X-force 2015 is a software for cracking autodesk products quickly and accurately does not take much of your time. The user is very easy, I will guide below or in the software, there are video tutorials installed most of the same.
When designing a reinforced concrete structure, engineers must check limit deflections, especially on beams and floors. This is achieved by taking into account the cracked inertia of the reinforced concrete section. However, in FEM calculation, this leads to specific modelling and iterative processes which are tedious and time-consuming tasks for engineers.Advance Design 2015 now automatically performs concrete deflection calculation based on theoretical or real reinforcement computed by the software.
autodesk advance concrete 2015 crack
Abstract:Bridge inspection using unmanned aerial vehicles (UAV) with high performance vision sensors has received considerable attention due to its safety and reliability. As bridges become obsolete, the number of bridges that need to be inspected increases, and they require much maintenance cost. Therefore, a bridge inspection method based on UAV with vision sensors is proposed as one of the promising strategies to maintain bridges. In this paper, a crack identification method by using a commercial UAV with a high resolution vision sensor is investigated in an aging concrete bridge. First, a point cloud-based background model is generated in the preliminary flight. Then, cracks on the structural surface are detected with the deep learning algorithm, and their thickness and length are calculated. In the deep learning method, region with convolutional neural networks (R-CNN)-based transfer learning is applied. As a result, a new network for the 384 collected crack images of 256 256 pixel resolution is generated from the pre-trained network. A field test is conducted to verify the proposed approach, and the experimental results proved that the UAV-based bridge inspection is effective at identifying and quantifying the cracks on the structures.Keywords: crack identification; deep learning; unmanned aerial vehicle (UAV); computer vision; spatial information 2ff7e9595c
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