Fusion based learning approach for predicting concrete pouring productivity based on construction and supply parameters

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.

Prediction of Pavement Performance: Application of Support Vector Regression with Different Kernels

“…Machine learning techniques have been widely used in the literature to solve civil engineering predictions and classification problems (29)(30)(31)(32)(33)(34)(35)(36). Also, some researchers used this technique in pavement evaluation (37)(38)(39)(40)(41).…”

Applications of artificial intelligence and machine learning within supply chains:systematic review and future research directions

“…Abbas et al (2020*) from another hand, proposed and tested a blockchain and ML-enabled system, which has the following two modules: the drug supply chain system and the drug recommendation system. Moreover and using advanced ML techniques, Maghrebi et al (2015), developed a model for predicting the duration of concrete operations, thereby reducing idleness and the cost of equipment in construction sites. Likewise, Hoefer (2017) in his master thesis, developed an automated method that characterizes a conceptual design’s geometry and uses that information to help select a suitable manufacturing process.…”

Automated planning of concrete joint layouts with 4D-BIM

“…Here sensors and drones can act as intelligent agents [5] to collect data from the concreting process, whilst these data could be synchronised with the BIM model, using linked-data [67], machine learning and artificial intelligence (AI) algorithms [75]. It has been shown that by accurate tuning the AI algorithms [74], the decision making in tasks related concrete pouring can be facilitated by forecasting the duration [29], estimating the productivity [55] and identifying the real-time hazards [47]. This process helps project managers compare the concreting process with the developed concrete schedules.…”