12 research outputs found
Drop-weight testing of slender reinforced concrete beams
No full textThe work presented herein sets out to investigate experimentally, via drop-weight testing, the behavior of slender reinforced concrete (RC) beam specimens under impact loading. During testing, the behavior of each specimen is established through the combined use of conventional instrumentation and a high-speed video camera. The primary objective of this work is to investigate the reasons that trigger the observed shift in specimen behavior (compared to that established from static tests) with increasing levels of applied loading rate and intensity. Analysis of the test data reveals that during drop-weight testing only a portion of the element span reacts to the applied load (as indicated by the deformation and cracking profiles recorded) which in turn affects the mechanics underlying specimen behavior and therefore, significantly influencing the mode of failure ultimately exhibited. The observed localized response becomes more prominent by increasing the loading rate and intensity of the imposed impact loading. In addition to the above, the strain-rate sensitivity of the material properties of concrete does not appear to have a significant effect on the behavior of the specimens tested. The aforementioned observations appear to be in conflict with current design practice raising questions concerning the effectives of the design solutions produced.</p
Reliability Analysis of RC Code for Predicting Load-Carrying Capacity of RCC Walls Through ANN
No full textOver the past couple of decades, a significant rise in utilization of artificial neural network (ANN) in the field of civil engineering has been observed. ANNs have been proven to be very helpful for researchers working in concrete technology. Reinforced cement concrete (RCC) shear walls play an important role in the stability of high-rise reinforced concrete structures. Current study is focused on using ANN-based design technique as an alternative to conventional design codes and physical models to estimate the ultimate load carrying capacity of RCC shear walls. In this study, database of 95 RCC wall samples has been collected from previously published literature. Various critical parameters considered for current research are; length of web portion of the wall (Lw), thickness of wall boundary member (bw), effective depth of wall (d), height of wall (H), shear span ratio (av/d), vertical steel ratio (ρv), horizontal steel ratio (ρh), yield strength of vertical and horizontal steel (fy), compressive strength of concrete (fc), and the ultimate load carrying capacity (Vexp)
Reliability analysis of models for predicting T-beam response at ultimate limit response
No full textThe aim of the present paper is to compare the current design codes' predictions for reinforced concrete (RC) T-beams with the alternative physical methods - namely, the compressive force path (CFP) method and artificial neural networks (ANNs). Therefore, two databases, for T-beams without stirrups and with stirrups, are developed using the available experimental studies. The comparative study on prediction (obtained from the American Concrete Institute and Eurocode 2, CFP and ANN models) shows that the predictions of the ANN model provide a closer fit to the experimental results; after ANN the predictions of the CFP method are close to the experimental results when compared with the counterpart physical model. Comparative studies are also conducted on the critical parameters for the behaviour of RC T-beams. Furthermore, a non-linear finite-element tool (i.e. Abaqus) is used to validate the prediction of the ANN and CFP model. The crack pattern from Abaqus exhibited the same mechanics, on which the CFP models are based. </p
Shear failure criterion for RC T-beams
Full text linkThe paper is concerned with the development of a failure criterion capable of accurately predicting the shear capacity of reinforced concrete T-beams while correctly accounting for the beneficial effect of the increase of the compressive zone due to the presence of flanges. The development of the subject criterion is based on an alternative design method (the compressive force path method) that leads to predictions of reinforced concrete structural behaviour and design solutions considerably different compared to those of the current design codes without however compromising structural performance requirements (mainly associated with ductility and strength). The validity of the proposed failure criterion is verified through a comparative study of the calculated values with their experimentally-established counterparts obtained from an extensive literature survey. Through this comparative study it is demonstrated that the predictions of the proposed criterion provide a closer fit to the available experimental data than their counterparts obtained from the design codes considered
Analytical modeling of corroded RC columns considering flexure-shear interaction for seismic performance assessment
Full text linkCorrosion of transverse reinforcement can lead to significant shear capacity deterioration resulting in a ductile-designed reinforced concrete (RC) column to potentially fail in a shear manner. Increased risk of shear failure can be more significant for short shear-critical columns when corrosion occurs. Therefore, shear response can be a particularly important issue in seismic performance assessment of corroded RC columns. An efficient analytical model which can capture the shear capacity deterioration due to corrosion and the flexure-shear interaction behaviors of corroded columns is developed in this work. Corrosion effect on flexural behavior is accounted for through the appropriate modification of steel reinforcement, concrete and bond properties. Shear response is simulated by a new macro zero-length shear spring element, and its shear force-shear deformation relationship is modelled with the Ibarra-Medina-Krawinkler deterioration model that can capture strength and stiffness deterioration as well as pinching behavior. A calibration study is carried out based on a collection of experiments of corroded columns failed in shear. Subsequently empirical formulae for the determination of the modeling parameters of the shear spring are proposed. The proposed model is validated by simulating several corroded columns and compering the predictions obtained with the relevant test data. Results show that the proposed model is able to predict reasonably the overall hysteretic behavior. Corrosion effects on the seismic performance of RC columns are investigated with the proposed model. Results demonstrate that the flexure-shear interaction behaviors should be considered for seismic performance assessment of corroded columns.</p
Assessing the behaviour of subsea pipes under impact
