Utilization of machine learning for analyzing concrete material consumption in Japan

N. A. Vios, M. Henry, J. Opon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Concrete is one of the most widely used construction material in the world. For developed countries like Japan, the trend of concrete consumption is affected by significant changes over time. These changes can be attributed to the decrease in population, stability of the economy, declining need of new infrastructures and other hidden factors that might not be easily recognizable with conventional statistical modeling. Understanding these drivers of concrete material consumption is important in evaluating construction resource efficiency. As such, this paper aims to understand concrete material consumption trends in Japan by utilizing machine learning techniques. Machine learning has gained popularity mainly due to its self-learning characteristics that allows performance enhancement without being explicitly programmed. A backward approach of stepwise regression analysis was performed to quantify the contribution of socioeconomic factors to concrete consumption. After which, an agglomerative hierarchical clustering was made to identify similar patterns of concrete consumption behavior across the prefectures of Japan and group these prefectures together. Through the detection of patterns in the historical data, understanding the drivers of concrete consumption leads to the enhancement of the efficiency of resource consumption in the future.

Original languageEnglish
Title of host publicationEASEC16 - Proceedings of the 16th East Asian-Pacific Conference on Structural Engineering and Construction, 2019
EditorsChien Ming Wang, Sritawat Kitipornchai, Vinh Dao
PublisherSpringer Science and Business Media Deutschland GmbH
Pages1271-1281
Number of pages11
ISBN (Print)9789811580789
DOIs
Publication statusPublished - 2021
Externally publishedYes
Event16th East Asian-Pacific Conference on Structural Engineering and Construction, 2019 - Brisbane, Australia
Duration: 2019 Dec 32019 Dec 6

Publication series

NameLecture Notes in Civil Engineering
Volume101
ISSN (Print)2366-2557
ISSN (Electronic)2366-2565

Conference

Conference16th East Asian-Pacific Conference on Structural Engineering and Construction, 2019
CountryAustralia
CityBrisbane
Period19/12/319/12/6

Keywords

  • Concrete material consumption
  • Historical data
  • Machine learning
  • Resource efficiency

ASJC Scopus subject areas

  • Civil and Structural Engineering

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