance. Objective. Taking into account environmental aspects in the design and operation of the motor road and transport infrastructure facilities intended for its maintenance can lead to an increase in the service life of these structures, a decrease in energy and material consumption for their restoration. Materials and methods. The life cycle assessment method allows for a comprehensive approach that recognizes strategies to reduce environmental impacts throughout all stages of transport infrastructure facilities life cycle, from design to end of life (if there is no possibility of their further restoration).

Preparation of geopolymer for in-situ pavement construction on the moon utilizing minimal additives and human urine in lunar regolith simulant

Constructing lunar pavements is of great significance for improving the transportation efficiency of materials and personnel transfer at lunar bases. Utilizing lunar regolith for the in-situ preparation of geopolymer is an effective means of supplying raw materials for lunar pavement construction. This present study prepared geopolymers for pavement material with lunar regolith simulant. The influence of NaOH on the compressive strength of geopolymers was understood by using different percentages of NaOH as alkaline activator. The effect of urine on the compressive strength of geopolymers by using artificial urine of different pH values as liquid phases. The results indicated that the addition of artificial urine slightly reduces the compressive strength of geopolymers, and the effects of pH and NaOH percentage are different. Microstructural analysis performed by Mercury Intrusion Porosimetry and Scanning Electron Microscope, indicated that choosing fine-particle lunar regolith simulant as precursor is more conducive to the preparation of high-strength geopolymers using human urine.

Study of Concrete Pavement at Chichpalli, Jambharla

Concrete pavement is a type of road surface made from a mixture of cement, water, and aggregates such as sand and gravel. It is a durable and long-lasting option for roads, highways, and parking lots due to its strength and resistance to wear and tear. The process of constructing concrete pavement involves preparing the subgrade, placing a base layer of aggregate material, and then pouring and finishing the concrete mixture on top. The concrete is typically reinforced with steel bars or fibers to improve its tensile strength and prevent cracking. Once the concrete pavement has been poured and cured, it provides a smooth and even surface for vehicles to travel on. It is also easy to maintain and repair, as damaged sections can be easily replaced or patched. Concrete pavement is a popular choice for infrastructure projects due to its durability, longevity, and low maintenance requirements

Influence of Sustainable Grout Material on the Moisture Damage of Semi-flexible Pavement

Semi-flexible pavement (SFP), also known as grouted pavement, is a type of pavement structure consisting of a porous asphalt skeleton with air voids between 25 and 35% injected with cementitious grout materials. The special skeleton of SFP provided enhanced durability and resilience, making it a promising option for the construction of road surfaces in high-traffic areas and severe conditions. The main aim of the current research is to investigate the rutting behavior and moisture resilience of SFP-containing sustainable grout using ceramic waste powder (CWP). This research introduced the use of CWP as a replacement for conventional grout (cement) in SFP for the first time. CWP partially replaced cement at ratios of 15, 20, 30, 40, and 50% of the cement weight. Indirect Tensile Strength (ITS) and Hamburg Wheel Tracking Tests (HWTT) were used to evaluate the performance of SFP to reduce the detrimental effect of moisture. The grout modified with CWP shows excellent results in the ITS and Tensile Strength Ratio (TSR), and all modified SFP mixtures give higher values in these tests compared with Control Mix (CM). In the HWTT, the minimum rut depth of the modified SFP was 2.8 mm and 3.10 mm. Compared to CM, rutting decreased by 73%–76% for CWP mixes with 20% and 30% replacement. This indicates the high fluidity of CWP, which enabled it to penetrate all voids in the porous pavement (PA) and form a dense microstructure due to its excellent pozzolanic interaction, making it a strong structure capable of bearing rutting.

MAGNO TUNE: ADVANCED ROAD SURFACE RECOGNITION & EMS OPTIMIZATION

Electromagnetic suspension systems represent a revolutionary technology in the automotive industry, offering unparalleled comfort, control, and safety in vehicle dynamics. This project delves into the design, implementation, and performance evaluation of electromagnetic suspension systems used in vehicles and road surface detection. This paper is an overview of traditional suspension systems and their limitations in providing a smooth ride quality while maintaining optimal handling characteristics. It then introduces electromagnetic suspension systems as an innovative solution to these challenges. Key components and principles of electromagnetic suspension systems are discussed, including electromagnetic actuators and sensors for road surface detection. The integration of these components into the vehicle chassis is explained, highlighting the synergy between mechanical and electrical engineering disciplines. Furthermore, the project explores the benefits of electromagnetic suspension systems, such as enhanced ride comfort, improved stability, and adaptive damping capabilities. Overall, this project provides valuable insights into the advancements and applications of electromagnetic suspension systems, paving the way for the future of vehicle ride and handling dynamics.

