Compaction quality evaluation of subgrade based on soil characteristics assessment using machine learning

Zeng, 2013, Subgrade failure division and influence factors analyze of expressway, Appl Mech Mater, 256-259, 1737, 10.4028/www.scientific.net/AMM.256-259.1737 Thompson, 2008, Estimating compaction of cohesive soils from machine drive power, J Geotech Geoenviron Eng, 134, 1771, 10.1061/(ASCE)1090-0241(2008)134:12(1771) Liu, 2016, Real-time quality monitoring and control of highway compaction, Autom Constr, 62, 114, 10.1016/j.autcon.2015.11.007 Arulrajah, 2014, Reclaimed asphalt pavement and recycled concrete aggregate blends in pavement subbases: Laboratory and field evaluation, J Mater Civ Eng, 26, 349, 10.1061/(ASCE)MT.1943-5533.0000850 Rinehart, 2008, Instrumentation of a roller compactor to monitor vibration behavior during earthwork compaction, Autom Constr, 17, 144, 10.1016/j.autcon.2006.12.006 Davich, 2006, Validation of dcp and lwd moisture specifications for granular materials, Granular Mater ASTM Standard test method for repetitive static plate load tests of soils and flexible pavement components, for use in evaluation and design of airport and highway pavements. ASTM West Conshohocken, PA; 2009. An, 2020, Dynamic optimization of compaction process for rockfill materials, Autom Constr, 110, 103038, 10.1016/j.autcon.2019.103038 Liu, 2014, Compaction quality assessment of earth-rock dam materials using roller-integrated compaction monitoring technology, Autom Constr, 44, 234, 10.1016/j.autcon.2014.04.016 Bruce, 2013, Federal highway administration design manual: Deep mixing for embankment and foundation support, Design Barman, 2018, Artificial neural network–based intelligent compaction analyzer for real-time estimation of subgrade quality, Int J Geomech, 18, 1943 Zhang, 2021, Investigation of the correlations between the field pavement in-place density and the intelligent compaction measure value (icmv) of asphalt layers, Constr Build Mater, 292, 123439, 10.1016/j.conbuildmat.2021.123439 Hu, 2019, Investigating key factors of intelligent compaction for asphalt paving: A comparative case study, Constr Build Mater, 229, 116876, 10.1016/j.conbuildmat.2019.116876 White, 2008, Relationships between in situ and roller-integrated compaction measurements for granular soils, J Geotech Geoenviron Eng, 134, 1763, 10.1061/(ASCE)1090-0241(2008)134:12(1763) Hu, 2017, Recommendations on intelligent compaction parameters for asphalt resurfacing quality evaluation, J Construct Eng Manage, 143, 04017065, 10.1061/(ASCE)CO.1943-7862.0001361 White DJ, Vennapusa P, Thompson MJ. Field validation of intelligent compaction monitoring technology for unbound materials; 2007. Hu, 2020, Influence of moisture content on intelligent soil compaction, Autom Constr, 113, 103141, 10.1016/j.autcon.2020.103141 Tatsuoka, 2018, Importance of controlling the degree of saturation in soil compaction linked to soil structure design, Transp Geotech, 17, 3, 10.1016/j.trgeo.2018.06.004 Yuan, 2008, Experimental research on compaction characteristics of aeolian sand, Front Architecture Civ Eng China, 2, 359, 10.1007/s11709-008-0053-3 Wang, 2021, Study on performance tests and the application of construction waste as subgrade backfill, Materials, 14, 2381, 10.3390/ma14092381 Shi, 2014, Meng F Experimental research on physical and mechanical properties of high-speed railway subgrade filler, Appl Mech Mater, 496-500, 2533, 10.4028/www.scientific.net/AMM.496-500.2533 Tarawneh, 2017, Predicting standard penetration test n-value from cone penetration test data using artificial neural networks, Geosci Front, 8, 199, 10.1016/j.gsf.2016.02.003 Shahin, 2016, State-of-the-art review of some artificial intelligence applications in pile foundations, Geosci Front, 7, 33, 10.1016/j.gsf.2014.10.002 Zaman, 2010, Neural network modeling of resilient modulus using routine subgrade soil properties, Int J Geomech, 10, 1, 10.1061/(ASCE)1532-3641(2010)10:1(1) Sivrikaya, 2011, Estimation of compaction parameters of fine-grained soils in terms of compaction energy using artificial neural networks, IJNAM, 35, 1830 Isik, 2013, Estimating compaction parameters of fine-and coarse-grained soils by