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Готові списки джерел за темами / Roughness prediction

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Добірка наукової літератури з теми "Roughness prediction"

Автор: Grafiati

Опубліковано: 4 листопада 2023

Оновлено: 31 липня 2025

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Статті в журналах з теми "Roughness prediction"

1

Saleh, A., D. W. Fryrear, and J. D. Bilbro. "AERODYNAMIC ROUGHNESS PREDICTION FROM SOIL SURFACE ROUGHNESS MEASUREMENT." Soil Science 162, no. 3 (1997): 205–10. http://dx.doi.org/10.1097/00010694-199703000-00006.

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2

Cai, Xiao Jiang, Z. Q. Liu, Q. C. Wang, Shu Han, Qing Long An, and Ming Chen. "Surface Roughness Prediction in Turning of Free Machining Steel 1215 by Artificial Neural Network." Advanced Materials Research 188 (March 2011): 535–41. http://dx.doi.org/10.4028/www.scientific.net/amr.188.535.

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Анотація:

Surface roughness is a significant aspect of the surface integrity concept. It is efficient to predict the surface roughness in advance by a prediction model. In this study, artificial neural network is used to model the surface roughness in turning of free machining steel 1215. The inputs considered in the prediction ANN model were cutting speed, feed rate and depth of cut, and the output was Ra. Several feed-forward neural networks with different architectures were compared in terms of prediction accuracy, and then the best prediction model, a 3-4-1-1 ANN was capable of predicting Ra with a

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3

Alajmi, Mahdi S., and Abdullah M. Almeshal. "Prediction and Optimization of Surface Roughness in a Turning Process Using the ANFIS-QPSO Method." Materials 13, no. 13 (2020): 2986. http://dx.doi.org/10.3390/ma13132986.

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This study presents a prediction method of surface roughness values for dry and cryogenic turning of AISI 304 stainless steel using the ANFIS-QPSO machine learning approach. ANFIS-QPSO combines the strengths of artificial neural networks, fuzzy systems and evolutionary optimization in terms of accuracy, robustness and fast convergence towards global optima. Simulations revealed that ANFIS-QPSO results in accurate prediction of surface roughness with RMSE = 4.86%, MAPE = 4.95% and R2 = 0.984 for the dry turning process. Similarly, for the cryogenic turning process, ANFIS-QPSO resulted in surfac

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4

Nalbant, Muammer, Hasan Gokkaya, and İhsan Toktaş. "Comparison of Regression and Artificial Neural Network Models for Surface Roughness Prediction with the Cutting Parameters in CNC Turning." Modelling and Simulation in Engineering 2007 (2007): 1–14. http://dx.doi.org/10.1155/2007/92717.

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Surface roughness, an indicator of surface quality, is one of the most specified customer requirements in machining of parts. In this study, the experimental results corresponding to the effects of different insert nose radii of cutting tools (0.4, 0.8, 1.2 mm), various depth of cuts (0.75, 1.25, 1.75, 2.25, 2.75 mm), and different feedrates (100, 130, 160, 190, 220 mm/min) on the surface quality of the AISI 1030 steel workpieces have been investigated using multiple regression analysis and artificial neural networks (ANN). Regression analysis and neural network-based models used for the predi

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5

Zhang, Qi, Yuechao Pei, Yixin Shen, Xiaojun Wang, Jingqi Lai, and Maohui Wang. "A New Perspective on Predicting Roughness of Discontinuity from Fractal Dimension D of Outcrops." Fractal and Fractional 7, no. 7 (2023): 496. http://dx.doi.org/10.3390/fractalfract7070496.

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In tunnel construction, predicting the roughness of discontinuity is significant for preventing the collapse of the excavation face. However, currently, we are unable to use a parameter with invariant properties to quantify and predict the roughness of discontinuity. Fractal dimension D is one such parameter that be used to characterize the roughness of discontinuity. The study proposes a new method to predict the roughness of discontinuity from the fractal dimension D of outcrops. The measurement method of the coordinates of outcrops is firstly summarized, and the most suitable method of calc

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6

Zeng, Shi, and Dechang Pi. "Milling Surface Roughness Prediction Based on Physics-Informed Machine Learning." Sensors 23, no. 10 (2023): 4969. http://dx.doi.org/10.3390/s23104969.

