In the construction industry, research is being carried out to look for feasible methods and technologies to cut down project costs and waste. Building Information Modelling (BIM) is certainly currently a promising technology/method that can achieve this. The output of the construction industry has a considerable scale; however, the concentration of the industry and the level of informatization are still not high. There is still a large gap in terms of productivity between the construction industry and other industries. Due to the lack of first-hand data regarding how much of an effect can be genuinely had by BIM in real cases, it is unrealistic for construction stakeholders to take the risk of widely adopting BIM. This paper focuses on the methodological quantification (through a case study approach) of BIM's benefits in building construction resource management and real-time costs control, in contrast to traditional non-BIM technologies. Through the use of BIM technology for the dynamic querying and statistical analysis of construction schedules, engineering, resources and costs, the three implementations considered demonstrate how BIM can facilitate the comprehensive grasp of a project's implementation and progress, identify and solve the contradictions and conflicts between construction resources and costs controls, reduce project over-spends and protect the supply of resources.
Artificial Neural Networks (ANNs) trained on specific cognitive tasks have re-emerged as a useful tool to study the brain. However, ANNs would better aid cognitive neuroscience if a given network could be easily trained on a wide range of tasks for which neural recordings are available. Moreover, unintentional divergent implementations of cognitive tasks can produce variable results, which limits their interpretability. Towards this goal, we present NeuroGym, an open-source Python package that provides a large collection of customizable neuroscience tasks to test and compare network models. Building upon the OpenAI Gym toolbox, NeuroGym tasks (1) are written in a high-level flexible Python framework; (2) possess a shared interface tailored to common needs of neuroscience tasks that facilitates their design and usage; (3) support the training of ANNs using both Reinforcement and Supervised Learning techniques. The toolbox allows easy assembly of new tasks by modifying existing ones in a hierarchical and modular fashion. These design features make it straightforward to take a network designed for one task and train it on many other tasks. NeuroGym is a community-driven effort that contributes to a rapidly evolving open ecosystem of neural network development, data analysis, and model-data comparison.
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