Maghrebi, M; Shamsoddini, A and Waller, S T (2016) Fusion-based learning approach for predicting concrete pouring productivity based on construction and supply parameters. Construction Innovation, 16(2), pp. 185-202. ISSN 1471-4175
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | construction scheduling,artificial intelligence,construction management,construction estimating,computer systems,construction engineering management |
| Index terms: | computer system, construction site, stability, methodology, construction estimating, mean square error, construction engineering management, construction scheduling, deviation, concrete industry, productivity, database, artificial intelligence |
| Subjects: | industry analysis, data management, project controls, probability and distributions, project management theory and practice, research methods, management, structural engineering, financial and cost management, artificial intelligence, work location, computing systems |
| Topics: | Business Strategy, Cost Management, Project Management, Research Practice, Engineering Principles, Site Management, Time Control, Digital Applications |
| Descriptive scope: | 4 PCTA |
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