Uncertainty modelling of critical column buckling for reinforced concrete buildings

Buckling is a critical issue for structural stability in structural design. In most of the buckling analyses, applied loads, structural and material properties are considered certain. However, in reality, these parameters are uncertain. Therefore, a prognostic solution is necessary and uncertainties have to be considered. Fuzzy logic algorithms can be a solution to generate more dependable results. This study investigates the material uncertainties on column design and proposes an uncertainty model for critical column buckling reinforced concrete buildings. Fuzzy logic algorithm was employed in the study. Lower and upper bounds of elastic modulus representing material properties were defined to take uncertainties into account. The results show that uncertainties play an important role in stability analyses and should be considered in the design. The proposed approach is applicable to both future numerical and experimental researches. According to the study results, it is seen that, calculated buckling load values are stayed in lower and upper bounds while the load values are different for same concrete strength values by using different code formula.     

A modification of Morrow and Smith–Watson–Topper mean stress correction models

A modification of the Morrow and the Smith, Watson and Topper (SWT) mean stress correction models is proposed to account for the mean stress effect on fatigue life. The capability and accuracy of the proposed model are compared to those of the original Morrow and the SWT model using published mean stress fatigue test data. The proposed mean stress correction model was found to be superior to both the SWT and the Morrow model in the case of the Incoloy 901 superalloy and the ASTM A723 steel. On the other hand both the proposed and the original SWT model provided equally good correlation with experimental data in the case of 7075‐T561 aluminium alloy and 1045 HRC 55 steel. The Morrow model was found to give the least accurate predictions for all four materials analysed.     

Maximum likelihood classification,optimal or problematic? A comparison with the nearest neighbour classification

Abstract

The maximum likelihood and the nearest neighbour classification algorithms are reviewed, particularly from the point of view of user/analyst requirements. The two algorithms were put to use for the classification or Landsat TM data of agricultural scenes and accuracy with respect to ‘ground truth’ was evaluated using different parametric settings. Results show that within the maximum likelihood classification, accuracies and errors can vary to a considerable degree depending on the formation of the statistical classes from the training data. More interestingly, it was found that the nearest neighbour algorithm produced higher accuracies and was judged to be more robust, but it has computer implementation problems with high data dimensionality.     

Artificial neural network‐based analysis of effective crack model in concrete fracture

Many non‐linear fracture models have been proposed by design codes and investigators to determine fracture parameters of cement‐based materials. To characterise failure of concrete structures, the effective crack model (ECM) needs two fracture parameters: the effective crack length a_e and the critical stress intensity factor . Nevertheless, ECM requires a closed‐loop testing system and the calculation of a_e needs considerable computational effort. For this reason, ECM is simulated with an artificial neural network (ANN) in this study. The main benefit of using an ANN approach is that the network is built directly on experimental data by using the self‐organizing capabilities of the ANN. The presented fracture model was developed by utilising 464 noisy test data taken from the literature, which were obtained via different test methods in different laboratories. The results of an ANN‐based ECM look viable and very promising.