Analysis of Asphalt Mixtures Modified with Steel Slag Surface Texture Using 3D Scanning Technology
Abstract
:1. Introduction
2. Materials and Methods
2.1. Steel Slag
2.2. Asphalt
2.3. Three-Dimensional Scanner
2.4. Gradation Design
2.5. Methods
2.5.1. The Performance Evaluation of Asphalt Mixture
2.5.2. BPN Tests and MTD Tests in the Laboratory
2.5.3. BPN Tests and MTD Tests on Pavement
2.5.4. Three-Dimensional Scanning Tests
3. Results
3.1. The Rutting Test
- Steel slag itself is a porous structure, and steel slag has strong alkalinity, which can adsorb more asphalt and enhance the high-temperature deformation resistance of the mixture;
- After production, most steel slag has a large particle size and needs to be broken down. Moreover, the surface of broken steel slag is not smooth, and the rough texture structure can also absorb more asphalt and improve the anti-rutting deformation ability;
- The needle and flake content of steel slag is higher than that of general aggregate, which indicates that steel slag has rich angularity, has close interlocking ability between aggregates and can also adsorb more asphalt.
3.2. The Water Immersion Marshall Stability
3.3. The Freeze–Thaw Splitting Test
3.4. Swelling Characteristic Test
4. Discussion
4.1. Surface Mean Texture Depth (MTD)
4.2. Friction Coefficient (BPN Value)
- Due to the excellent bonding ability between steel slag and asphalt and its good wear resistance, it is of great significance to apply it to areas with more rain, highways with steep slopes and expressways;
- Due to the better combination characteristics of steel slag and asphalt, this mixture can improve the water damage and skid resistance of expressway pavements, prolong the service life of the pavement and reduce the cost and time spent on renovation.
4.3. Three-Dimensional Texture Scanning of Pavement
4.3.1. Comparison of Surface Image and Scanned Image
- Van der Waals forces influence the skid resistance of the pavement, adhesion forces, the elastic deformation of tyres and micro-cutting forces acting on micro convex bodies [50]. Steel slag–asphalt mixes have more bumps, which are prone to stress concentration and more pronounced micro-cutting action, which provides friction. It is known that the hindrance force accounts for about 10% of the tyre–road friction [51], and the cutting effect of micro-convexity accounts for about 90% of the overall friction [52,53].
- Vehicles traveling on highways at speeds greater than 60 km/h are greatly influenced by the thickness of the water film, and a water film of about 0.025 mm can reduce the friction factor by roughly 20% to 30%, a situation that occurs when rainfall is greater than 0.25 mm [54], which can cause the water drifting phenomenon to occur.
- The use of industrial wastes on roads greatly reduces the exploitation of scarce resources, reduces the cost of materials and meets the national requirements for environmental protection, enabling the conversion of waste into valuable materials.
4.3.2. Three-Dimensional Texture Analysis
5. Conclusions
- The free CaO content of the steel slag used in this study meets national standards and can be used for road surfaces. The performance enhancement in the mix by steel slag was superior to that of the conventional basalt–asphalt mix, with a reduction in friction value decay from 5.7% to 4% before and after high-temperature tests with steel slag replacing the conventional basalt aggregate, significantly improving the skid resistance of the pavement.
- The steel slag asphalt mixture has better performance than the basalt mixture in all properties, with the former having 14% higher dynamic stability than the latter; the former having 27% higher residual stability than the latter; the former having 10.5% higher freeze–thaw resistance than the latter; and the former having 0.16% higher swelling characteristics than the latter, with almost no effect.
- In addition to comparing the texture values of the two mixes, the test results show that the texture depths of the two mixes are basically the same, and both textures show a tendency to protrude, although the steel slag–asphalt mix pavement has a sharper and rougher surface texture than the basalt–asphalt mix, with better skid resistance, and the maximum height Sz value can reflect the thickness of the pavement structure layer and verify whether the pavement matches the paving requirements.
- This paper combines 3D scanning technology to investigate the initial skid resistance of the pavement from the analysis of the macro road performance of the mixture to the analysis of the microtexture data. In the early stages of pavement formation, there is a good correlation between pavement texture parameters and BPN values.
