Structural Engineering Systems Research Articles

Topology optimization of the flat steel shear wall based on the volume constraint and strain energy assumptions under the seismic loading conditions

In structural engineering systems, shear walls are two-dimensional vertical elements designed to endure lateral forces acting in-plane, most frequently seismic and wind loads. Shear walls come in a variety of materials and are typically found in high-rise structures. Because steel shear walls are lighter, more ductile, and stronger than other concrete shear walls, they are advised for usage in steel constructions. It is important to remember that the steel shear wall has an infill plate, which can be produced in a variety of forms. The critical zones in flat steel shear walls are the joints and corners where the infill plate and frame meet. The flat infill plate can be modified to improve the strength and weight performance of the steel shear walls. One of these procedures is Topology Optimization (TO) and this method can reduce the weight and also, increase the strength against the cyclic loading sequences. In the current research paper, the TO of the infill steel plate was considered based on the two methods of volume constraint and maximization of strain energy. Four different volumes (i.e., 60%, 70%, 80%, and 90%) were assumed for the mentioned element in the steel shear wall. The obtained results revealed that the topology configuration of CCSSW with 90% volume constraint presented the highest seismic loading performance. The cumulated energy for this type of SSW was around 700 kJ while it was around 600 kJ for other topology optimization configurations.

On the use of mechanics-informed models to structural engineering systems: Application of graph neural networks for structural analysis

This paper investigates the application of mechanics-informed artificial intelligence to civil structural systems. Structural analysis is a traditional practice that involves engineers to solve different real-life problems. Several approaches can be used for this task, going from “by hand” computation to the recent advanced finite element method. However, when structures become complex, the success of the analysis can be complicated, often requiring high computational efforts and time. To tackle this challenge, traditional high-demanding methods can be supported by new technologies, such as machine-learning tools. This new paradigm aims to solve structural problems by defining the desired output after directly elaborating input data. One of the current limitations is that often the physics behind the problem is ignored. To solve this issue, resolution models can combine empirical data and available mechanics prior knowledge to improve the predictive performance involving physical mechanisms. In this paper, a method to develop a Mechanics-Informed Surrogate Model (MISM) on structural systems is proposed, for which input structured data are used to enrich the informative content of mechanics systems. Then, Graph Neural Networks (GNNs) are explored, as a method capable of properly representing and embedding knowledge about a structural system, such as truss structures. The main advantage of the proposed approach is to provide an alternative way to the usual black-box machine-learning-based models. In fact, in the proposed MISM, the mechanics of the structural system plays the key role in the surrogate model definition, in order to obtain physically based outputs for the investigated problem. For the case at hand, MISMs are developed and employed to learn the deformations map of the system, starting from the knowledge of the structural features. The proposed approach is applied to bi-dimensional and tri-dimensional truss structures and the results indicate that the proposed solution performs better than standard surrogate models.

Aspects Regarding of Nanomaterials and Nanocomposites in 3D Printing Technology Process Development for Application in Biomedicine

This paper will present the advantages of developing the 3D printing process of nanomaterials in different fields such as electronics, biomedical and bioelectronics. As it is already known, nanomaterials are starting to become more and more useful, and more emphasis is being put on the development of new technologies to enable the use of these materials. Nanomaterials consist mainly of chemical substances made up from very small particles that are no larger than a hundred nanometers. These materials occur in nature, they can be an accidental product of human activity, or they can be consciously made to develop new characteristics such as strength, chemical reactivity or increased conductivity compared to the same material that does not display nanometric characteristics. By integrating nanomaterials to 3D printing technology, it is possible to create unique structures, which are difficult to achieve. Nanomaterials can possibly work on personal satisfaction and add to the advancement of European industry. However, new materials can also pose health and environmental risks. Scientific research has turned its attention to the potential outcomes of the production and application of nanomaterials. Meanwhile, the newest method for 3D printing of nanomaterials is Multiphase Direct Ink Writing (MDIW), a method developed from Direct Ink Writing (DIW), a revolutionary additive manufacturing mechanism with wide applications in structural engineering systems, thermal isolation, electrical conductivity, optical reflectivity, and biomedical scaffolds.

A Deep Learning-Based Approach for the Identification of a Multi-Parameter BWBN Model

A restoring-force model is a versatile mathematical model that can describe the relationship between the restoring force and the deformation obtained from a large number of experiments. Over the past few decades, a large body of work on the development of restoring-force models has been reported in the literature. Under high intensity cyclic loadings or seismic excitations, reinforced concrete (RC) structures undergo a wide range of hysteretic deteriorations such as strength, stiffness and pinching degradations. These characteristic behaviors can be described by the multi-parameter Bouc-Wen-Baber-Noori (BWBN) model, which offers a wide range of applicability. This model has been applied for the response prediction and modeling restoring-force behavior in structural and mechanical engineering systems, by adjusting the distribution range of this model’s parameters. However, a major difficulty in utilizing the multi-parameter BWBN model is the parameters’ identification. In this paper, a deep neural network model is used to estimate the hysteresis parameters of the BWBN model. This model is one of the most versatile and widely used general hysteresis models that can describe the hysteretic behavior of RC columns. The experimental data of the RC columns used in this paper are collected from the database of the Pacific Earthquake Engineering Research Center (PEER). Firstly, the hysteretic loop obtained from a physical experiment is described by the BWBN model, and the parameters of the BWBN model are identified via a genetic optimization algorithm. Then a neural network is established by a backpropagation (BP) algorithm for associating the identified BWBN model parameters with physical parameters of the RC column. Finally, the regression analysis of the identified parameters is carried out to obtain the regression characteristics of the RC columns. The trained neural network model can directly identify the parameters of BWBN model based on the physical parameters of RC columns, and is effective and computationally efficient for multi-parameter BWBN model identification. The proposed approach overcomes the difficult problem of identifying the parameters of BWBN model and provides a promising approach for a wider application of this multi-parameter hysteresis model.

