A comparison of machine learning methods for predicting the compressive strength of field-placed concrete

Details

• Journal Title:
  Construction and Building Materials
• Personal Author:
  DeRousseau, M.A. ; Laftchiev, E. ; Kasprzyk, J.R. ; Rajagopalan, B. ; Srubar, W.V.
• NOAA Program & Office:
  CIRES (Cooperative Institute for Research in Environmental Sciences)
• Description:
  This study evaluates the efficacy of machine learning (ML) methods to predict the compressive strength of field-placed concrete. We employ both field- and laboratory-obtained data to train and test ML models of increasing complexity to determine the best-performing model specific to field-placed concrete. The ability of ML models trained on laboratory data to predict the compressive strength of field-placed concrete is evaluated and compared to those models trained exclusively on field-acquired data. Results substantiate that the random forest ML model trained on field-acquired data exhibits the best performance for predicting the compressive strength of field-placed concrete; the RMSE, MAE, and R2 values were 730 psi, 530 psi, and 0.51, respectively. We also show that hybridization of field- and laboratory-acquired data for training ML models is a promising method for reducing common over-prediction issues encountered by laboratory-trained models that are used in isolation to predict the compressive strength of field-placed concrete.
• Source:
  Construction and Building Materials, 228, 116661
• DOI:
  https://doi.org/10.1016/j.conbuildmat.2019.08.042
• ISSN:
  0950-0618
• Format:
  pdf
• Publisher:
  Elsevier BV
• Document Type:
  Journal Article-Accepted Manuscript
• Rights Information:
  Accepted Manuscript
• Rights Statement:
  The NOAA IR provides access to this content under the authority of the government's retained license to distribute publications and data resulting from federal funding. While users may legally access this content, the copyright owners retain rights that govern the reproduction, redistribution, and re-use of this work. The user is solely responsible for complying with applicable copyright law.
• Compliance:
  Library
• Main Document Checksum:
  urn:sha-512:e3c496044b57d797fec3541907f94587882eb52a837a32f9623d514aaaf4c5b1aca46803a59342a2db395a85207e496872c22c6871394c212618477199f164ad
• Download URL:
  https://repository.library.noaa.gov/view/noaa/68243/noaa_68243_DS1.pdf
• File Type:
  [PDF - 1.25 MB ]