• Steel and Composite Structures
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• v.46 no.6
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• pp.793-804
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• 2023

In this study, cyclic loading tests were conducted to assess the seismic performance of cast-in-place (CIP) concrete-filled hollow core precast concrete columns (HPCC) constructed using steel ducts and rubber tubes. The outer shells of HPCC, with a hollow ratio of 47%, were fabricated using steel ducts and rubber tubes, respectively. Two combinations of shear studs & long threaded bars or cross-deformed bars & V-ties were employed to ensure the structural integrity of the old concrete (outer shell) and new CIP concrete. Up to a drift ratio of 3.8%, the hysteresis loop, yielding stiffness, dissipated energy, and equivalent damping ratio of the HPCC specimens were largely comparable to those of the solid columns. Besides the similarities in cyclic load-displacement responses, the strain history of the longitudinal bars and the transverse confinement of the three specimens also exhibited similar patterns. The measured maximum moment exceeded the predicted moment according to ACI 318 by more than 1.03 times. However, the load reduction of the HPCC specimen after reaching peak strength was marginally greater than that of the solid specimen. The energy dissipation and equivalent damping ratios of the HPCC specimens were 20% and 25% lower than those of the solid specimen, respectively. Taking into account the overall results, the structural behavior of HPCC specimens fabricated using steel ducts and rubber tubes is deemed comparable to that of solid columns. Furthermore, it was confirmed that the two combinations for securing structural integrity functioned as expected, and that rubber air-tubes can be effectively used to create well-shaped hollow sections.

• Journal of Platform Technology
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• v.9 no.1
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• pp.15-22
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• 2021

In a smart manufacturing environment, more and more devices are connected to the Internet so that a large volume of data can be obtained during all phases of the product life cycle. The large-scale industries, companies and organizations that have more operational units scattered among the various geographical locations face a huge resource consumption because of their unorganized structure of sharing resources among themselves that directly affects the supply chain of the corresponding concerns. Cloud-based smart manufacturing paradigm facilitates a new variety of applications and services to analyze a large volume of data and enable large-scale manufacturing collaboration. The manufacturing units include machinery that may be situated in different geological areas and process instances that are executed from different machinery data should be constantly managed by the super admin to coordinate the manufacturing process in the large-scale industries these environments make the manufacturing process a tedious work to maintain the efficiency of the production unit. The data from all these instances should be monitored to maintain the integrity of the manufacturing service system, all these data are computed in the cloud environment which leads to the latency in the performance of the smart manufacturing service system. Instead, validating data from the external device, we propose to validate the data at the front-end of each device. The validation process can be automated by script validation and then the processed data will be sent to the cloud processing and storing unit. Along with the end-device data validation we will implement the APM(Asset Performance Management) to enhance the productive functionality of the manufacturers. The manufacturing service system will be chunked into modules based on the functionalities of the machines and process instances corresponding to the time schedules of the respective machines. On breaking the whole system into chunks of modules and further divisions as required we can reduce the data loss or data mismatch due to the processing of data from the instances that may be down for maintenance or malfunction ties of the machinery. This will help the admin to trace the individual domains of the smart manufacturing service system that needs attention for error recovery among the various process instances from different machines that operate on the various conditions. This helps in reducing the latency, which in turn increases the efficiency of the whole system