- The swelling characteristics of steel slag are controlled to ensure that it does not crack when used in roads, and the use of the steel slag asphalt mixture for paving and pavement repair has high economic benefits and will also gradually reduce the demand for natural aggregate extraction. Analysing the textural structure of the surface of steel slag asphalt mixes and establishing the relationship between texture and skid resistance are a guide to the use of steel slag on pavements. With the long-term use of the experimental pavement, the collection of texture values at the same locations will be considered in future tests to investigate the skid decay of the experimental pavement and to establish a skid decay model for both mixtures.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Feng, J.; Yi, F.; Tong, Y.; Le, W.; Cheng, W. Research status of comprehensive utilization of steel slag. China Nonferr. Metall. 2021, 50, 77–82. [Google Scholar]
- Research and Markets. Global Steel Markets to 2026—Hit by COVID-19 Outbreak, Steel Industry & Demand Speedily Returning to Normalcy. 2021. Available online: https://www.globenewswire.com/news-release/2021/10/06/2309315/0/en/Global-Steel-Markets-to-2026-Hit-by-COVID-19-Outbreak-Steel-Industry-Demand-Speedily-Returning-to-Normalcy.html (accessed on 25 October 2022).
- Xue, Y.; Wu, S.; Chen, X. Application of steel slag in asphalt pavement engineering. Foreign Build. Mater. Sci. Technol. 2005, 26, 1–3. [Google Scholar]
- Guo, J.; Bao, Y.; Wang, M. Steel slag in China: Treatment, recycling, and management. Waste Manag. 2018, 78, 318–330. [Google Scholar] [CrossRef] [PubMed]
- Le, K.; Hui, L.D.; Hao, Z.; Wan, L.M. Systematic research on the application of steel slag resources under the background of big data. Complexity 2018, 2018, 6703908. [Google Scholar]
- Ma, L.; Xu, D.; Wang, S.; Gu, X. Expansion inhibition of steel slag in asphalt mixture by a surface water isolation structure. Road Mater. Pavement Des. 2019, 21, 2215–2229. [Google Scholar] [CrossRef]
- Du, S. Influence of chemical additives on mixing procedures and performance of asphalt emulsion recycled mixture with reclaimed cement-stabilized macadam. Constr. Build. Mater. 2016, 118, 146–154. [Google Scholar] [CrossRef]
- Li, C.; Xiang, X.; Zhou, X. Research on steel slag open-graded permeable asphalt mixture and its performance. J. Build. Mater. 2015, 18, 168–171. [Google Scholar]
- Collins, R.j.; Ciesielski, S.K. Recycling and Use of Waste Materials and By-Products in Highway Construction; Transportation Research Board: Washington, DC, USA, 1994. [Google Scholar]
- Ahmedzade, P.; Sengoz, B. Evaluation of steel slag coarse aggregate in hot mix asphalt concrete. J. Hazard. Mater. 2009, 165, 300–305. [Google Scholar] [CrossRef]
- Liu, J.; Wang, Z.; Guo, H.; Yan, F. Thermal transfer characteristics of asphalt mixtures containing hot poured steel slag through microwave heating. J. Clean. Prod. 2021, 308, 127225. [Google Scholar] [CrossRef]
- Fisher, L.; Barron, A. The recycling and reuse of steelmaking slags—A review. Resour. Conserv. Recycl. 2019, 146, 244–255. [Google Scholar] [CrossRef]
- Li, C.; Chen, Z.; Wu, S.; Li, B.; Xie, J.; Xiao, Y. Effects of steel slag fillers on the rheological properties of asphalt mastic. Constr. Build. Mater. 2017, 145, 383–391. [Google Scholar] [CrossRef]
