Abstract : Composite construction is now well-recognized in the building industry for its aesthetically pleasing, high strengths and cost-effective properties. In structural engineering, composite construction exists when two different materials were bound to act as a single unit employing composite action. Columns are vertically oriented structural components that primarily carry compression load. The columns formed from single steel skin with concrete placed inside are considered composite columns. The present research aims to validate composite columns of circular tubular sections cast with various grades of concrete such as M25, M35, M45 and M60. The steel tubes considered in this study are of grades Fe350, Fe415 and Fe500. The circular sections are of the following dimensions, 167mm in diameter and 350mm in length. The thickness was varied as 2.5, 3.1 and 3.6mm to also study the effect of diameter to thickness ratio. Abaqus 6.14 was used in the current investigation to analyse and validate the test specimen results which are available in the literature [1]. The behavior of concrete-filled composite tubular columns cast with various grades of concrete was briefly addressed and assessed in the current research work using FEA (Finite Element Analysis)..
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Abstract : Blast resistant design of structures are very popular since past two decades. Engineering structures world-wide have a threat from terrorist attack. The stud walls can be designed for giving ample warning in terms of plastic deformation to in order to safeguard the buildings. In the present study energy dissipation of blast wave is focused. The rate of change of strain with respect to time was determined. The time period of positive blast wave is very infinitesimal, and it collapses the structure drastically. Cold-formed steel stud wall is popular in showing resistant against blasts. Two different cross section of stud were used. Influence of various parameters such as thickness, material property and mass of explosives were discussed in detail. The utilisation of channel section was briefly discussed. Theoretical equation was taken in to account. Reflected pressure is more than peak over pressure in most of the cases. Post peak behaviour of blast gives better idea to find measures to enhance the structural resistivity..
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Abstract : Concrete surface cracks are a major sign of structural safety and deterioration. Regular structure inspection and surface crack monitoring are crucial to maintaining the buildings' structural health and dependability. Human surface examination takes time and could result in erratic results because inspectors have different empirical backgrounds. Deep learning methods for visual assessment of surface cracks on civil structures have generated a lot of attention in the field of structural health monitoring. However, these vision-based algorithms depend on powerful computing resources and demand high-quality photos as inputs for image categorization. Thus, a comparison of various deep learning models is done in this work. Inception-v3 is a 48-layer deep convolutional neural network. A pretrained version of the network which has been trained on more than a million images is present in the ImageNet database. The pretrained network can categorize photos into 1000 different object categories, including several animals, a keyboard, a mouse, and a pencil. The network has therefore acquired rich feature representations for a variety of images. The network accepts images of a 299 by 299 resolution. The suggested paradigm facilitates the use of deep learning methods with low-power computing devices for trouble-free civil structure monitoring. The effectiveness of the suggested model is contrasted with that of additional well-liked deep learning methods, like VGG16 and straightforward CNN. The proposed model was determined to have a minimum computation accuracy of 99.8%. Even with a short layer stack for improved computation, a CNN architecture with better hyperparameter optimization produces higher accuracy. The evaluation findings show that the suggested method can be used with autonomous devices, including unmanned aerial vehicles, for real-time surface crack inspection with less computation..
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Abstract : Traffic forecasting has emerged as a core component of intelligent transportation systems. Traffic forecasting is crucial for public safety and resource optimization that can be modelled as saptio-temporal data. The uncertainty hinders spatio-temporal data prediction in time-series data, the existence of diverse data patterns and incompetence in accessing and accommodating spatial dynamics, causing inconsistent performance. Most recent traffic prediction works are based on deep learning models, which have applied CNN, RNN, encoder-decoder, graph-based and transformer. These approaches harness spatial and temporal features for prediction but fail to combine spatial and temporal dynamics together with generalization and high robust model capacity. In this work, we propose TrapNet, combining convolution and transformer, resulting in better generalization and higher model capacity. Convolution captures the spatial dynamics by modelling the spatial features, and the Self-Attention in the transformer captures the temporal dynamics by modelling temporal features. TrapNet has been trained and evaluated on the PEMS-BAY traffic dataset, and it has been compared with existing machine learning and deep learning techniques. The proposed model achieves higher accuracy by 1.51%, 1.23%, 2.19% from best baselines in long-term traffic prediction..
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Abstract : Long-term impacts on prestressed segmentally erected balanced cantilever bridges frequently result in greater deflections than predicted. Creep and shrinkage which occurs in the concrete deck of Prestressed Concrete (PSC) box girder bridges, can lead to a considerable redistribution of loads as well as an increase in bending moments across continuous supports, resulting in a rise in deflections. Since the cantilevers and the middle part have a peculiar static arrangement, creep deflections in this situation are constantly evolving, notably at the extremities of the cantilevers and the middle part. Traffic movement on the bridge will be made more difficult with these extreme deflections, which can lead to a collapse of the structure. In the current study, prediction models of PSC bridges using Free Cantilever Method in view of the long-term effects were proposed and discussed. The static systems of the structures were changed to achieve this goal by introducing different grades of concrete and steel tendons, which play a major role in creep, shrinkage and progressive cracking of concrete deck. Creep and shrinkage evaluations are considered as the critical factors in simulation and analysis of free cantilever bridge, particularly in the case of cast-in-place segmental bridges that demands for extensive prestressing. MIDAS Civil software was used to create 3-D finite element (FE) models of the specified bridges, which includes the implications of static and dynamic load applications, creep and ageing of concrete. It is possible to simulate construction phases, the effects of time-dependent material displacements and improvements to the bridge's structural system using the stage process approach. It is necessary to modify equations available in practice when utilising the free cantilever technique to construct bridges in order to account for time-dependent deformation and stress redistribution. Results from the current approach and numerical analysis are in good agreement with each other..
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