Theoretical and Experimental Investigations of Identifying Bridge Damage Using Instantaneous Amplitude Squared Extracted from Vibration Responses of a Two-Axle Passing Vehicle

Identifying bridge damage using a movable test vehicle is highly regarded for its mobility, cost-effectiveness, and broad monitoring coverage. Previous studies have shown that the residual contact-point (CP) response between connected vehicles is free of the impact of vehicle self-vibrations and road roughness, making it particularly suitable for the indirect extraction of bridge modal properties. However, most experimental campaigns regarding contact-point (CP) responses focus on a single-axle testing vehicle within a non-moving state. This study aims to theoretically and experimentally identify bridge damage using the instantaneous amplitude squared (IAS) extracted from the residual CP response of a two-axle passing vehicle. First, the closed-form solution of the residual CP acceleration was derived for a two-axle vehicle interacting with a simply supported beam. The IAS index was constructed from the driving frequency of the residual CP acceleration. Then, numerical investigations using finite element simulation were conducted to validate using the IAS index for indirect bridge damage identification. The application scope of the approach under various vehicle speeds and road roughness grades was examined. Finally, a laboratory vehicle–bridge interaction system was tested to validate the approach. Numerical studies demonstrated that bridge damage could be directly determined by observing the IAS abnormalities, which were baseline-free. The IAS from the residual CP response outperformed the IAS from CP responses in identifying bridge damage. However, it was better to use the IAS when the vehicle speed was no greater than 2 m/s and the grade of the road surface roughness was not high. Laboratory tests showed that it was possible to identify bridge damage using the IAS extracted from the residual CP acceleration under perfect road surfaces. However, it fell short under rough road surfaces. Hence, further experiments are required to fully examine the capacity of the IAS for bridge damage identification in practical applications.

Validation of stamper device to measure international roughness index IRI

Measuring International Roughness Index (IRI) is one of the tasks in the process of checking, monitoring and evaluating road surface quality. In Vietnam, the types of IRI measuring devices are still limited. There are specialized devices with high accuracy and speed, but the cost is high. Therefore, they are not commonly used for regular monitoring. To measure IRI regularly and conveniently, it is necessary to use device that is easy to use, convenient, with high speed, high accuracy, but at a reasonable cost. The STAMPER (System with Two Accelerometers for Measuring Profile, Enabling Real-time data collection) from Taisei Rotec company (Japan) has been used to measure on different types of roads in Hanoi: city road, expressway and national highway. Data will be collected many times, with speed ranges and by many types of vehicles. The results show that STAMPER can measure IRI quickly and stably. It is easy to install on many type of vehicles and gives accurate, reliable measurement results

Delivery rate alters the effects of tire wear particles on soil microbial activities

BackgroundTire wear particles (TWPs) produced by the abrasion between tires and road surfaces have been recognized as an emerging threat to soil health globally in recent years. They can be transported from the road surface to adjacent soil at different delivery rates, with precipitation a main driver underpinning this movement. However, studies typically assume an abrupt exposure of TWPs in their experimental design. In this study, we investigated the impacts of abrupt and gradual delivery of TWPs on soil physicochemical properties and microbial activities. We used two different delivery rates of TWPs (abrupt and gradual) and devised two experimental phases, namely the TWPs-delivery period (phase 1) and the end-of-delivery period (phase 2).ResultsWe found that the gradual TWPs delivery treatments negatively influenced the activity of carbon cycle-related enzymes (β-glucosidase and β-D-1,4-cellobiosidase). Furthermore, the abrupt treatment highly increased the effects on nitrogen cycle-related enzyme activity (β-1,4-N-acetyl-glucosaminidase). In phase 2 (end-of-delivery period), each enzyme activity was returned to a similar level as the control group, and these changes between phases 1 and 2 depended on the prior delivery rates.ConclusionAbruptly and gradually delivered TWPs induce different responses to soil microbial activities. Our findings imply that the delivery rate of TWPs could be a key factor changing the effects of TWPs, further enhancing our understanding of the ecological impacts of TWPs.Graphical

Analysis of the Degree of Blending (DoB) of recycled asphalt mixtures with variation in mixing temperature, type, and RAP content