- Wei, M.; Wu, S.; Xu, H.; Li, H.; Yang, C. Characterization of Steel Slag Filler and Its Effect on Aging Resistance of Asphalt Mastic with Various Aging Methods. Materials 2021, 14, 869. [Google Scholar] [CrossRef] [PubMed]
- Qazizadeh, M.J.; Farhad, H.; Kavussi, A.; Sadeghi, A. Evaluating the Fatigue Behavior of Asphalt Mixtures Containing Electric Arc Furnace and Basic Oxygen Furnace Slags Using Surface Free Energy Estimation. J. Clean. Prod. 2018, 188, 355–361. [Google Scholar] [CrossRef]
- Hainin, M.R.; Rusbintardjo, G.; Hameed, M.; Hassan, N.A.; Yusoff, N.M. Utilisation of Steel Slag as an Aggregate Replacement in Porous Asphalt Mixtures. J. Teknol. 2014, 69, 67–73. [Google Scholar] [CrossRef]
- Sorlini, S.; Sanzeni, A.; Rondi, L. Reuse of steel slag in bituminous paving mixtures. J. Hazard. Mater. 2012, 209, 84–91. [Google Scholar] [CrossRef]
- Ziari, H.; Nowbakht, S.; Rezaei, S.; Mahboob, A. Laboratory Investigation of Fatigue Characteristics of Asphalt Mixtures with Steel Slag Aggregates. Adv. Mater. Sci. Eng. 2015, 2015, 623245. [Google Scholar] [CrossRef]
- Kavussi, A.; Jalili, Q.M.; Hassani, A. Fatigue behavior analysis of asphalt mixes containing electric arc furnace (EAF) steel slag. J. Rehabil. Civ. Eng. 2015, 3, 74–86. [Google Scholar]
- Fwa, T.F.; Yoong, C.C.; Than, T.N.; See, S.L. Development of Environmentally Sustainable Pavement Mix. Int. J. Pavement Res. Technol. 2013, 6, 440–446. [Google Scholar]
- Asi, I.M. Evaluating skid resistance of different asphalt concrete mixes. Build. Environ. 2007, 42, 325–329. [Google Scholar] [CrossRef]
- Saha, P.; Ksaibati, K. An optimization model for improving highway safety. J. Traffic Transp. Eng. (Engl. Ed.) 2016, 6, 63–72. [Google Scholar] [CrossRef]
- Hanna, A.N. Guide for Pavement Friction: Background and Research. NCHRRP Res. Results Dig. 2009, 321, 1–8. [Google Scholar] [CrossRef]
- Huschek, S. Assessment of skid resistance of roads under wet conditons. Str. Autob. 1995, 46, 125–131. [Google Scholar]
- Kuang, H.-Z.; Liao, Z.-G.; Liu, B.-M. A study on evaluation standard of skid resistance perrformance for expressway tunnel pavement. Highway 2007, 4, 85–88. (In Chinese) [Google Scholar]
- Ahammed, M.A.; Tighe, R.L. Early-life, long-term, and seasonal variations in skid resistance in flexible and rigid pavements. Transp. Res. Rec. 2009, 2094, 112–120. [Google Scholar] [CrossRef]
- Hall, J.W.; Smith, K.L.; Titus-Glover, L.; Wambold, J.C.; Yager, T.J.; Rado, Z. Guide for pavement friction. In Final Report for NCHRP Project; National Cooperative Highway Research Program: Washington, DC, USA, 2009. [Google Scholar]
- Das, V.K.L. Evaluation of Louisiana Asphalt Pavement Friction; Louisiana State University: Baton Rouge, LA, USA, 2011. [Google Scholar]
- Ahammed, M.A.; Tighe, S.L. Pavement surface mixture, texture, and skid resistance: A factorial analysis. In Airfield and Highway Pavements: Efficient Pavements Supporting Transportation’s Future, Proceedings of the 2008 Airfield and Highway Pavements Conference Bellevue, WA, USA, 15–18 October 2008; American Society of Civil Engineers: Reston, VA, USA, 2008; p. 329. [Google Scholar]
- Liu, H.; Zhang, Z.; Guo, D.; Peng, L.; Bao, Z.; Han, W. Research progress on characteristic technique of pavement micro-texture and testing technology of pavement skid resistance at home and abroad. In Proceedings of the 2011 International Conference on Remote Sensing, Environment and Transportation Engineering, Nanjing, China, 24–26 June 2011; pp. 4368–4372. [Google Scholar]
