{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T08:01:49Z","timestamp":1762761709125,"version":"build-2065373602"},"reference-count":43,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2020,9,10]],"date-time":"2020-09-10T00:00:00Z","timestamp":1599696000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Yalong River Joint Funds of the National Natural Science Foundation of China","award":["U1965207"],"award-info":[{"award-number":["U1965207"]}]},{"DOI":"10.13039\/501100001809","name":"The National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51839007"],"award-info":[{"award-number":["51839007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"The Natural Science Foundation of Tianjin","award":["17JCQNJC07100"],"award-info":[{"award-number":["17JCQNJC07100"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The compaction construction process is a critical operation in civil engineering projects. By establishing a construction simulation model, the compaction duration can be predicted to assist construction management. Existing studies have achieved adaptive modelling of input parameters from a Bayesian inference perspective, but usually assume the model as parametric distribution. Few studies adopt the nonparametric distribution to achieve robust inference, but still need to manually set hyper-parameters. In addition, the condition of when the roller stops moving ignores the impact of randomness of roller movement. In this paper, a new adaptive compaction construction simulation method is presented. The Bayesian field theory is innovatively adopted for input parameter adaptive modelling. Next, whether rollers have offset enough distance is used to determine the moment of stopping. Simulation experiments of the compaction process of a high earth dam project are demonstrated. The results indicate that the Bayesian field theory performs well in terms of accuracy and efficiency. When the size of roller speed dataset is 787,490, the Bayesian field theory costs only 1.54 s. The mean absolute error of predicted compaction duration reduces significantly with improved judgment condition. The proposed method can contribute to project resource planning, particularly in a high-frequency construction monitoring environment.<\/jats:p>","DOI":"10.3390\/s20185178","type":"journal-article","created":{"date-parts":[[2020,9,10]],"date-time":"2020-09-10T22:58:01Z","timestamp":1599778681000},"page":"5178","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Adaptive Compaction Construction Simulation Based on Bayesian Field Theory"],"prefix":"10.3390","volume":"20","author":[{"given":"Jun","family":"Zhang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia","family":"Yu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Guan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1269-0366","authenticated-orcid":false,"given":"Jiajun","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dawei","family":"Tong","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Binping","family":"Wu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.autcon.2014.04.016","article-title":"Compaction quality assessment of earth-rock dam materials using roller-integrated compaction monitoring technology","volume":"44","author":"Liu","year":"2014","journal-title":"Autom. Constr."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e12357","DOI":"10.1111\/exsy.12357","article-title":"Smart bacteria-foraging algorithm-based customized kernel support vector regression and enhanced probabilistic neural network for compaction quality assessment and control of earth-rock dam","volume":"35","author":"Wang","year":"2018","journal-title":"Expert Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1140","DOI":"10.1061\/(ASCE)CO.1943-7862.0000220","article-title":"Role of Simulation in Construction Engineering and Management","volume":"136","author":"Abourizk","year":"2010","journal-title":"J. Constr. Eng. Manag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1016\/j.aej.2015.03.034","article-title":"Analysis of earth-moving systems using discrete-event simulation","volume":"54","author":"Shawki","year":"2015","journal-title":"Alex. Eng. J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/j.aap.2015.09.015","article-title":"A hybrid simulation approach for integrating safety behavior into construction planning: An earthmoving case study","volume":"93","author":"Goh","year":"2016","journal-title":"Accid. Anal. Prev."},{"key":"ref_6","first-page":"1853","article-title":"Overview of construction simulation approaches to model construction processes","volume":"11","author":"Bokor","year":"2019","journal-title":"Organ. Technol. Manag. Constr. Int. J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1016\/j.jclepro.2018.10.113","article-title":"\u2018Eco-Hauling\u2019 principles to reduce carbon emissions and the costs of earthmoving\u2014A case study","volume":"208","author":"Krantz","year":"2019","journal-title":"J. Clean. Prod."