Modelling Of Compressive Arch And Catenary Actions In Rc Frame Structures

Progressive collapse of reinforced concrete(RC)structures has attracted much attention in the past two decades due to the devastating effects associated with it.In this thesis,a systematic review of the failure,resisting mechanism,and the factors affecting progressive collapse resistance of RC frame structures has been presented.Compressive arch action(CAA)and catenary action(CA)are recognized to be the two major load redistribution mechanisms against progressive collapse in RC beam-column substructures at small and large deformations.Two key design parameters,i.e.,span-todepth ratio and longitudinal reinforcement ratio are analyzed,and numerical equations for these parameters are developed on the basis of the data collected from literature.An energy absorption index is also proposed for evaluating progressive collapse resistance of RC beam-column and beam-slab substructures.Further,two models for the CAA capacity of RC beam-column substructures are proposed:(i)a prediction model by using state-of-the-art artificial intelligence technique of gene expression programming(GEP);(ii)a semi-analytical model for high strength concrete(HSC)by modifying classical Park and Gamble model,which is based on plastic theory.A prediction model for the CA capacity of RC beam-column substructures is also proposed by utilizing the GEP technique.For the three proposed models,comprehensive and reliable databases are established via a comprehensive literature review.The influencing parameters that enter the GEP algorithm were identified based on the source research,which also provided their values.The performance of the proposed GEP-based models for CAA and CA is assessed by using various parameters such as objective function,performance index,and various statistical measures.Similarly,the validity and accuracy of the proposed models are assessed by parametric analysis and external validation criteria available in literature,which showed that the models represented the considered systems very well.Sensitivity/variable importance analysis is also conducted to show the contribution of each input parameter in the model development.The models are also validated on independent experimental tests results,which were not used in the development stages.Since there are no prediction models available for CAA and CA,the efficiency of the GEP-based models is shown by comparison with prevalent machine learning algorithms.The proposed GEP-based models demonstrated superior performance when compared and demonstrated high prediction and generalization capabilities.The CAA and CA capacities of RC beam-column substructures can be easily calculated by the proposed simplified mathematical equations.The proposed semi-analytical model for CAA advances a new method of assessing the CAA capacity of RC beams under the action of column removal is based on the classical model developed by Park and Gamble.An extended method of simulating the distribution of stresses in both normal and high strength concrete(NSC and HSC)is proposed in the model based on analytical calculations.While a more comprehensive approach for the prediction of the beam deflection associated with the maximum compressive arching capacity is devised by utilizing the GEP algorithm,which simplifies the computational process by replacing iterative calculations with a closed-form solution.The comparative study showed that the proposed method produced accurate results when compared with the experimental data and other models present in literature.Also,the proposed model outperforms the other models when it was explicitly used to calculate the CAA resistance of HSC specimens.The method can give a great insight into the practicality of using CAA as a robustness tool.