- Serigos, P.A.; De Fortier Smit, A.; Prozzi, J.A. Incorporating Surface Micro-texture in the Prediction of Skid Resistance of Flexible Pavements. Transp. Res. Rec. 2014, 2457, 105–113. [Google Scholar] [CrossRef]
- Zhang, Z.; Xiao, W.; Xu, X.; Liu, F. Study on outdoor monitoring of anti-sliding performance decay law of asphalt pavement. Traffic Stand. 2014, 42, 58–61. [Google Scholar]
- Kane, M.; Do, M.T.; Piau, J.M. On the Study of Polishing of Road Surface under Traffic load. J. Transp. Eng. 2010, 236, 45–51. [Google Scholar] [CrossRef]
- Kane, M.; Zhao, D.; Do, M.T.; Chailleux, E.; De-Lalarrard, F. Exploring the Ageing Effect of Binder on Skid Resistance Evolution of Asphalt, Road Materials, and Pavement Design. Road Mater. Pavement Des. 2010, 11 (Suppl. S1), 543–557. [Google Scholar] [CrossRef]
- Chen, X.-H.; Chen, S.-X.; Huang, X.-M.; Yang, H.; Steinauer, B. Study on the Polish of Asphalt Pavement: From Macro to Micro Scale. J. China Foreign Highw. 2013, 33, 45–50. [Google Scholar]
- Ueckermann, A.; Wang, D.W.; Oeser, M.; Steinauer, B. A contribution to non-contact skid resistance measurement. Int. J. Pavement Eng. 2015, 16, 646–659. [Google Scholar] [CrossRef]
- Chen, D.; Sefifidmazgi, N.R.; Bahia, H. Exploring the feasibility of evaluating asphalt pavement surface macro-texture using image-based texture analysis method. Road Mater. Pavement Des. 2015, 16, 405–420. [Google Scholar] [CrossRef]
- Wang, H.N.; Wang, C.H.; Bu, Y.; You, Z.P.; Yang, X.; Oeser, M. Correlate aggregate angularity characteristics to the skid resistance of asphalt pavement based on image analysis technology. Constr. Build. Mater. 2020, 242, 118150. [Google Scholar] [CrossRef]
- Xiao, Y.; Wang, F.; Cui, P.D.; Lei, L.; Lin, J.T.; Yi, M.W. Evaluation of Fine Aggregate Morphology by Image Method and Its Effect on Skid-Resistance of Micro-Surfacing. Materials 2018, 11, 920. [Google Scholar] [CrossRef]
- Gao, L.; Ni, F.J.; Luo, H.L.; Wang, H.N.; Chen, Y.Q. Evaluation of Coarse Aggregate in Cold Recycling Mixes Using X-Ray CT Scanner and Image Analysis. J. Test. Eval. 2016, 44, 1239–1249. [Google Scholar] [CrossRef]
- Wang, Z.Q.; Xie, J.G.; Gao, L.; Liu, Y.P.; Tang, L. Three-dimensional characterization of air voids in porous asphalt concrete. Constr. Build. Mater. 2021, 272, 121633. [Google Scholar] [CrossRef]
- Gao, L.; Liu, M.X.; Wang, Z.Q.; Xie, J.G.; Jia, S.C. Correction of texture depth of porous asphalt pavement based on CT scanning technique. Constr. Build. Mater. 2019, 200, 514–520. [Google Scholar] [CrossRef]
- Xin, Q.; Qian, Z.Y.; Miao, Y.H.; Meng, L.J.; Wang, L.B. Three-dimensional characterisation of asphalt pavement macrotexture using laser scanner and micro element. Road Mater. Pavement Des. 2017, 18, 190–199. [Google Scholar] [CrossRef]
- Wang, X.-D. Design of pavement structure and material for full-scale test track. J. Highw. Transp. Res. Dev. 2017, 34, 30–37. (In Chinese) [Google Scholar]
- JTG E42-2005; Test Methods of Aggregate for Highway Engineering. China Communications Press: Beijing, China, 2005.
- JTG F40-2004; Technical Specification for Construction of Highway Asphalt Pavements. China Communications Press: Beijing, China, 2004.
- YBT 4328-2012; Method for The Determination of Content of Free Calcium Oxide in Steel Slag. China Communications Press: Beijing, China, 2012.
- JTG E20-2011; Standard Test Methods of Bitumen and Bituminous Mixtures for Highway Engineering. China Communications Press: Beijing, China, 2011.