means of artificial neural networks, Environ Earth Sci, 69, 2287, 10.1007/s12665-012-2057-5 Jayan, 2015, Prediction of compaction parameters of soils using artificial neural network, Asian J Eng Technol, 3, 2321 Zhang, 2021, Intelligent modelling of clay compressibility using hybrid meta-heuristic and machine learning algorithms, Geosci Front, 12, 441, 10.1016/j.gsf.2020.02.014 Das, 2021, Principles of geotechnical engineering, Cengage Learn Xu, 2016, Adaptive quality control and acceptance of pavement material density for intelligent road construction, Autom Constr, 62, 78, 10.1016/j.autcon.2015.11.004 Ministry of transport of the people's republic of china. Test methods of soils for highway engineering. JTG E40-2007. Beijing: China Communications Press Co., Ltd; 2007. Pietzsch, 1992, Simulation of soil compaction with vibratory rollers, J Terramech, 29, 585, 10.1016/0022-4898(92)90038-L Jain, 2010, Computational approach to predict soil shear strength, Int J Eng Sci Technol, 2, 3874 Kanungo, 2014, Artificial neural network (ann) and regression tree (cart) applications for the indirect estimation of unsaturated soil shear strength parameters, Front Earth Sci, 8, 439, 10.1007/s11707-014-0416-0 ASTM D A Standard test method for unconfined compressive strength of cohesive soil. ASTM international West Conshohocken, PA; 2016. Pandian, 1997, Re-examination of compaction characteristics of fine-grained soils, Geotechnique, 47, 363, 10.1680/geot.1997.47.2.363 Ratnam, 2019, Prediction of compaction and compressibility characteristics of compacted soils, Int J Appl Eng Res, 14, 621 Wesley, 2003, Residual strength of clays and correlations using atterberg limits, Geotechnique, 53, 669, 10.1680/geot.2003.53.7.669 Li, 2001 Rui, 2013, Experimental study on compaction characteristics of lime-treated expansive soil, Eng Geol, 21, 864 Antognozzi, 2001, Observation of molecular layering in a confined water film and study of the layers viscoelastic properties, Appl Phys Lett, 78, 300, 10.1063/1.1339997 Dung, 2007, Experimental study on intensity character of rock-soil aggregate mixture, Yantu Lixue(Rock Soil Mech), 28, 1269 Liu, 2021, Guo Z Identification of grouting compactness in bridge bellows based on the bp neural network, Structures, 32, 817, 10.1016/j.istruc.2021.02.069 Breiman, 2001, Random forests, MLear, 45, 5 Wang, 2018, Particle swarm optimization algorithm: An overview, Soft Comput, 22, 387, 10.1007/s00500-016-2474-6 Yang, 2019 Shi, 2019 Zhu, 2018 An, 2019, Analysis and research on compaction control index of silt subgrade, J Guangxi Univ (Nat Sei Ed), 44, 206 Wang, 2007 Wang, 2014, The shear strength research of compacted loess considering the impact of moisture content and dry density, J Xian Univ Arch Technol (Nat Sci Ed), 46, 687 Wang, 2011 Yang, 2014 Huang, 2018, Application of improved pso-bp neural network in customer churn warning, Proc Comput Sci, 131, 1238, 10.1016/j.procs.2018.04.336 Kumar, 2021, Evaluation of pavement condition index using artificial neural network approach, Transport Dev Econ, 7, 1 Efe S, Shokouhian M. Proposal on implementing machine learning with highway datasets. Yao, 2021, Gong G Evaluation of chloride diffusion in concrete using pso-bp and bp neural network, IOP Conf Ser: Earth Environ Sci, 687, 012037, 10.1088/1755-1315/687/1/012037 Srikanth, 2020, Deterioration models for prediction of remaining useful life of timber and concrete bridges: A review, J Traffic Transport Eng (English Ed), 7, 152, 10.1016/j.jtte.2019.09.005 Risal, 2021, Improving phase prediction accuracy for high entropy alloys with machine learning, Comput Mater Sci, 192, 110389, 10.1016/j.commatsci.2021.110389 Nkulikiyinka, 2020, Prediction of sorption enhanced steam methane reforming products from machine learning based soft-sensor models, Energy AI, 2, 100037, 10.1016/j.egyai.2020.100037 He, 2021, Study on the application of excess pore water pressure in analyzing the effect of dynamic compaction for the subgrades filled with aeolian sand and gravel soil underwater, IOP Conf Ser: Earth Environ Sci, 768, 012086, 10.1088/1755-1315/768/1/012086 Machet, 1977, Vibratory compaction of bituminous mixes in france, Assoc Asphalt Paving Technol Proc