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The use of three artificial neural network (ANN)-based models for the prediction of unconfined compressive strength (UCS) of granite using three non-destructive test indicators, namely pulse velocity, Schmidt hammer rebound number, and effective porosity, has been investigated in this study. For this purpose, a sum of 274 datasets was compiled and used to train and validate three ANN models including ANN constructed using Levenberg–Marquardt algorithm (ANN-LM), a combination of ANN and particle swarm optimization (ANN-PSO), and a combination of ANN and imperialist competitive algorithm (ANN-ICA). The constructed ANN-LM model was proven to be the most accurate based on experimental findings. In the validation phase, the ANN-LM model has achieved the best predictive performance with R = 0.9607 and RMSE = 14.8272. Experimental results show that the developed ANN-LM outperforms a number of existing models available in the literature. Furthermore, a Graphical User Interface (GUI) has been developed which can be readily used to estimate the UCS of granite through the ANN-LM model. The developed GUI is made available as a supplementary material.

This work aims to develop a novel and practical equation for predicting the axial load of rectangular concrete-filled steel tubular (CFST) columns based on soft computing techniques. More precisely, a dataset containing 880 experimental tests was first collected from the available literature for the development of an artificial neural network (ANN) model. An optimization strategy was conducted to obtain a final set of ANN’s architecture as well as its weight and bias parameters. The performance of the developed ANN was then compared to current codes (AS, EN, AIJ, ACI, AISC, LRFD, and DBJ) and existing empirical equations. The accuracy of the present model was found superior to the results obtained by others when predicting the axial load of rectangular CFST columns. For practical application, an explicit equation and an Excel-based Graphical User Interface were derived based on the ANN model. The graphical user interface is provided freely for all interested users, to support the design, teaching, and interpretation of the axial behavior of CFST columns.

In this study, a model for the estimation of the compressive strength of concretes incorporating metakaolin is developed and parametrically evaluated, using soft computing techniques. Metakaolin is a component extensively employed in recent decades as a means to reduce the requirement for cement in concrete. For the proposed models, six parameters are accounted for as input data. These are the age at testing, the metakaolin percentage in relation to the total binder, the water-to-binder ratio, the percentage of superplasticizer, the binder to sand ratio and the coarse to fine aggregate ratio. For training and verification of the developed models a database of 867 experimental specimens has been compiled, following a broad survey of the relevant published literature. A robust evaluation process has been utilized for the selection of the optimum model, which manages to estimate the concrete compressive strength, accounting for metakaolin usage, with remarkable accuracy. Using the developed model, a number of diagrams is produced that reveal the highly non-linear influence of mix components to the resulting concrete compressive strength.

In this paper an Artificial Neural Network (ANN) model is developed for the prediction of the ultimate compressive load of rectangular Concrete Filled Steel Tube (CFST) columns, taking into account load eccentricity. To this end, an experimental database of CFST specimens from the literature has been compiled, totaling 1224 individual tests, both under concentric and under eccentric loading. Except for eccentricity, other parameters taken into consideration include the cross section width, height and thickness, the steel yield limit, the concrete strength and the column length. Both short and long specimens were evaluated. The architecture of the proposed ANN model was optimally selected, according to predefined performance metrics. The developed model was then compared against available design codes. It was found that its accuracy was significantly improved while maintaining a stable numerical behavior. The explicit equation that describes mathematically the ANN is offered in the paper, for easier implementation and evaluation purposes.

Masonry is a building material that has been used in the last 10.000 years and remains competitive today for the building industry. The compressive strength of masonry is used in modern design not only for gravitational and lateral loading, but also for quality control of materials and execution. Given the large variations of geometry of units and joint thickness, materials and building practices, it is not feasible to test all possible combinations. Many researchers tried to provide relations to estimate the compressive strength of masonry from the constit-uents, which remains a challenge. Similarly, modern design codes provide lower bound solutions, which have been demonstrated to be weakly correlated to observed test results in many cases. The present paper adopts soft-computing techniques to address this problem and a dataset with 401 specimens is considered. The obtained results allow to identify the most relevant parameters affecting masonry compressive strength, areas in which more experimental research is needed and expressions providing better estimates when compared to formulas existing in codes or literature., MU - Malayer University(undefined)

This paper investigates the seismic performance of steel moment resisting frames, exploring the potential for energy dissipation not only in the beams but in the beam-to-column joints too. Such an approach practically doubles the possible locations for dissipative zones in the structure but on the other hand places requirements for ductile behaviour on the connections. A design methodology that facilitates the formation of plastic hinges in the beam-to-column joints and the beams at the same time, is presented and examined in comparison to typically designed frames with rigid joints. A number of 24, 2D regular and irregular frame configurations are studied numerically, employing both nonlinear static and dynamic analyses. The examined configurations involve either irregular mass distribution across the floors or stiffness discontinuities of the framing across the floors and the bays. The results indicate a great reduction in ductility demands for the frames with energy dissipation in the beams and the joints concurrently, while other metrics of their seismic response, such as interstorey drifts and lateral capacity remain practically unaffected.

Normally, the design of steel moment resisting frames in seismic areas encourages the formation of plastic zones in the beams. This paper investigates numerically the performance of moment resisting frames designed to dissipate hysteretic energy in joints and beams together. Joints can be a stable source of hysteresis, provided they are designed with ductility requirements. The commonly used bolted end-plate connection is adopted here. In order to provide spacing between the expected plastic zones in the connection and the beam, a reinforcement scheme with cover plates is employed. Appropriate methodologies are provided for the design of connections and cover plates. The proposed joint and beam dissipative frames are evaluated through parametric push-over and time-history analyses. Comparison is performed with frames designed with rigid joints that dissipate energy mainly in beams. The results show that the proposed frames not only don’t suffer in terms of drifts or capacity, due to the added joint flexibility but they provide superior performance in many aspects, while they can offer economical advantages, due to their reduced bending moment requirements.

The performance of steel moment resisting frames with sway imperfections is numerically studied in this paper, under various sudden column removal scenarios. The response of 2D frames with varying values of sway imperfection is comparatively examined against respective perfect frames, under both nonlinear static and time history analyses. In this context, the study also investigates the role of frame design, working with two groups of alternatively designed frames: the first one governed by earthquake loading and the second governed by wind loading. Furthermore, some additional variables to the study are introduced. These are the number of frame storeys, the location of the removed column and the duration of the column removal. The obtained results reveal that the imperfect frames, depending on the location of the removed column, can deteriorate in terms of ductility, reaching increased column demands, compared to the perfect ones.