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Анотація:

Surface roughness is a key indicator of the quality of mechanical products, which can precisely portray the fatigue strength, wear resistance, surface hardness and other properties of the products. The convergence of current machine-learning-based surface roughness prediction methods to local minima may lead to poor model generalization or results that violate existing physical laws. Therefore, this paper combined physical knowledge with deep learning to propose a physics-informed deep learning method (PIDL) for milling surface roughness predictions under the constraints of physical laws. This

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7

Alam, S., A. K. M. Nurul Amin, Anayet Ullah Patwari, and Mohamed Konneh. "Prediction and Investigation of Surface Response in High Speed End Milling of Ti-6Al-4V and Optimization by Genetic Algorithm." Advanced Materials Research 83-86 (December 2009): 1009–15. http://dx.doi.org/10.4028/www.scientific.net/amr.83-86.1009.

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Анотація:

In this study, statistical models were developed using the capabilities of Response Surface Methodology (RSM) to predict the surface roughness in high-speed flat end milling of Ti-6Al-4V under dry cutting conditions. Machining was performed on a five-axis NC milling machine with a high speed attachment, using spindle speed, feed rate, and depth of cut as machining variables. The adequacy of the model was tested at 95% confidence interval. Meanwhile, a time trend was observed in residual values between model predictions and experimental data, reflecting little deviations in surface roughness pr

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8

Ng, J. J., Z. W. Zhong, and T. I. Liu. "Prediction of Roughness Heights of Milled Surfaces for Product Quality Prediction and Tool Condition Monitoring." Journal of Materials and Applications 8, no. 2 (2019): 97–104. http://dx.doi.org/10.32732/jma.2019.8.2.97.

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Анотація:

The objective of this research is to predict the roughness heights of milled surfaces, which indicates product quality and tool conditions. Two experiments are carried out to evaluate relevant factors such as vibration, force, and surface roughness. The purpose of the first experiment is to find out the limits of the machining variables compared to the constraints of the materials. The purpose of the second experiment is to identify, collect, and compare how each factor affects product quality and tool conditions. Based on this study, the vibration, force, and surface roughness are good indica

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9

Sun, Hao, Chaochao Zhang, Yikai Li, Tingting Yin, Hanming Zhang, and Jin Pu. "Study on prediction model of surface roughness of SiCp/Al composites based on Neural Network." Journal of Physics: Conference Series 2174, no. 1 (2022): 012091. http://dx.doi.org/10.1088/1742-6596/2174/1/012091.

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Abstract In order to effectively meet the actual industrial production standards and improve the prediction accuracy of composite surface roughness, a prediction model of SiCp/Al composite surface roughness based on neural network is proposed. The influence parameters of surface roughness of SiCp/Al composites are analyzed from the cutting tool parameters, and the mathematical calculation of surface roughness of SiCp/Al composites is carried out. Using neural network technology, by determining various parameters of neural network, collecting and processing various data of material surface, the

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10

Mirifar, Siamak, Mohammadali Kadivar, and Bahman Azarhoushang. "First Steps through Intelligent Grinding Using Machine Learning via Integrated Acoustic Emission Sensors." Journal of Manufacturing and Materials Processing 4, no. 2 (2020): 35. http://dx.doi.org/10.3390/jmmp4020035.

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The surface roughness of the ground parts is an essential factor in the assessment of the grinding process, and a crucial criterion in choosing the dressing and grinding tools and parameters. Additionally, the surface roughness directly influences the functionality of the workpiece. The application of artificial intelligence in the prediction of complex results of machining processes, such as surface roughness and cutting forces has increasingly become popular. This paper deals with the design of the appropriate artificial neural network for the prediction of the ground surface roughness and g

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Більше джерел

Дисертації з теми "Roughness prediction"

1

Munoz-Escalona, Patricia. "Surface roughness prediction when milling with square inserts." Thesis, University of Bath, 2010. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.519033.