- Filippo, G.P.; Armando, A. A new and simplified approach to assess the pavement surface micro-and macrotexture. Constr. Build. Mater. 2017, 148, 476–483. [Google Scholar] [CrossRef]
- Veirh, A.G. A Review of importance Factors Affecting Treadwear. Rubber Chem. Technol. 1992, 65, 601–659. [Google Scholar] [CrossRef]
- Cheng, Y.H. Mathematic Characterization of Road Surface Texture and Its Relation to Laboratory Friction Measures; Michigan Technological University: Houghton, MI, USA, 2002. [Google Scholar]
- Zhang, S. Evaluation and Application of Multi-Scale Asphalt Pavement Skid Resistance Based on Interface Contact Characteristics; South China University of Technology: Guangzhou, China, 2015. [Google Scholar]
- Cao, P.; Yan, X. Theoretical analysis of the influence of asphalt pavement morphology on skid resistance. J. Tribol. 2009, 4, 306–310. [Google Scholar]
- Wang, S.; Veneziano, D.A.; Huang, J.; Shi, X. Estimating Wet-Pavement Exposure with Precipitation Data: Final Report; California Department of Transportation (Caltrans) Division of Research and Innovation: Sacramento, CA, USA, 2006.
Index | Steel Slag | Basalt | Spec Requirements [46] | |
---|---|---|---|---|
Crushing value | 12.8 | 13.8 | ≤28 | |
Los Angeles wear value (%) | 10.2 | 17.9 | ≤30 | |
Apparent relative density | Particle size (12–17 mm) | 3.553 | 2.916 | ≥2.50 |
Particle size (7–12 mm) | 3.604 | 2.928 | ||
Water absorption | Particle size (12–17 mm) | 0.873 | 1.209 | ≤30 |
Particle size (7–12 mm) | 0.962 | 2.611 | ||
Percentage of flat-elongated particles (%) | 8.9 | 6.7 | ≤18 | |
Washing particle content (<0.075 mm) | 0.09 | 0.33 | ≤18 | |
Content of soft rock | 1.7 | 0.5 | ≤5 | |
Adhesion grade | 5 | 5 | ≥4 |
Aggregate Batches | Free Calcium (%) | Ca (OH)2/% | Free-CaO/% |
---|---|---|---|
1st batch | 1.64 | 0.0016 | 1.640 |
2nd batch | 2.03 | 0.0075 | 2.020 |
3rd batch | 0.67 | 0.0047 | 0.660 |
4th batch | 1.32 | 0.023 | 1.300 |
5th batch | 1.45 | 0.0016 | 1.450 |
6th batch | 1.46 | 0.0043 | 1.450 |
7th batch | 1.96 | 0.0046 | 1.950 |
8th batch | 2.11 | 0.0036 | 2.100 |
9th batch | 1.71 | 0.0066 | 1.701 |
Index | Results | Spec Requirements [46] | |
---|---|---|---|
Apparent relative density | >2.36 mm | 2.806 | ≥2.50 |
Water absorption (%) | 1.344 | / | |
Gross volume relative density | 2.704 | / | |
Ruggedness (%) | 4 | ≤12 | |
Methylene blue number (g/kg) | 1 | ≤1.4 | |
Sand equivalent (%) | 74 | ≥60 | |
Angularity (flowing time (s)) | 35.7 | ≥30 |
Index | Results | Spec Requirements [46] | |
---|---|---|---|
Apparent relative density (t/m3) | 2.816 | ≥2.50 | |
Water content (%) | 0.4 | ≤1 | |
Appearance | No agglomeration | No agglomeration | |
Hydrophilic coefficient | 0.7 | <1 | |
Index of plasticity | 2.7 | <4 | |
Heating stability | No change in colour | Physical record | |
Particle size range (%) | <0.6 mm | 100 | 100 |
<0.15 mm | 99.7 | 90~100 | |
<0.075 mm | 96.5 | 75~100 |
Index | Results | Spec Requirements [46] | |
---|---|---|---|
Thixotropic index (25 °C, 5 s,100 g) | 49 | 40~60 | |
Penetration index PI | 0.309 | ≥0 | |
Ductility (5 cm/min, 5 °C) (cm) | 29 | ≥20 | |
Softening degree (global method) (°C) | 75 | ≥60 | |
135 °C kinematic viscosity (Pa·s) | 1.55 | ≤3 | |
Flash point (°C) | 295 | ≥230 | |
Density (15 °C) (g/cm3) | 1.03 | Physical record | |
