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Title:

Machine Learning Fusion Based Technique for Predicting the Concrete Pouring Production Rate Based on Traffic and Supply Chain Parameters

Accession Number:

01555296

Record Type:

Component

Availability:

Transportation Research Board Business Office

500 Fifth Street, NW
Washington, DC 20001 United States

Abstract:

Most construction materials are supplied by out-sourced suppliers and are transferred via road transportation. Concrete is the most used construction material in the world and demand for concrete is ever increasing. In addition, a wide range of crews and machineries are involved in concrete based constructions tasks. As a result, being able to accurately estimate the concrete pouring task will potentially have both cost and time saving effects. Moreover, due to space limitations as well as technical obligations, fresh concrete is mixed at a Ready Mixed Concrete (RMC) depot and then hauled by trucks to constructions sites. Therefore, to predict the concrete pouring duration managers must consider both traffic and supply parameters. In this paper, a data structure is presented to cover these parameters and Machine Learner Fusion-Regression (MLF12R) is used to predict the production rate of concrete pouring tasks. A field database that covers a month of deliveries across a metropolitan area was gathered for evaluating the proposed method. The dataset includes over 2600 deliveries to 507 different locations. Finally, the MLF-R was tested with the proposed dataset and the results compared with ANN-Gaussian, ANN-Sigmoid and Adaboost.R2 (ANN-Gaussian) which are trained with the exact training sets. The results show that MLF-R obtained the least RMSE in comparison with other methods, and also acquired the least standard deviation of RMSE and correlation coefficient with the stability of this approach.

Supplemental Notes:

This paper was sponsored by TRB committee AFH50 Portland Cement Concrete Pavement Construction.

Monograph Accession #:

01550057

Report/Paper Numbers:

15-5928

Language:

English

Corporate Authors:

Transportation Research Board

500 Fifth Street, NW
Washington, DC 20001 United States

Authors:

Maghrebi, Mojtaba
Shamsoddini, Ali
Waller, S Travis

Pagination:

15p

Publication Date:

2015

Conference:

Transportation Research Board 94th Annual Meeting

Location: Washington DC, United States
Date: 2015-1-11 to 2015-1-15
Sponsors: Transportation Research Board

Media Type:

Digital/other

Features:

Figures; References; Tables

Geographic Terms:

Adelaide (Australia)

Subject Areas:

Data and Information Technology; Freight Transportation; I72: Traffic and Transport Planning

Source Data:

Transportation Research Board Annual Meeting 2015 Paper #15-5928

Files:

TRIS, TRB, ATRI

Created Date:

Dec 30 2014 1:58PM