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2

Shauche, Vishwesh. "Health Assessment based In-process Surface Roughness Prediction System." University of Cincinnati / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1298323430.

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3

Staheli, Kimberlie. "Jacking Force Prediction: An Interface Friction Approach based on Pipe Surface Roughness." Diss., Available online, Georgia Institute of Technology, 2006, 2006. http://etd.gatech.edu/theses/available/etd-07052006-203035/.

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Thesis (Ph. D.)--Civil and Environmental Engineering, Georgia Institute of Technology, 2007.<br>Dr. J. David Frost, Committee Chair ; Dr. G. Wayne Clough, Committee Co-Chair ; Dr. William F. Marcuson III, Committee Member ; Dr. Paul W. Mayne, Committee Member ; Dr. Susan Burns, Committee Member.

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4

Yamaguchi, Keiko. "Improved ice accretion prediction techniques based on experimental observations of surface roughness effects on heat transfer." Thesis, Massachusetts Institute of Technology, 1990. http://hdl.handle.net/1721.1/14148.

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5

Sakthi, Gireesh. "WIND POWER PREDICTION MODEL BASED ON PUBLICLY AVAILABLE DATA: SENSITIVITY ANALYSIS ON ROUGHNESS AND PRODUCTION TREND." Thesis, Uppsala universitet, Institutionen för geovetenskaper, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-400462.

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The wind power prediction plays a vital role in a wind power project both during the planning and operational phase of a project. A time series based wind power prediction model is introduced and the simulations are run for different case studies. The prediction model works based on the input from 1) nearby representative wind measuring station 2) Global average wind speed value from Meteorological Institute Uppsala University mesoscale model (MIUU) 3) Power curve of the wind turbine. The measured wind data is normalized to minimize the variation in the wind speed and multiplied with the MIUU

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6

Srinivasan, Sriram. "Development of a Cost Oriented Grinding Strategy and Prediction of Post Grind Roughness using Improved Grinder Models." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/78298.

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Irregularities in pavement profiles that exceed standard thresholds are usually rectified using a Diamond Grinding Process. Diamond Grinding is a method of Concrete Pavement Rehabilitation that involves the use of grinding wheels mounted on a machine that scraps off the top surface of the pavement to smooth irregularities. Profile Analysis Software like ProVAL© offers simulation modules that allow users to investigate various grinding strategies and prepare a corrective action plan for the pavement. The major drawback with the current Smoothness Assurance Module© (SAM) in ProVAL© is that it pr

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7

Celik, Kazim Arda. "Development Of A Methodology For Prediction Of Surface Roughness Of Curved Cavities Manufactured By 5-axes Cnc Milling." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608368/index.pdf.

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The surface quality is identified by surface roughness parameters. The average surface roughness (Ra) is used in this study, as it is the most commonly used roughness parameter in the industry. A particular curved cavity of a forging die is selected for the experimental study. Different milling methods are tested. The comparison studies are conducted between 3-axes and 5-axes milling, linear and circular tool path strategies and down and up milling. According to the experimental study, appropriate method is determined for the milling of a particular curved cavity of a forging die. The experime

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8

Cummings, Patrick. "Modeling the Locked-Wheel Skid Tester to Determine the Effect of Pavement Roughness on the International Friction Index." Scholar Commons, 2010. https://scholarcommons.usf.edu/etd/1604.

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Анотація:

Pavement roughness has been found to have an effect on the coefficient of friction measured with the Locked-Wheel Skid Tester (LWT) with measured friction decreasing as the long wave roughness of the pavement increases. However, the current pavement friction standardization model adopted by the American Society for Testing and Materials (ASTM), to compute the International Friction Index (IFI), does not account for this effect. In other words, it had been previously assumed that the IFI's speed constant (SP), which defines the gradient of the pavement friction versus speed relationship, is an

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9

Mangin, Steven F. "Development of an Equation Independent of Manning's Coefficient n for Depth Prediction in Partially-Filled Circular Culverts." Youngstown State University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1284488143.

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10

Levin, Ori. "Stability analysis and transition prediction of wall-bounded flows." Licentiate thesis, KTH, Mechanics, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-1663.