No full textSubsea steel pipes are often used to form extensive networks for transporting oil and gas over large distances. Such pipes can potentially be subjected to actions characterized by high loading rates and intensities stemming from accidental loads, due to high-mass low-velocity impacts. In order to ensure that such networks can continue to operate even after being subjected to such extreme loading conditions, it is essential that the behavior of the pipes is characterized by a certain level of resilience. The present study aims to investigate numerically, via dynamic nonlinear finite element analysis, the effect of various parameters on the dynamic response exhibited by pipes under impact loading. The considered parameters are associated with the impacting object, the geometry and the support conditions of the pipes, the level of axial loading as well as the internal and the external pressures imposed onto the walls of the pipes. The numerical predictions, which are validated against relevant published test data, reveal that the above parameters, which are associated with the in-situ conditions imposed onto subsea pipes throughout their operational life, can significantly affect, often detrimentally, the behavior exhibited under impact loading. Existing assessment methods employed in practice for predicting the level of damage sustained by pipes during impact do not accurately consider the effect of the above parameters. As a result, questions are raised concerning the ability of such guidelines to realistically predict the level of damage sustained by subsea pipes under impact.</p
Framework for the development of artificial neural networks for predicting the load carrying capacity of RC members
No full textThis paper aims at establishing a framework for the development of artificial neural networks (ANNs) capable of realistically predicting the load-carrying capacity of reinforced concrete (RC) members. Multilayer back propagation neural networks are developed through the use of MATLAB and enriched databases which contain information describing the variation of load-carrying capacity in relation to key design parameters associated with the RC specimens (i.e. beams) considered. This work forms the basis for the development of a knowledge-based structural analysis tool capable of predicting RC structural response. A detailed discussion is provided on the different aspects of the proposed framework which include (1) the formation and analysis of the relevant (experimental) data, (2) the architecture of the ANNs, (3) the training/calibration process they undergo and finally, (4) ways of extending their applicability enabling them to predict the behaviour of RC structural forms with design parameters not represented in the available experimental database. Non-linear finite element analysis is used for validating the predictions of the ANN models developed. The comparative study reveals that the ANN models developed through the proposed framework are capable of effectively predicting the load-carrying capacity s of the RC structural forms considered quickly, accurately and without requiring significant computational resources.</p
Assessment of RC exterior beam-column Joints based on artificial neural networks and other methods
No full textA database on the behaviour of reinforced concrete external beam-column joint sub-assemblages established from the results of over 150 tests is developed and used for the development, training and validation of an artificial neural network (ANN) based model. The ANN model predictions on the mode of failure and load-carrying capacity of the joints, together with the predictions of widely used code methods and those of a recently proposed method, which does not require calibration through the use of test data, are compared with their counterparts stored in the database developed herein. The comparison confirms the already reported shortcomings of current code methods and demonstrates that both ANN model and the recently proposed method can provide reliable alternatives to the code methods
Seismic fragility analysis of shear-critical concrete columns considering corrosion induced deterioration effects
Full text linkShear-critical reinforced concrete structures such as older columns with insufficient transverse reinforcement details or short columns are found to be vulnerable to earthquake loading. Meanwhile, in the aggressive environment, RC structures tend to be more vulnerable to earthquake since corrosion of reinforcements will cause deterioration of the material properties. In the present study, a new framework is proposed for seismic fragility analysis of shear-critical structures with the consideration of corrosion effects. A new model for corroded concrete columns is proposed which can account for shear performance deterioration due to corrosion and the seismic flexure-shear interaction (FSI) behaviors. The modified Ibarra-Medina-Krawinkler deterioration model is adopted to simulate the shear response in order to capture shear strength and stiffness deterioration as well as pinching behavior of corroded shear-critical columns. The deteriorating material properties are determined based on corrosion modeling methods, and the corrosion level differences between transverse and longitudinal reinforcement are addressed. Furthermore, the proposed framework adopts time-variant structural capacities as obtained from the proposed numerical model in the fragility analysis. The developed framework is demonstrated with a shear-critical bridge column. The results clearly indicate the adverse effects of corrosion on seismic fragility of shear-critical columns, especially at severer damage states. Using flexure model and time-invariant capacity index will underestimate seismic fragility compared with the results obtained using the proposed method
Prediction of response of RC multi-span beams through a proposed ANN–FEA model
No full textPredicting the brittle response of reinforced concrete (RC) members is a complex challenge. Different industries and tools provide varying accuracy and analysis times, and advanced finite-element (FE) tools such as Abaqus, Ansys and Diana require high computational costs and expertise. To overcome these issues without extra computational cost, a method combining an artificial neural network (ANN) and finite-element analysis (FEA) method is proposed. The proposed method was designed for the analysis and design of both new and existing RC structures, including multi-span beams. In this study, two experimental control model beams (CM-0 and CM-180) and four new FE models (models with half-diameter stirrups (HDN-0 and HDN-180) and models with double spacing of stirrups (DSN-0 and DSN-180)) were examined, where 0 and 180 represent the values of axial loads (in kN). The analysis assessed the impact of critical design parameters, specifically the transverse reinforcement ratio, on the load-carrying capacity of multi-span beams, particularly in brittle conditions. The results showed that the ANN–FEA model closely aligned with the experimental values and Abaqus results for the control models. For the other four models, both the ANN–FEA and Abaqus yielded similar results, while SAP2000 displayed uniform values regardless of the stirrup arrangements