Presently, there is a significant increase in studies dedicated to the reuse of Reclaimed Asphalt Pavement (RAP) material obtained from the recycling of asphalt coatings. This procedure aims to reduce production costs and minimize environmental impacts. To carry out the reuse of this material, it is crucial to understand the interaction mechanisms between the aged binder of the RAP and the new binder. These mechanisms can be assessed through the Degree of Binder Activity (DoA) and the Degree of Blending (DoB). DoA represents the quantity of aged binder available for the new mixture, being an intrinsic property of each RAP. On the other hand, DoB indicates the degree of blending between the aged and new binders, being influenced by external factors such as mixing parameters, thus presenting a challenge in quantification. This study sought to establish a predictive model to quantify DoB based on Dynamic Modulus and Fourier-Transform Infrared Spectroscopy (FTIR) tests. These tests were conducted on mixtures containing four different RAPs, varying in different content levels and mixing temperatures. In each RAP, DoA was quantified, and a statistical analysis was performed to verify the significance of independent variables in dynamic modulus results. After this step, four modeling attempts were made, choosing the one that not only presented significant independent variables but also obtained the highest predicted R² value. The chosen model was validated and demonstrated small variations compared to DoB obtained by the generated equation. Thus, it is concluded that the DoB modeling conducted in this study highlights that mixing temperatures and RAP content influence factors such as DoA, dynamic modulus, FTIR, and consequently, DoB. Additionally, the practicality of the model was emphasized, as it successfully predicted DoB using a single mechanical and chemical test.

Study on the Long-Term Salt Release Characteristics of Self-Melting Ice Asphalt Mixtures and Their Impact on Pavement Performance

Self-melting ice asphalt pavement materials inhibit pavement freezing and improve driving safety. This paper aims to study the long-term salt release characteristics of self-melting ice asphalt mixtures and the impact on pavement after complete salt release. Firstly, a method to accelerate the rapid release of salt based on the Los Angeles abrasion tester. Then, long-term salt release patterns were elucidated under the influence of deicing agent dosage, type of asphalt, and type of gradation. Finally, a quantitative analysis of the pavement performance after complete salt release is conducted. The results indicate that the release efficiency of the Los Angeles abrasion tester method has increased by 91 times compared to the magnetic stirrer immersion flushing method and by 114 times compared to the natural soaking method. The SBS-modified self-melting ice asphalt mixture possesses a longer duration of salt release, but the uniformity of salt release is inferior. Salt release duration is directly proportional to the dosage of deicing agents. SMA-13 self-melting ice asphalt mixture exhibits poorer uniformity in salt release. After complete salt release, high-temperature stability of self-melting ice asphalt mixtures decreased by 31.6%, low-temperature performance decreased by 15.4%, water stability decreased by 26.7%, and fatigue life decreased by 35.9%.

An Overview of Phase Change Materials and Their Applications in Pavement

The composite of a phase change material (PCM) and bitumen or asphalt as a matrix is expected as a new, advanced material for road construction. The main motivation for this article was to show the new possibilities and perspectives of developing the pavement with the usage of PCMs. Incorporating PCMs into paving materials can improve their properties, including allowing the regulation of the pavement temperature, enhancement of the pavement durability, and avoiding the phenomenon of a heat-island on the road. The main purpose of this article was to evaluate contemporary investigations in the area of the application of PCMs in pavement materials, especially asphalt and bitumen; to summarize the advantages and disadvantages of the implementation of PCM for road construction; and to discuss further trends in this area. This manuscript explored the state of the art in this area based on research in the literature. It shows the possible material solutions, presenting their composition and discussing their key properties and the manufacturing technologies used. The possibilities for further implementations are considered, especially economic issues.

TEDNet: Twin Encoder Decoder Neural Network for 2D Camera and LiDAR Road Detection

Abstract Robust road surface estimation is required for autonomous ground vehicles to navigate safely. Despite it becoming one of the main targets for autonomous mobility researchers in recent years, it is still an open problem in which cameras and LiDAR sensors have demonstrated to be adequate to predict the position, size and shape of the road a vehicle is driving on in different environments. In this work, a novel Convolutional Neural Network model is proposed for the accurate estimation of the roadway surface. Furthermore, an ablation study has been conducted to investigate how different encoding strategies affect model performance, testing 6 slightly different neural network architectures. Our model is based on the use of a Twin Encoder–Decoder Neural Network (TEDNet) for independent camera and LiDAR feature extraction and has been trained and evaluated on the Kitti–Road dataset. Bird’s Eye View projections of the camera and LiDAR data are used in this model to perform semantic segmentation on whether each pixel belongs to the road surface. The proposed method performs among other state-of-the-art methods and operates at the same frame rate as the LiDAR and cameras, so it is adequate for its use in real-time applications.