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<p>Disturbances introduced in wall-bounded .ows can grow andlead to transition from laminar to turbulent .ow. In order toreduce losses or enhance mixing in energy systems, afundamental understanding of the .ow stability is important. Inlow disturbance environments, the typical path to transition isan exponential growth of modal waves. On the other hand, inlarge disturbance environments, such as in the presence of highlevels of free-stream turbulence or surface roughness,algebraic growth of non-modal streaks can lead to transition.In the present work, the stability of wall-bounded .ows isinvest

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Книги з теми "Roughness prediction"

1

Fox, Christopher Gene. Description, analysis and predictions of sea floor roughness using spectral models. Naval Oceanographic Office, 1985.

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2

Kurlanda, Marian Henryk. Predicting roughness progression of asphalt overlays: Joint C-SHRP/Alberta Bayesian application. Canadian Strategic Highway Research Program, Transportation Association of Canada, 1995.

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3

Chan, Johnny C. L. Physical Mechanisms Responsible for Track Changes and Rainfall Distributions Associated with Tropical Cyclone Landfall. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190676889.013.16.

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As a tropical cyclone approaches land, its interaction with the characteristics of the land (surface roughness, topography, moisture availability, etc.) will lead to changes in its track as well as the rainfall and wind distributions near its landfall location. Accurate predictions of such changes are important in issuing warnings and disaster preparedness. In this chapter, the basic physical mechanisms that cause changes in the track and rainfall distributions when a tropical cyclone is about to make landfall are presented. These mechanisms are derived based on studies from both observations

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4

Chan, Johnny C. L. Physical Mechanisms Responsible for Track Changes and Rainfall Distributions Associated with Tropical Cyclone Landfall. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190699420.013.16.

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Анотація:

As a tropical cyclone approaches land, its interaction with the characteristics of the land (surface roughness, topography, moisture availability, etc.) will lead to changes in its track as well as the rainfall and wind distributions near its landfall location. Accurate predictions of such changes are important in issuing warnings and disaster preparedness. In this chapter, the basic physical mechanisms that cause changes in the track and rainfall distributions when a tropical cyclone is about to make landfall are presented. These mechanisms are derived based on studies from both observations

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5

McAdams, Stephen, and Bruno L. Giordano. The perception of musical timbre. Edited by Susan Hallam, Ian Cross, and Michael Thaut. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199298457.013.0007.

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This article discusses musical-timbre perception. Musical timbre is a combination of continuous perceptual dimensions and discrete features to which listeners are differentially sensitive. The continuous dimensions often have quantifiable acoustic correlates. The timbre-space representation is a powerful psychological model that allows predictions to be made about timbre perception in situations beyond those used to derive the model in the first place. Timbre can play a role in larger-scale movements of tension and relaxation and thus contribute to the expression inherent in musical form. Unde

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Частини книг з теми "Roughness prediction"

1

Trung, Do Duc, Nhu Tung Nguyen, Hoang Tien Dung, et al. "A Study on Prediction of Grinding Surface Roughness." In Advances in Engineering Research and Application. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64719-3_13.

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2

Sreekantan, P. G., and G. V. Ramana. "Roughness based prediction of geofoam interfaces with concrete." In Geosynthetics: Leading the Way to a Resilient Planet. CRC Press, 2023. http://dx.doi.org/10.1201/9781003386889-61.

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3

Vagdatli, T., and K. Petroutsatou. "A dynamic Bayesian network for pavement roughness prediction." In Bituminous Mixtures and Pavements VIII. CRC Press, 2024. http://dx.doi.org/10.1201/9781003402541-106.

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4

Yan, Tingxu, Huiping Zhu, Xudong Liu, et al. "Wetting Behavior of LBE on 316L and T91 Surfaces with Different Roughness." In Springer Proceedings in Physics. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1023-6_41.

