Whilst there has been research about reduced web section beams; RWS, their seismic behaviour when they are overlaid by slabs is not well understood. This study addresses this by assessing a high-definition finite element representation of non-seismically detailed RWS connections with slabs. The composite action is investigated, focusing on effects of the size and location of the web openings. These connections can reach a 4% interstorey drift, thus comprising special moment frames in accordance with AISC 341. Moreover, beam, column, and joint tearing were avoided entirely, and at the most, brittle failure of bolts in the end plate was observed.

In this paper, a novel methodology is developed for the characterization of the capacity of rectangular-shaped concrete-filled steel tubes (CFSTs). In the scientific research field, of particular interest is the behavior of long CFST columns under eccentric compressive load. These conditions promote failure mechanisms involving global member buckling. The developed methodologies are based on machine learning techniques found on artificial neural networks (ANNs). Furthermore, optimization methodologies, employing the grey wolf optimization algorithm and the firefly algorithm, have been attempted. For the training and validation of the models, a database consisting of 1,641 experimental tests collected from literature sources has been prepared, containing long and short specimens as well as specimens with or without load eccentricity. As the vast majority of the available experimental tests involve short specimens, the database has been augmented with 216 3D finite element models (FEMs), featuring increased member slenderness values. The calibration of the FEMs has been performed against experimental tests. The performance of the developed models has been measured through a number of performance indices, and compared with available code procedures. They have been found to provide significant improvements, both for short and long CFST columns, with the ANN model optimized with the firefly algorithm outperforming the others. Furthermore, a graphical user interface (GUI) has been developed which can be readily used to estimate the axial load capacity of CFST columns through the optimal ANN model. The developed GUI is made available as a supplementary material.

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.

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.

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.

There is an unmet need of models for early prediction of morbidity and mortality of Coronavirus disease-19 (COVID-19). We aimed to a) identify complement-related genetic variants associated with the clinical outcomes of ICU hospitalization and death, b) develop an artificial neural network (ANN) predicting these outcomes and c) validate whether complement-related variants are associated with an impaired complement phenotype. We prospectively recruited consecutive adult patients of Caucasian origin, hospitalized due to COVID-19. Through targeted next-generation sequencing, we identified variants in complement factor H/CFH, CFB, CFH-related, CFD, CD55, C3, C5, CFI, CD46, thrombomodulin/THBD, and A Disintegrin and Metalloproteinase with Thrombospondin motifs (ADAMTS13). Among 381 variants in 133 patients, we identified 5 critical variants associated with severe COVID-19: rs2547438 (C3), rs2250656 (C3), rs1042580 (THBD), rs800292 (CFH) and rs414628 (CFHR1). Using age, gender and presence or absence of each variant, we developed an ANN predicting morbidity and mortality in 89.47% of the examined population. Furthermore, THBD and C3a levels were significantly increased in severe COVID-19 patients and those harbouring relevant variants. Thus, we reveal for the first time an ANN accurately predicting ICU hospitalization and death in COVID-19 patients, based on genetic variants in complement genes, age and gender. Importantly, we confirm that genetic dysregulation is associated with impaired complement phenotype.

In this paper a model for the prediction of the ultimate axial compressive capacity of square and rectangular Concrete Filled Steel Tubes, based on an Artificial Neural Network modeling procedure is presented. The model is trained and tested using an experimental database, compiled for this reason from the literature that amounts to 1193 specimens, including long, thin-walled and high-strength ones. The proposed model was selected as the optimum from a plethora of alternatives, employing different activation functions in the context of Artificial Neural Network technique. The performance of the developed model was compared against existing methodologies from design codes and from proposals in the literature, employing several performance indices. It was found that the proposed model achieves remarkably improved predictions of the ultimate axial load.

In this study, we estimate the ultimate load of rectangular concrete-filled steel tubes (CFST) by developing a novel hybrid predictive model (ANN-BCMO) which is a combination of balancing composite motion optimization (BCMO) - a very new optimization technique and artificial neural network (ANN). For this aim, an experimental database consisting of 422 datasets is used for the development and validation of the ANN-BCMO model. Variables in the database are related with the geometrical characteristics of the structural members, and the mechanical properties of the constituent materials (steel and concrete). Validation of the hybrid ANN-BCMO model is carried out by applying standard statistical criteria such as root mean square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE). In addition, the selection of appropriate values for parameters of the hybrid ANN-BCMO is conducted and its robustness is evaluated and compared with the conventional ANN techniques. The results reveal that the new hybrid ANN-BCMO model is a promising tool for prediction of the ultimate load of rectangular CFST, and prove the effective role of BCMO as a powerful algorithm in optimizing and improving the capability of the ANN predictor.

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.

An accurate estimation of the axial compression capacity of the concrete-filled steel tubular (CFST) column is crucial for ensuring the safety of structures containing them and preventing related failures. In this article, two novel hybrid fuzzy systems (FS) were used to create a new framework for estimating the axial compression capacity of circular CCFST columns. In the hybrid models, differential evolution (DE) and firefly algorithm (FFA) techniques are employed in order to obtain the optimal membership functions of the base FS model. To train the models with the new hybrid techniques, i.e., FS-DE and FS-FFA, a substantial library of 410 experimental tests was compiled from openly available literature sources. The new model’s robustness and accuracy was assessed using a variety of statistical criteria both for model development and for model validation. The novel FS-FFA and FS-DE models were able to improve the prediction capacity of the base model by 9.68% and 6.58%, respectively. Furthermore, the proposed models exhibited considerably improved performance compared to existing design code methodologies. These models can be utilized for solving similar problems in structural engineering and concrete technology with an enhanced level of accuracy.