25 °C elastic recovery (%) | 83.1 | ≥75 | |
Storage stability segregation, 48 h softening point difference (°C) | 2 | ≤2.5 | |
Thin-film heating test 163 °C, 5 h | Quality loss | −0.0304 | ±1.0 |
Residual penetration ratio (25) (%) | 76.4 | ≥65 | |
Residual ductility (5 cm/min, 5 °C) (cm) | 59 | ≥15 |
Equipment Composition | Technical Parameter | Numerical |
---|---|---|
Scanning head | Measurement rate (standard mode) | 1,350,000 times/s |
Measurement rate (fine mode) | 450,000 times/s | |
Scan area | Max 600 × 550 mm | |
Resolution | ≥0.01 | |
Accuracy (standard mode) | Up to 0.02 mm | |
Accuracy (fine mode) | Up to 0.01 mm | |
Volumetric accuracy (standard mode) | 0.02 + 0.035 mm/m | |
Volumetric accuracy (fine mode) | 0.02 + 0.015 mm/m | |
Datum distance (standard mode) | 300 mm | |
Datum distance (fine mode) | 150 mm | |
Depth of field (standard mode) | 450 mm | |
Depth of field (fine mode) | 150 mm | |
Maximum depth of field | 550 mm | |
Hardware specifications | Operating temperature range | −20~40 °C |
Working humidity range | 10–90% |
Gradation Type | The Average Value of Swelling | Spec Requirements |
---|---|---|
Steel-slag SMA-13 | 0.59% | ≤1.0% |
Basalt SMA-13 | 0.43% |
Gradation Type | Laboratory/mm | Pavement/mm | |
---|---|---|---|
Steel-slag SMA-13 | Group 1 | 0.67 | 0.64 |
Group 2 | 0.65 | 0.63 | |
Group 3 | 0.64 | 0.63 | |
Basalt SMA-13 | Group 1 | 0.63 | 0.63 |
Group 2 | 0.60 | 0.61 | |
Group 3 | 0.61 | 0.60 | |
Regulation | ≥0.55 |
Gradation Type | Before the Rutting Test | After Rutting Test | Attenuation Rate/% | |
---|---|---|---|---|
Steel-slag SMA-13 | Group 1 | 76.4 | 73.2 | 4.19 |
Group 2 | 77.3 | 74.2 | 4.01 | |
Group 3 | 76.3 | 73 | 4.33 | |
Group 4 | 75.1 | 72.3 | 3.73 | |
Basalt SMA-13 | Group 1 | 78.6 | 74.7 | 5.96 |
Group 2 | 77 | 73.1 | 5.36 | |
Group 3 | 76 | 72.7 | 5.34 | |
Group 4 | 75 | 70 | 6.67 |
Gradation Type | BPN Value | |
---|---|---|
Steel-slag SMA-13 | MP 1 | 78.3 |
MP 2 | 77.4 | |
MP 3 | 76.6 | |
MP 4 | 76.3 | |
MP 5 | 76.8 | |
MP 6 | 77.3 | |
Basalt SMA-13 | MP 7 | 76.1 |
Parameter | Sp | Sz | Sv | Sq | Ssk | Spc |
---|---|---|---|---|---|---|
a | 73.925 | 35.19 | 53.307 | 69.892 | 83.382 | 55.503 |
b | 1.704 | 4.931 | 3.508 | 5.306 | 4.538 | 5.939 |
R2 | 0.85 | 0.87 | 0.85 | 0.97 | 0.74 | 0.81 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Zhang, S.; Guo, R.; Yan, F.; Dong, R.; Kong, C.; Li, J. Analysis of Asphalt Mixtures Modified with Steel Slag Surface Texture Using 3D Scanning Technology. Materials 2023, 16, 3256. https://doi.org/10.3390/ma16083256
Zhang S, Guo R, Yan F, Dong R, Kong C, Li J. Analysis of Asphalt Mixtures Modified with Steel Slag Surface Texture Using 3D Scanning Technology. Materials. 2023; 16(8):3256. https://doi.org/10.3390/ma16083256
Chicago/Turabian StyleZhang, Shuai, Rongxin Guo, Feng Yan, Ruzhu Dong, Chuiyuan Kong, and Junjie Li. 2023. "Analysis of Asphalt Mixtures Modified with Steel Slag Surface Texture Using 3D Scanning Technology" Materials 16, no. 8: 3256. https://doi.org/10.3390/ma16083256
APA StyleZhang, S., Guo, R., Yan, F., Dong, R., Kong, C., & Li, J. (2023). Analysis of Asphalt Mixtures Modified with Steel Slag Surface Texture Using 3D Scanning Technology. Materials, 16(8), 3256. https://doi.org/10.3390/ma16083256