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AbstractIn this paper, two typical candidate structural materials of 316L and T91 with different surface roughnesses were studied at temperatures from 200–500 ℃. The surface with different roughness was prepared by mechanical polishing on the sandpapers with particle sizes from 400 to 2000 mesh. The wetting test was carried out in a smart contact angle measuring device by using the sessile-drop method. Meanwhile, the microstructure of the liquid-solid surface was analyzed by scanning electron microscope (SEM). The results show that the surfaces of both materials are non-wetting to LBE in the t

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5

Seehausen, Hendrik, and Joerg R. Seume. "Influence of Complex Surface Structures on the Aerodynamic Loss Behaviour of Blades." In Regeneration of Complex Capital Goods. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-51395-4_9.

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AbstractOperating airfoils under mechanical stress in combination with oxidation and corrosion, abrasion wear, and subsequent regeneration results in complex surface structures that influence the performance of aircraft engines. For predicting this impact on performance the authors propose a reduced order model capable of assessing the effect of surface roughness as a basis for making decisions before or during the regeneration process. The accuracy of this model is increased by using improved roughness sensitive transition and turbulence models created for Reynolds-Averaged Navier–Stokes simu

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6

Trung, Do Duc, Nguyen Nhu Tung, Nguyen Hong Son, et al. "Prediction of Surface Roughness in Turning with Diamond Insert." In Advances in Engineering Research and Application. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37497-6_69.

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7

Yang, Jiasheng, Alexander Stroh, and Pourya Forooghi. "Study of Data-Driven Prediction of Roughness Skin Friction." In High Performance Computing in Science and Engineering '22. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-46870-4_11.

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8

Xu, Jiakuan, Min Chang, and Junqiang Bai. "Transition Prediction Model Considering the Effects of Surface Roughness." In CFD-Compatible RANS/LES Modeling of Transitional and Separated Flows. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-6886-1_4.

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9

Chen, Ying, Yanhong Sun, Han Lin, and Bing Zhang. "Prediction Model of Milling Surface Roughness Based on Genetic Algorithms." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15235-2_179.

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10

Ibrahim, Musa Alhaji, and Yusuf Şahin. "Surface Roughness Modelling and Prediction Using Artificial Intelligence Based Models." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-35249-3_3.

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Тези доповідей конференцій з теми "Roughness prediction"

1

Sundaram, S. Meenakshi, Kassem AL-Attabi, Veena Yadav S, Abdul Lateef Haroon P.S., and Vanitha Potula. "Prediction of Pavement Roughness using a Deep Neural Network Approach." In 2024 International Conference on Integrated Intelligence and Communication Systems (ICIICS). IEEE, 2024. https://doi.org/10.1109/iciics63763.2024.10860023.

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2

Chen, Jianing. "Prediction of Asphalt Pavement Roughness Index Based on Bayesian Optimization XGBoost Algorithm." In 2025 5th International Symposium on Computer Technology and Information Science (ISCTIS). IEEE, 2025. https://doi.org/10.1109/isctis65944.2025.11065410.

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3

Wardhani, Rivai, Hendro Nurhadi, and Harus Laksana Guntur. "Prediction of Surface Roughness and Hardness on Multi 3D Printers with Machine Learning." In 2024 International Automatic Control Conference (CACS). IEEE, 2024. https://doi.org/10.1109/cacs63404.2024.10773263.

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4

Li, Beibei, Xiyue Zhang, Minnan Han, Haoxuan Luan, and Shujie Sun. "End-to-End Surface Roughness Prediction Method Driven by Multi-Source Information Fusion." In 2024 10th International Conference on Computer and Communications (ICCC). IEEE, 2024. https://doi.org/10.1109/iccc62609.2024.10941778.

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5

Wu, Dazhong, Yupeng Wei, and Janis Terpenny. "Surface Roughness Prediction in Additive Manufacturing Using Machine Learning." In ASME 2018 13th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/msec2018-6501.

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Анотація:

To realize high quality, additively manufactured parts, real-time process monitoring and advanced predictive modeling tools are crucial for accelerating quality assurance and quality control in additive manufacturing. While previous research has demonstrated the effectiveness of physics- and model-based diagnosis and prognosis for additive manufacturing, very little research has been reported on real-time monitoring and prediction of surface roughness in fused deposition modeling (FDM). This paper presents a new data-driven approach to surface roughness prediction in FDM. A real-time monitorin

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6

Rami´rez, M. de J., M. Correa, C. Rodri´guez, and J. R. Alique. "Surface Roughness Modeling Based on Surface Roughness Feature Concept for High Speed Machining." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-82256.

