Neural Networks for Estimating the Productivity of Concreting Activities

Modelling Masonry Crew Productivity Using Two Artificial Neural Network Techniques

“…In our study we focused on total crew productivity, because we were able to capture onsite production data for the entire crew on a given day as opposed to collecting data pertaining to productions of individual workers. This is consistent with the approach of other researchers (Ezeldin, Sharara 2006;Oral, E. L., Oral, M. 2010). The aims of this study are twofold: (a) to analyse the relationship between the various input factors listed in Table 1 and the output of masonry crew productivity; and (b) to develop predictive models for crew productivity under the given conditions.…”

“…Sanders and Thomas (1991) focused on five project-related factors that significantly affect masonry productivity; namely, work type, building element, design requirements, construction methods, and weather. Ezeldin and Sharara (2006) tried to estimate the productivity of concreting activities by using neural networks, and obtained successful predictive models with strong generalization capabilities.…”

Development of Human Pose Analyzing Algorithms for the Determination of Construction Productivity in Real-Time

“…Some of these approaches are based on analysis by simulation and are application specific. Other models employing neural networks were developed by [17], [18] and [19]. Additional approach based on neural networks was presented which analyses the attitude patterns of the workers to determine the productivity [20].…”

“…There are several other methods that do not require the background to be modeled such as the optical flow methods [15,17] and hierarchical color segmentation by Biancardini [27]. These methods show good results but are computationally very expensive.…”

شبیه‌سازی بهره‌وری با استفاده از یک پارچه‌سازی رویکردهای شبیه‌سازی پویایی سیستم و مدل سازی عامل‌محور

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