SC 합성 바닥판의 동적 거동

Sandeep Chaudhary, Kashyap Arvindbhai Patel, 김두기(Dookie Kim),조성국(Sung Gook Cho),Ahmer Ali 한국구조물진단유지관리학회 2011 한국구조물진단학회 학술발표회논문집 Vol.15 No.1

Studies are presented for the dynamic behaviour of steel-concrete composite floors. A typical two spans composite floor is considered for this study and assumed to be subjected to a typical earthquake. The deflection and stresses are obtained at different time instants for the composite floor with rigid connection and flexible connection. The FE model developed, in ABAQUS, for the study accounts for the nonlinear material behaviour. The flexibility of shear connectors between steel girder and concrete slab is found to significantly affect the dynamic behaviour of steel-concrete composite floors.

Rapid prediction of long-term deflections in composite frames

Umesh Pendharkar,Sandeep Chaudhary,K. A. Patel,A.K. Nagpal 국제구조공학회 2015 Steel and Composite Structures, An International J Vol.18 No.3

Deflection in a beam of a composite frame is a serviceability design criterion. This paper presents a methodology for rapid prediction of long-term mid-span deflections of beams in composite frames subjected to service load. Neural networks have been developed to predict the inelastic mid-span deflections in beams of frames (typically for 20 years, considering cracking, and time effects, i.e., creep and shrinkage in concrete) from the elastic moments and elastic mid-span deflections (neglecting cracking, and time effects). These models can be used for frames with any number of bays and stories. The training, validating, and testing data sets for the neural networks are generated using a hybrid analytical-numerical procedure of analysis. Multilayered feed-forward networks have been developed using sigmoid function as an activation function and the back propagation-learning algorithm for training. The proposed neural networks are validated for an example frame of different number of spans and stories and the errors are shown to be small. Sensitivity studies are carried out using the developed neural networks. These studies show the influence of variations of input parameters on the output parameter. The neural networks can be used in every day design as they enable rapid prediction of inelastic mid-span deflections with reasonable accuracy for practical purposes and require computational effort which is a fraction of that required for the available methods.

Prediction of moments in composite frames considering cracking and time effects using neural network models

Umesh Pendharkar,Sandeep Chaudhary,A.K. Nagpal 국제구조공학회 2011 Structural Engineering and Mechanics, An Int'l Jou Vol.39 No.2

There can be a significant amount of moment redistribution in composite frames consisting of steel columns and composite beams, due to cracking, creep and shrinkage of concrete. Considerable amount of computational effort is required for taking into account these effects for large composite frames. A methodology has been presented in this paper for taking into account these effects. In the methodology that has been demonstrated for moderately high frames, neural network models are developed for rapid prediction of the inelastic moments (typically for 20 years, considering instantaneous cracking, and time effects, i.e., creep and shrinkage, in concrete) at a joint in a frame from the elastic moments (neglecting instantaneous cracking and time effects). The proposed models predict the inelastic moment ratios (ratio of elastic moment to inelastic moment) using eleven input parameters for interior joints and seven input parameters for exterior joints. The training and testing data sets are generated using a hybrid procedure developed by the authors. The neural network models have been validated for frames of different number of spans and storeys. The models drastically reduce the computational effort and predict the inelastic moments, with reasonable accuracy for practical purposes, from the elastic moments, that can be obtained from any of the readily available software.

Prediction of moments in composite frames considering cracking and time effects using neural network models

Pendharkar, Umesh,Chaudhary, Sandeep,Nagpal, A.K. Techno-Press 2011 Structural Engineering and Mechanics, An Int'l Jou Vol.39 No.2

There can be a significant amount of moment redistribution in composite frames consisting of steel columns and composite beams, due to cracking, creep and shrinkage of concrete. Considerable amount of computational effort is required for taking into account these effects for large composite frames. A methodology has been presented in this paper for taking into account these effects. In the methodology that has been demonstrated for moderately high frames, neural network models are developed for rapid prediction of the inelastic moments (typically for 20 years, considering instantaneous cracking, and time effects, i.e., creep and shrinkage, in concrete) at a joint in a frame from the elastic moments (neglecting instantaneous cracking and time effects). The proposed models predict the inelastic moment ratios (ratio of elastic moment to inelastic moment) using eleven input parameters for interior joints and seven input parameters for exterior joints. The training and testing data sets are generated using a hybrid procedure developed by the authors. The neural network models have been validated for frames of different number of spans and storeys. The models drastically reduce the computational effort and predict the inelastic moments, with reasonable accuracy for practical purposes, from the elastic moments, that can be obtained from any of the readily available software.

Neural networks for inelastic mid-span deflections in continuous composite beams

Umesh Pendharkar,Sandeep Chaudhary,A.K. Nagpal 국제구조공학회 2010 Structural Engineering and Mechanics, An Int'l Jou Vol.36 No.2

Maximum deflection in a beam is a design criteria and occurs generally at or close to the mid-span. Neural networks have been developed for the continuous composite beams to predict the inelastic mid-span deflections (typically for 20 years, considering cracking, and time effects, i.e., creep and shrinkage, in concrete) from the elastic moments and elastic mid-span deflections (neglecting instantaneous cracking and time effects). The training and testing data for the neural networks is generated using a hybrid analytical-numerical procedure of analysis. The neural networks have been validated for four example beams and the errors are shown to be small. This methodology, of using networks enables a rapid estimation of inelastic mid-span deflections and requires a computational effort almost equal to that required for the simple elastic analysis. The neural networks can be extended for the composite building frames that would result in huge saving in computational time.