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This paper explains the concepts to develop a Model of Surface Roughness in order to help researchers to model predictors for high speed machining, also a concept of a surface roughness feature (RaF) is introduced. A RaF is an information piece that shows the factors used by a Ra prediction technique associate with a specific geometric feature. The surface roughness information model is a repository of the RaFs designed to focus on particular workpiece geometries. The Ra predictor developer can design the content of the Ra information model according with his Ra prediction technique to be deve

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7

"Roughness Prediction For FDM Produced Surfaces." In International Conference Recent treads in Engineering & Technology. International Institute of Engineers, 2014. http://dx.doi.org/10.15242/iie.e0214527.

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8

Zhang, Dingtong, and Ning Ding. "Surface Roughness Intelligent Prediction on Grinding." In 3rd International Conference on Material, Mechanical and Manufacturing Engineering (IC3ME 2015). Atlantis Press, 2015. http://dx.doi.org/10.2991/ic3me-15.2015.415.

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9

Hanson, David, and Michael Kinzel. "An Improved CFD Approach for Ice-Accretion Prediction Using the Discrete Element Roughness Method." In ASME 2017 Fluids Engineering Division Summer Meeting. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/fedsm2017-69365.

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Ice-shape prediction results are shown wherein Discrete-Element Roughness Method (DERM)-based CFD solutions are coupled with LEWICE to supplement the built-in heat transfer prediction module. This coupling produces multi-step ice-shape predictions. The effect of using the newer roughness-height distribution model of Han and Palacios rather than the roughness-height prediction of LEWICE is also gauged. DERM is used in an attempt to improve heat transfer predictions beyond the capability of a sand-grain-roughness model while only slightly increasing the computation time. LEWICE is the industry-s

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Agarwal, Sanjay, and P. Venkateswara Rao. "Surface Roughness Prediction Model for Ceramic Grinding." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-79180.

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Surface quality of workpiece during ceramic grinding is an ever-increasing concern in industries now-a-days. Every industry cares to produce products with supposedly better surface finish. The importance of the surface finish of a product depends upon its functional requirements. Since surface finish is governed by many factors and its experimental determination is laborious and time consuming. So the establishment of a model for the reliable prediction of surface roughness is still a key issue for ceramic grinding. In this study, a new analytical surface roughness model is developed on the ba

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Звіти організацій з теми "Roughness prediction"

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Taylor, R. P., and B. K. Hodge. Validated heat-transfer and pressure-drop prediction methods based on the discrete element method: Phase 1, Three-dimensiional roughness. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/10154300.

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Taylor, R. P., and B. K. Hodge. Validated heat-transfer and pressure-drop prediction methods based on the discrete element method: Phase 1, Three-dimensiional roughness. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/5096745.

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James, C. A., B. K. Hodge, and R. P. Taylor. Validated heat-transfer and pressure-drop prediction methods based on the discrete-element method: Phase 2, two-dimensional rib roughness. Office of Scientific and Technical Information (OSTI), 1993. http://dx.doi.org/10.2172/10192770.

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Thegeya, Aaron, Thomas Mitterling, Arturo Martinez Jr, Joseph Albert Niño Bulan, Ron Lester Durante, and Jayzon Mag-atas. Application of Machine Learning Algorithms on Satellite Imagery for Road Quality Monitoring: An Alternative Approach to Road Quality Surveys. Asian Development Bank, 2022. http://dx.doi.org/10.22617/wps220587-2.

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This paper examines the feasibility of using satellite imagery and artificial intelligence to develop an efficient and cost-effective way to determine and predict the condition of roads in the Asia and Pacific region. The paper notes that collecting information on road quality is difficult, particularly in harder to reach middle- and low-income areas, and explains why this method offers an alternative. It shows how the study’s preliminary algorithm was created using satellite imagery and existing road roughness data from the Philippines. It assesses the accuracy rate and finds it sufficient fo

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Al-Qadi, Imad, Jaime Hernandez, Angeli Jayme, et al. The Impact of Wide-Base Tires on Pavement—A National Study. Illinois Center for Transportation, 2021. http://dx.doi.org/10.36501/0197-9191/21-035.