Enhancing Tire Condition Monitoring through Weightless Neural Networks Using MEMS-Based Vibration Signals

Tire pressure monitoring system (TPMS) has a critical role in safeguarding vehicle safety by monitoring tire pressure levels. Keeping the accurate tire pressure is necessary for confirming comfortable driving and safety, and improving fuel consumption. Tire problems can result from various factors, such as road surface conditions, weather changes, and driving activities, emphasizing the importance of systematic tire checks. This study presents a novel method for tire condition monitoring using weightless neural networks (WNN), which mimic neural processes using random-access memory (RAM) components, supporting fast and precise training. Wilkes, Stonham, and Aleksander Recognition Device (WiSARD), a type of WNN, stands out for its capability in classification and pattern recognition, gaining from its ability to avoid repetitive training and residual formation. For vibration data acquisition from tires, cost-effective micro-electro-mechanical system (MEMS) sensors are employed, offering a more economical solution than piezoelectric sensors. This approach yields a variety of features, such as autoregressive moving average (ARMA), statistical and histogram features. The J48 decision tree algorithm plays a critical role in selecting essential features for classification, which are subsequently divided into training and testing sets, crucial for assessing the WiSARD classifier’s efficacy. Hyperparameter optimization of the WNN leads to improved classification accuracy and shorter computation times. In practical tests, the WiSARD classifier, when optimally configured, achieved an impressive 97.92% accuracy with histogram features in only 0.008 seconds, showcasing the capability of WNN to enhance tire technology and the accuracy and efficiency of tire monitoring and maintenance.

Study on the influencing factors of mechanical properties of anti-icing modified asphalt mortar

Accumulated snow and ice on road surface affect traffic operation. Anti-icing asphalt pavement is widely implemented as an active snow melting technology. However, there are few studies on anti-icing modified asphalt mortar mechanical properties and the effect of mortar materials on the interaction between modified asphalt and anti-icing filler. For this reason, this study investigated the mechanical properties of asphalt including rheological, high-temperature, fatigue and low-temperature properties through frequency and temperature scan, multiple stress creep recovery, linear amplitude scan, and single edge notched beam test. Based on this, the significant factors affecting asphalt mechanical properties were identified by analysis of variance. Subsequently, the Palierne-C-G* parameter was used to quantify the interaction between modified asphalt and anti-icing filler, and the effect of asphalt chemical structure on the interaction was revealed by Fourier Transform Infrared Spectroscopy (FTIR) test. The results indicated that the anti-icing filler significantly improved the high temperature and fatigue resistance of asphalt. Compared with SBS modified asphalt mortar, the mechanical properties of composite modified asphalt mortar were superior. The most significant factor affecting the mechanical properties of SBS modified asphalt mortar was modifier dosage, while the most significant factor affecting the composite modified asphalt mortar was crushed rubber dosage. The interaction showed a decreasing trend with the increase of frequency, while it appeared to increase and then decrease with the increase of temperature. The carbonyl functional group of asphalt correlated best with the interaction between asphalt and filler.

Elevation Accuracy of Forest Road Maps Derived from Aerial Imaging, Airborne Laser Scanning and Mobile Laser Scanning Data

Forest road maps are a fundamental source of information for the sustainable management, protection, and public utilization of forests. However, the precision of these maps is crucial to their use. In this context, we assessed and compared the elevation accuracy of terrain on three forest road surfaces (i.e., asphalt, concrete, and stone), which were derived based on data from three remote sensing technologies (i.e., aerial imaging, airborne laser scanning, and mobile laser scanning) using five geospatial techniques (i.e., inverse distance; natural neighbor; and conversion by average, maximal, and minimal elevation value). Specifically, the elevation accuracy was assessed based on 700 points at which elevation was measured in the field, and these elevations were extracted from fifteen derived forest road maps with a resolution of 0.5 m. The highest precision was found on asphalt roads derived from mobile laser scanning data (RMSE from ±0.01 m to ±0.04 m) and airborne laser scanning data (RMSE from ±0.03 m to ±0.04 m). On the other hand, the lowest precision was found on all roads derived from aerial imaging data (RMSE from ±0.11 m to ±0.23 m). Furthermore, we found significant differences in elevation between the measured and derived terrains. However, the differences in elevation between specific techniques, such as inverse distance, natural neighbor, and conversion by average, were mostly random. Moreover, we found that airborne and mobile laser scanning technologies provided terrain on concrete and stone roads with random elevation differences. In these cases, it is possible to replace a specific technique or technology with one that is similar without significantly decreasing the elevation accuracy (α = 0.05).