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"867","DOI":"10.1016\/j.aei.2015.03.001","article-title":"Construction equipment activity recognition for simulation input modeling using mobile sensors and machine learning classifiers","volume":"29","author":"Akhavian","year":"2015","journal-title":"Adv. Eng. Inform."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"04016106","DOI":"10.1061\/(ASCE)CO.1943-7862.0001243","article-title":"Methodology for Real-Time Monitoring of Construction Operations Using Finite State Machines and Discrete-Event Operation Models","volume":"143","author":"Louis","year":"2017","journal-title":"J. Constr. Eng. Manag."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"04018001","DOI":"10.1061\/(ASCE)CO.1943-7862.0001441","article-title":"Chaos Theory\u2013Inspired Evolutionary Method to Refine Imperfect Sensor Data for Data-Driven Construction Simulation","volume":"144","author":"Shrestha","year":"2018","journal-title":"J. Constr. Eng. Manag."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.aei.2015.01.011","article-title":"Construction performance monitoring via still images, time-lapse photos, and video streams: Now, tomorrow, and the future","volume":"29","author":"Yang","year":"2015","journal-title":"Adv. Eng. Inform."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Du, L., Zhong, R., Sun, H., Zhu, Q., and Zhang, Z. (2018). Study of the Integration of the CNU-TS-1 Mobile Tunnel Monitoring System. Sensors, 18.","DOI":"10.3390\/s18020420"},{"key":"ref_13","first-page":"39","article-title":"Productivity based method for forecasting cost & time of earthmoving operations using sampling GPS data","volume":"21","author":"Alshibani","year":"2016","journal-title":"J. Inf. Technol. Constr."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.autcon.2018.04.002","article-title":"Analyzing context and productivity of tunnel earthmoving processes using imaging and simulation","volume":"92","author":"Kim","year":"2018","journal-title":"Autom. Constr."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Mehrang, S., Pietil\u00e4, J., and Korhonen, I. (2018). An Activity Recognition Framework Deploying the Random Forest Classifier and a Single Optical Heart Rate Monitoring and Triaxial Accelerometer Wrist-Band. Sensors, 18.","DOI":"10.3390\/s18020613"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Cheung, W.-F., Lin, T.-H., and Lin, Y.-C. (2018). A Real-Time Construction Safety Monitoring System for Hazardous Gas Integrating Wireless Sensor Network and Building Information Modeling Technologies. Sensors, 18.","DOI":"10.3390\/s18020436"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"04013021","DOI":"10.1061\/(ASCE)CO.1943-7862.0000775","article-title":"Knowledge-Based Simulation Modeling of Construction Fleet Operations Using Multimodal-Process Data Mining","volume":"139","author":"Akhavian","year":"2013","journal-title":"J. Constr. Eng. Manag."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.autcon.2014.02.018","article-title":"Framework for near real-time simulation of earthmoving projects using location tracking technologies","volume":"42","author":"Vahdatikhaki","year":"2014","journal-title":"Autom. Constr."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Bolstad, W.M. (2007). Introduction to Bayesian Statistics, Wiley-Interscience.","DOI":"10.1002\/9780470181188"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1061\/(ASCE)0733-9364(2006)132:8(882)","article-title":"Bayesian Updating Application into Simulation in the North Edmonton Sanitary Trunk Tunnel Project","volume":"132","author":"Chung","year":"2006","journal-title":"J. Constr. Eng. Manag."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.autcon.2012.05.007","article-title":"Adaptive real-time tracking and simulation of heavy construction operations for look-ahead scheduling","volume":"27","author":"Song","year":"2012","journal-title":"Autom. Constr."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"04013031","DOI":"10.1061\/(ASCE)CO.1943-7862.0000764","article-title":"Bayesian-Based Hybrid Simulation Approach to Project Completion Forecasting for Underground Construction","volume":"140","author":"Zhang","year":"2014","journal-title":"J. Constr. Eng. Manag."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1631\/jzus.A1700372","article-title":"Construction simulation of high arch dams based on fuzzy Bayesian updating algorithm","volume":"19","author":"Guan","year":"2018","journal-title":"J. Zhejiang Univ. Sci. A"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ghosal, S., and Van Der Vaart, A. (2017). Fundamentals of Nonparametric Bayesian Inference, Cambridge University Press.","DOI":"10.1017\/9781139029834"},{"key":"ref_25","first-page":"445","article-title":"Nonparametric Bayesian methods: A gentle introduction and overview","volume":"23","author":"MacEachern","year":"2016","journal-title":"Commun. Stat. Appl. Methods"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jmp.2011.08.004","article-title":"A tutorial on Bayesian nonparametric models","volume":"56","author":"Gershman","year":"2012","journal-title":"J. Math. Psychol."