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Researchers have been studying wide-base tires for over two decades, but no evidence has been provided regarding the net benefit of this tire technology. In this study, a comprehensive approach is used to compare new-generation wide-base tires (NG-WBT) with the dual-tire assembly (DTA). Numerical modeling, prediction methods, experimental measurements, and environmental impact assessment were combined to provide recommendations about the use of NG-WBT. A finite element approach, considering variables usually omitted in the conventional analysis of flexible pavement was utilized for modeling. F

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Michaels, Michelle, Theodore Letcher, Sandra LeGrand, Nicholas Webb, and Justin Putnam. Implementation of an albedo-based drag partition into the WRF-Chem v4.1 AFWA dust emission module. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/42782.

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Employing numerical prediction models can be a powerful tool for forecasting air quality and visibility hazards related to dust events. However, these numerical models are sensitive to surface conditions. Roughness features (e.g., rocks, vegetation, furrows, etc.) that shelter or attenuate wind flow over the soil surface affect the magnitude and spatial distribution of dust emission. To aide in simulating the emission phase of dust transport, we used a previously published albedo-based drag partition parameterization to better represent the component of wind friction speed affecting the immedi

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LeGrand, Sandra, Theodore Letcher, Gregory Okin, et al. Application of a satellite-retrieved sheltering parameterization (v1.0) for dust event simulation with WRF-Chem v4.1. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/47116.

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Employing numerical prediction models can be a powerful tool for forecasting air quality and visibility hazards related to dust events. However, these numerical models are sensitive to surface conditions. Roughness features (e.g., rocks, vegetation, furrows, etc.) that shelter or attenuate wind flow over the soil surface affect the magnitude and spatial distribution of dust emission. To aide in simulating the emission phase of dust transport, we used a previously published albedo-based drag partition parameterization to better represent the component of wind friction speed affecting the immedi

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Ziegler, Nancy, Nicholas Webb, Adrian Chappell, and Sandra LeGrand. Scale invariance of albedo-based wind friction velocity. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40499.

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Obtaining reliable estimates of aerodynamic roughness is necessary to interpret and accurately predict aeolian sediment transport dynamics. However, inherent uncertainties in field measurements and models of surface aerodynamic properties continue to undermine aeolian research, monitoring, and dust modeling. A new relation between aerodynamic shelter and land surface shadow has been established at the wind tunnel scale, enabling the potential for estimates of wind erosion and dust emission to be obtained across scales from albedo data. Here, we compare estimates of wind friction velocity (u*)

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Ziegler, Nancy, Nicholas Webb, John Gillies, et al. Plant phenology drives seasonal changes in shear stress partitioning in a semi-arid rangeland. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/47680.

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Accurate representation of surface roughness in predictive models of aeolian sediment transport and dust emission is required for model accuracy. While past studies have examined roughness effects on drag partitioning, the spatial and temporal variability of surface shear velocity and the shear stress ratio remain poorly described. Here, we use a four-month dataset of total shear velocity (u*) and soil surface shear velocity (us*) measurements to examine the spatiotemporal variability of the shear stress ratio (R) before, during, and after vegetation green-up at a honey mesquite (Prosopis glan

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Agassi, Menahem, Michael J. Singer, Eyal Ben-Dor, et al. Developing Remote Sensing Based-Techniques for the Evaluation of Soil Infiltration Rate and Surface Roughness. United States Department of Agriculture, 2001. http://dx.doi.org/10.32747/2001.7586479.bard.

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The objective of this one-year project was to show whether a significant correlation can be established between the decreasing infiltration rate of the soil, during simulated rainstorm, and a following increase in the reflectance of the crusting soil. The project was supposed to be conducted under laboratory conditions, using at least three types of soils from each country. The general goal of this work was to develop a method for measuring the soil infiltration rate in-situ, solely from the reflectance readings, using a spectrometer. Loss of rain and irrigation water from cultivated fields is

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