Neural networks for inelastic mid-span deflections in continuous composite beams

Pendharkar, Umesh,Chaudhary, Sandeep,Nagpal, A.K. Techno-Press 2010 Structural Engineering and Mechanics, An Int'l Jou Vol.36 No.2

Maximum deflection in a beam is a design criteria and occurs generally at or close to the mid-span. Neural networks have been developed for the continuous composite beams to predict the inelastic mid-span deflections (typically for 20 years, considering cracking, and time effects, i.e., creep and shrinkage, in concrete) from the elastic moments and elastic mid-span deflections (neglecting instantaneous cracking and time effects). The training and testing data for the neural networks is generated using a hybrid analytical-numerical procedure of analysis. The neural networks have been validated for four example beams and the errors are shown to be small. This methodology, of using networks enables a rapid estimation of inelastic mid-span deflections and requires a computational effort almost equal to that required for the simple elastic analysis. The neural networks can be extended for the composite building frames that would result in huge saving in computational time.

A probabilistic capacity spectrum strategy for the reliability analysis of bridge pile shafts considering soil structure interaction

Kim, Dookie,Chaudhary, Sandeep,Nocete, Charito Fe,Wang, Feng,Lee, Do Hyung s.n.] 2011 Latin american journal of solids and structures Vol.8 No.3

<P> This paper presents a probabilistic capacity spectrum strategy for the reliability analysis of a bridge pile shaft, accounting for uncertainties in design factors in the analysis and the soil-structure interaction (SSI). Monte Carlo simulation method (MCS) is adopted to determine the probabilities of failure by comparing the responses with defined limit states. The analysis considers the soil structure interaction together with the probabilistic application of the capacity spectrum method for different types of limit states. A cast-in-drilledhole (CIDH) extended reinforced concrete pile shaft of a bridge is analysed using the proposed strategy. The results of the analysis show that the SSI can lead to increase or decrease of the structure’s probability of failure depending on the definition of the limit states. </P>

Osteogenic Nanofibrous Coated Titanium Implant Results in Enhanced Osseointegration: In Vivo Preliminary Study in a Rabbit Model

Siddhartha Das,Sandeep Gurav,Vivek Soni,Arvind Ingle,Bhabani S. Mohanty,Pradip Chaudhari,Kiran Bendale,Kanchan Dholam,Jayesh R. Bellare 한국조직공학과 재생의학회 2018 조직공학과 재생의학 Vol.15 No.2

A titanium implant surface when coated with biodegradable, highly porous, osteogenic nanofibrous coating has shown enhanced intrinsic osteoinductive and osteoconductive properties. This coating mimics extracellular matrix resulting in differentiation of stem cells present in the peri-implant niche to osteoblast and hence results in enhanced osseointegration of the implant. The osteogenic nanofibrous coating (ONFC) consists of poly-caprolactone, gelatin, nano- sized hydroxyapatite, dexamethasone, ascorbic acid and beta-glycerophosphate. ONFC exhibits optimum mechanical properties to support mesenchymal stem cells and steer their osteogenic differentiation. ONFC was subjected to various characterization tests like scanning electron microscopy, Fourier-transform infrared spectroscopy, x-ray diffractometry, thermal degradation, biomineralization, mechanical properties, wettability and proliferation assay. In pre-clinical animal trials, the coated implant showed enhanced new bone formation when placed in the tibia of rabbit. This novel approach toward implant bone integration holds significant promise for its easy and economical coating thus marking the beginning of new era of electrospun osteogenic nanofibrous coated bone implants.

Influence of granite waste aggregate on properties of binary blend self-compacting concrete

Jain, Abhishek,Gupta, Rajesh,Chaudhary, Sandeep Techno-Press 2020 Advances in concrete construction Vol.10 No.2

This study explores the feasibility of granite waste aggregate (GWA) as a partial replacement of natural fine aggregate (NFA) in binary blend self-compacting concrete (SCC) prepared with fly ash. Total of nine SCC mixtures were prepared wherein one was Ordinary Portland cement (OPC) based control SCC mixture and remaining were fly ash based binary blend SCC mixtures which included the various percentages of GWA. Fresh properties tests such as slump flow, T₅₀₀, V-funnel, J-ring, L-box, U-box, segregation resistance, bleeding, fresh density, and loss of slump flow (with time) were conducted. Compressive strength and percentage of permeable voids were evaluated in the hardened state. All the SCC mixtures exhibited sufficient flowability, passing ability, and resistance to segregation. Besides, all the binary blend SCC mixtures exhibited lower fresh density and bleeding, and better residual slump (up to 50% of GWA) compared to the OPC based control SCC mixture. Binary blend SCC mixture incorporating up to 40% GWA provided higher compressive strength than binary blend control SCC mixture. The findings of this study encourage the utilization of GWA in the development of binary blend SCC mixtures with satisfactory workability characteristics as a replacement of NFA.

Rapid prediction of inelastic bending moments in RC beams considering cracking

K. A. Patel,Sandeep Chaudhary,A.K. Nagpal 사단법인 한국계산역학회 2016 Computers and Concrete, An International Journal Vol.18 No.6

A methodology using neural networks has been proposed for rapid prediction of inelastic bending moments in reinforced concrete continuous beams subjected to service load. The closed form expressions obtained from the trained neural networks take into account cracking in concrete at in-span and at near the internal supports and tension stiffening effect. The expressions predict the inelastic moments (considering the concrete cracking) from the elastic moments (neglecting the concrete cracking) at supports. Three separate neural networks are trained since these have been postulated to represent all the beams having any number of spans. The training, validating, and testing data sets for the neural networks are generated using an analytical-numerical procedure of analysis. The proposed expressions are verified for example beams of different number of spans and cross-section properties and the errors are found to be small. The proposed expressions, at minimal input data and computation effort, yield results that are close to FEM results. The expressions can be used in preliminary every day design as they enable a rapid prediction of inelastic moments and require a computational effort that is a fraction of that required for the available methods in literature.