},{"key":"ref_27","first-page":"175","article-title":"Nonparametric Bayesian inference in applications","volume":"27","author":"Mueller","year":"2017","journal-title":"J. Ital. Stat. Soc."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"654","DOI":"10.3846\/jcem.2019.7948","article-title":"Construction phase oriented dynamic simulation: Taking RCC dam placement process as an example","volume":"25","author":"Hu","year":"2019","journal-title":"J. Civ. Eng. Manag."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"160605","DOI":"10.1103\/PhysRevLett.121.160605","article-title":"Density Estimation on Small Data Sets","volume":"121","author":"Chen","year":"2018","journal-title":"Phys. Rev. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4693","DOI":"10.1103\/PhysRevLett.77.4693","article-title":"Field Theories for Learning Probability Distributions","volume":"77","author":"Bialek","year":"1996","journal-title":"Phys. Rev. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"026137","DOI":"10.1103\/PhysRevE.65.026137","article-title":"Occam factors and model independent Bayesian learning of continuous distributions","volume":"65","author":"Nemenman","year":"2002","journal-title":"Phys. Rev. E"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"011301","DOI":"10.1103\/PhysRevE.90.011301","article-title":"Estimation of probability densities using scale-free field theories","volume":"90","author":"Kinney","year":"2014","journal-title":"Phys. Rev. E"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"032107","DOI":"10.1103\/PhysRevE.92.032107","article-title":"Unification of field theory and maximum entropy methods for learning probability densities","volume":"92","author":"Kinney","year":"2015","journal-title":"Phys. Rev. E"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1007\/s11431-007-6006-6","article-title":"Theory and practice of construction simulation for high rockfill dam","volume":"50","author":"Zhong","year":"2007","journal-title":"Sci. China Ser. E Technol. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1111\/mice.12337","article-title":"Earth Dam Construction Simulation Considering Stochastic Rainfall Impact","volume":"33","author":"Zhang","year":"2017","journal-title":"Comput. Civ. Infrastruct. Eng."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"05015002","DOI":"10.1061\/(ASCE)CP.1943-5487.0000523","article-title":"Construction Simulation for a Core Rockfill Dam Based on Optimal Construction Stages and Zones: Case Study","volume":"30","author":"Du","year":"2016","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zhong, D.-H., Zhao, M., Yu, J., and Lv, F. (2019). An Optimization Model for Construction Stage and Zone Plans of Rockfill Dams Based on the Enhanced Whale Optimization Algorithm. Energies, 12.","DOI":"10.3390\/en12030466"},{"key":"ref_38","first-page":"51","article-title":"Theory and application of construction simulation coupled with quality factors for high core rock-fill dam","volume":"43","author":"Zhong","year":"2012","journal-title":"Water Resour. Hydropower Eng."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3406","DOI":"10.1007\/s11431-009-0343-6","article-title":"Theoretical research on construction quality real-time monitoring and system integration of core rockfill dam","volume":"52","author":"Zhong","year":"2009","journal-title":"Sci. China Ser. E Technol. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1906","DOI":"10.1007\/s11431-011-4429-6","article-title":"Real-time compaction quality monitoring of high core rockfill dam","volume":"54","author":"Zhong","year":"2011","journal-title":"Sci. China Ser. E Technol. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.autcon.2014.10.003","article-title":"Study on real-time construction quality monitoring of storehouse surfaces for RCC dams","volume":"49","author":"Liu","year":"2015","journal-title":"Autom. Constr."},{"key":"ref_42","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"JMLR"},{"key":"ref_43","unstructured":"Gordon, J.R., Dean, M., and Kees, M. (2019, May 03). Dirichletprocess: Build Dirichlet Process Objects for Bayesian Modelling. R Package Version 0.3.1. Available online: https:\/\/CRAN.R-project.org\/package=dirichletprocess."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/18\/5178\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:08:54Z","timestamp":1760177334000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/18\/5178"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,10]]},"references-count":43,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["s20185178"],"URL":"https:\/\/doi.org\/10.3390\/s20185178","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2020,9,10]]}}}