Journal Article10.1108/CI-05-2015-0025
Fusion based learning approach for predicting concrete pouring productivity based on construction and supply parameters
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TL;DR: In this paper, a machine learner fusion-regression (MLF-R) is used to predict the concrete pouring production rate by considering both construction and supply parameters, and by using a more stable learning method.
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Abstract: Purpose
The purpose of this paper is to predict the concrete pouring production rate by considering both construction and supply parameters, and by using a more stable learning method.
Design/methodology/approach
Unlike similar approaches, this paper considers not only construction site parameters, but also supply chain parameters. Machine learner fusion-regression (MLF-R) is used to predict the production rate of concrete pouring tasks.
Findings
MLF-R is used on a field database including 2,600 deliveries to 507 different locations. The proposed data set and the results are compared with ANN-Gaussian, ANN-Sigmoid and Adaboost.R2 (ANN-Gaussian). The results show better performance of MLF-R obtaining the least root mean square error (RMSE) compared with other methods. Moreover, the RMSEs derived from the predictions by MLF-R in some trials had the least standard deviation, indicating the stability of this approach among similar used approaches.
Practical implications
The size of the database used in this study is much larger than the size of databases used in previous studies. It helps authors draw their conclusions more confidently and introduce more generalised models that can be used in the ready-mixed concrete industry.
Originality/value
Introducing a more stable learning method for predicting the concrete pouring production rate helps not only construction parameters, but also traffic and supply chain parameters.
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Citations
Automated planning of concrete joint layouts with 4D-BIM
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TL;DR: The developed automated programme framework offers a cost-effective and accurate methodology to address the limitations and inefficiencies of traditional methods of designing construction joints and planning pours and provides a novel, yet technically sound, basis for further application of BIM in structural engineering.
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Hazard-based model for concrete pouring duration using construction site and supply chain parameters
TL;DR: In this paper, the impact of supply chain parameters on predicting concrete pouring duration was investigated, and the results showed that severe bias occurs when assumptions associated with linear regression are overlooked, while in the developed models the supply parameters are found to have significant impacts on concrete pouring time.
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Estimating on-site emissions during ready mixed concrete (RMC) delivery: A methodology
TL;DR: In this article, a literature review was conducted to define and delineate upon greenhouse gases emitted during ready mixed concrete (RMC) operations and the appropriate methods to calculate them, and a methodology was then developed and validated by analysing ten delivery cycle times in a case study.
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