The subject of progressive collapse assessment in RC structures has been concerned in various studies. In contrast, most of these studies have attended to a scenario in which an instantaneous removal of a column due to unexpected impact or explosion has occurred. The present study addresses progressive collapse in RC structures resulting from both instantaneous and gradual removal of columns. The scenario for a gradual removal is the result of slow decreasing strength due to fire propagation in a specific zone of structure which is partially fire-proved. A five-story building model was selected as a case study for which the nonlinear model is later developed. Vertical displacement in the upper node of the removed column, redistribution of forces after removing the column, plastic deformations in adjoining elements, and the stress imposed on the sections of the beams adjacent to the removed column in both instantaneous and gradual cases are studied.

Chloride-induced corrosion of concrete structures in marine areas is a serious problem and is generally affected by several factors. Chloride concentration is an important parameter for estimating the corrosion state of concrete. In this research, first chloride concentration at various depths of concrete specimens was measured using the accelerated chloride penetration test method under laboratory conditions, simulating a marine environment after 4·5 and 9 months. Then the obtained experimental dataset of 162 in 9 months of exposure was used to develop classification and regression trees (CARTs) and an artificial neural network (ANN) as subsets of artificial intelligence methods. Environmental condition, penetration depth, water-to-cementitious material ratio and silica fume mass were considered as input parameters, and chloride concentration was taken as the output parameter. Finally, results for the two methods were compared with the experimental observations to evaluate their accuracy in phases of training and testing. As a further aspect to the study, prediction of chloride concentration as a function of the exposure time and unavailable testing parameters was carried out. The results showed that ANN and CART have good ability and accuracy for predicting the chloride concentration in concrete under marine environment conditions. In the present research, the ANN method showed more accuracy.

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This paper introduces a new framework for reliability based design optimization (RBDO) of the reinforced concrete (RC) frames. This framework is constructed based on the genetic algorithm (GA) and finite element reliability analysis (FERA) to optimize the frame weight by selecting appropriate sections for structural elements under deterministic and probabilistic constraints. Modulus of elasticity of the concrete and steel bar, dead load, live load, and earthquake equivalent load are considered as random variables. Deterministic constraints include the code design requirements that must be satisfied for all the frame elements according to the nominal values of the aforementioned random variables. On the other hand, this framework provides the minimum required reliability index as the probabilistic constraint. The first-order reliability method (FORM) using the Newton-type recursive relationship will be used to compute the reliability index. The maximum inter-story drift is considered as an engineering demand parameter to define the limit-state function in FORM analysis. To implement the proposed framework, a mid-rise five-story RC frame is selected as an example. Based on the analysis results, increasing the minimum reliability index from 6 to 7 causes an 11 % increase in the weight of the selected RC frame as an objective function. So, we can obtain a trade-off between the optimized frame weight and the required reliability index utilizing the developed framework. Furthermore, the high values of the reliability index for the frame demonstrate the conservative nature of code requirements for interstory drift limitations based on the linear static analysis method.