In this research, a new machine-learning approach was proposed to evaluate the effects of eight input parameters (surface area, relative compactness, wall area, overall height, roof area, orientation, glazing area distribution, and glazing area) on two output parameters, namely, heating load (HL) and cooling load (CL), of the residential buildings. The association strength of each input parameter with each output was systematically investigated using a variety of basic statistical analysis tools to identify the most effective and important input variables. Then, different combinations of data were designed using the intelligent systems, and the best combination was selected, which included the most optimal input data for the development of stacking models. After that, various machine learning models, i.e., XGBoost, random forest, classification and regression tree, and M5 tree model, were applied and developed to predict HL and CL values of the energy performance of buildings. The mentioned techniques were also used as base techniques in the forms of stacking models. As a result, the XGboost-based model achieved a higher accuracy level (HL: coefficient of determination, R2 = 0.998; CL: R2 = 0.971) with a lower system error (HL: root mean square error, RMSE = 0.461; CL: RMSE = 1.607) than the other developed models in predicting both HL and CL values. Using new stacking-based techniques, this research was able to provide alternative solutions for predicting HL and CL parameters with appropriate accuracy and runtime.

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.

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.

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.

This paper presents an analytical model for the estimation of initial lateral stiffness of steel moment resisting frames with masonry infills. However, rather than focusing on the single bay-single storey substructure, the developed model attempts to estimate the global stiffness of multi-storey and multi-bay frames, using an assembly of equivalent springs and taking into account the shape of the lateral loading pattern. The contribution from each infilled frame panel is included as an individual spring, whose properties are determined on the basis of established diagonal strut macro-modeling approaches from the literature. The proposed model is evaluated parametrically against numerical results from frame analyses, with varying number of frame stories, infill openings, masonry thickness and modulus of elasticity. The performance of the model is evaluated and found quite satisfactory.

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.

An established practice for the design of steel frame structures is to ignore the contribution of non-structural elements, such as masonry infilled walls.Even though this is considered a pro-safety simplification, it removes the opportunity to predict more realistically the actual structural response, on one hand, and to evaluate non-structural damage, particularly under less severe earthquakes, on the other.This study attempts to evaluate the contribution of masonry infills to the response of typical steel frames.In particular, a selection of multistorey frames designed according to Eurocode 3 and 8, ignoring the presence of infills, is examined.The infills are then modeled, using an equivalent diagonal strut scheme.Their contribution to the structural response is evaluated through both linear and nonlinear analyses, for variable ratio of wall openings and number of storeys.The response characteristics are compared with the respective ones of the bare steel frames.The results reveal a significant increase in stiffness, as well as improved lateral resistance.Moreover, in regard to damage limitation requirements, the effectiveness of the infilled frames is significantly enhanced.

The moment M versus rotation  curve represents an essential response characteristic of structural steel joints, since for every load level, it provides the bending moment and the relative rotation between the connected members. The complete M  curve, that describes the full, up to failure, range of the joint response, allows the realisation of nonlinear structural analyses techniques, where the joints are actively simulated, similar to the members, without the traditional idealisations of rigid or pinned joints. Some benefits of this development include the ability to enhance the overall ductility of the structure, since the formation of plastic hinges inside the joints becomes possible, and the more accurate approximation of the actual response, through the global structural analysis. Also, financial savings are available due to the minimised use of rigid joints, as well as by means of a more optimal selection of the connected members. Some existing proposals that allow the estimation of the complete M  curve can be found in references [1-5].

A methodology for the estimation of the complete moment–rotation curve of structural beam-to-column joints is presented in this paper. The methodology is based on the component method and is materialized through appropriate mechanical models that are analyzed nonlinearly. The cases examined in this work include bolted connections with end-plates and with angles. Significant consideration is devoted to the reliable simulation of the tensile joint components that have the form of equivalent T-stubs, by application of a recently developed incremental T-stub model, for the calculation of their nonlinear force–displacement laws. The proposed methodology is evaluated against experimental tests and advanced finite element models in terms of stiffness, strength and rotational capacity, and its performance is found to be very satisfactory.

An incremental model for predicting the mechanical characteristics of T -stub steel connections is presented in this paper. The response is calculated analytically on the basis of a simple beam representation for the flange and a deformational spring for the bolt. Contact phenomena in the flange are taken into account, and by means of an incremental procedure it becomes possible to follow the development of these phenomena throughout the loading history. Material nonlinearity is also accounted for, both in the flange and the bolt, assuming a bilinear constitutive model. We propose several refinements of the model, which enhance its effectiveness with respect to intricate characteristics of T -stub behavior, such as bolt-flange interaction and three-dimensional geometry. The performance of the model is validated by comparison to experimental results found in the literature and by a parametric study performed in parallel with three-dimensional finite element analyses.