Causality in structural engineering: discovering new knowledge by tying induction and deduction via mapping functions and explainable artificial intelligence

Causality is the science of cause and effect. It is through causality that explanations can be derived, theories can be formed, and new knowledge can be discovered. This paper presents a modern look into establishing causality within structural engineering systems. In this pursuit, this paper starts with a gentle introduction to causality. Then, this paper pivots to contrast commonly adopted methods for inferring causes and effects, i.e., induction (empiricism) and deduction (rationalism), and outlines how these methods continue to shape our structural engineering philosophy and, by extension, our domain. The bulk of this paper is dedicated to establishing an approach and criteria to tie principles of induction and deduction to derive causal laws (i.e., mapping functions) through explainable artificial intelligence (XAI) capable of describing new knowledge pertaining to structural engineering phenomena. The proposed approach and criteria are then examined via a case study.

A multi-objective optimization algorithm for Bouc–Wen–Baber–Noori model to identify reinforced concrete columns failing in different modes

Concrete columns are the most important load-bearing components in civil structures. The potential damage in reinforced concrete (RC) columns could be categorized into three different failure modes: flexural shear (FS) failure, flexural-failure (FF), and shear failure (SF). The corresponding hysteresis loops for each mode differ significantly. Therefore, a multi-parameter hysteretic restoring force model is needed to describe the hysteretic energy dissipation phenomenon and behavior. Identification of the optimal parameter values of a multi-parameter hysteresis model of RC columns under different failure modes is essential in the evaluation of structural inelastic seismic performance. In this paper, a multi-objective optimization algorithm called NSGA-II is employed to identify the parameters of Bouc–Wen–Baber–Noori model (BWBN) hysteresis model, this model has been used for describing the response and modelling restoring force behavior in several structural and mechanical engineering systems, that can fully describe the hysteretic restoring force characteristics of RC columns. An objective function for the restoring force is proposed to identify the parameters of BWBN model. In order to ensure the accuracy of identification, based on the sensitivity analysis, the parameters distribution law of RC columns in different failure modes is obtained. Furthermore, the reference values under different failure modes are proposed. The results presented in this paper will significantly reduce the calculation of subsequent identification. Twelve groups of experimental data are randomly selected to verify the feasibility of the above algorithm. It is demonstrated that using the multi-objective optimization algorithm leads to better identification accuracy with minimum prior experience. Performance of the algorithm is verified using simulated and experimental data. The experimental data of the RC columns were collected from the database of the Pacific Earthquake Engineering Research Center.

Response surface-based model updating to detect damage on reduced-scale masonry arch bridge

Response surface (RS) methods, a combination of mathematical and statistical techniques, have been widely used in design optimization, response prediction, and model validation in structural engineering systems. However, its usage in structural damage identification, especially for historic structures has not been quite common. For this purpose, this study attempts to investigate damage detection in a masonry arch bridge. Within the scope of this, a reduced-scale model of a one span historical masonry arch bridge was built in a laboratory environment. To determine the modal parameters of the reduced-scaled bridge model, operational modal analysis (OMA) was performed under ambient vibrations. Signals originated by sensitive accelerometers were collected to quantify the vibratory response of the reduced-scaled model bridge. The experimental natural frequencies, mode shapes, and damping ratios resulting from these measurements were figured out by using the Enhanced Frequency Domain Decomposition (EFDD) technique. The three-dimensional model of the reduced-scale bridge was created in the ANSYS finite element (FE) software program to expose the analytical dynamic characteristics of the bridge model. The results obtained in the experimental application were compared with those of the finite-element analysis of the bridge model. The calibration of the numeric model was utilized depending on the experimental modal analysis results of the reduced-scale bridge by using the RS method. Design of experiments was constructed by using central composite design, and the RS models were generated by performing the genetic aggregation approach. The optimum results between the experimental and numerical analyses were found by using the RS optimization. Then, regional damages created on the scaled model and the changes of dynamic properties of the damaged case were evaluated. The damage location was approximately identified by using the RS method in the calibrated finite-element model. The results demonstrated that the RS-based FE updating approach is an effective way for damage detection and localization in masonry type structures.

Structural analysis and design of a multistoried RCC building to prevent disproportionate collapse using E-Tabs

Over the recent decenniums disproportionate collapse debacles have emphasized the codified bodies around the globe; as a result, there had been an increased research interest in the topic “Disproportionate Collapse”. Civil engineering structural systems are susceptible to disproportionate collapse when prone to aberrant loads beyond the ultimate capacity of critical structural components. Sudden loss of critical structural components may result in the complete collapse of the structure. Presently, researchers follow different modelling ideas to simulate the loss of critical load supporting member for disproportionate collapse assessment. This paper is intended to study about the practices that we should follow to reduce the potential of disproportionate collapse in building. This project includes the effective modes to analyze and model the peculiar types of structures using advanced software like E-TABS. In this dissertation work a G + 10 storied reinforced concrete building was analyzed and designed for dead load, live load, wind load, and seismic load with factor of safety. The bending moment and area of steel required by the elements are noted for the comparative study. Few critical elements are studied which could trigger the progressive collapse. Notional removal of the critical element is carried out and the building is analyzed for gravity loads alone. Due to the removal of key elements the performance in the structure changes. These changes are noted and the necessary corrections are made in the sections to prevent it from disproportionate collapse.

Upgrading Seismic Behaviors of R/C Frames with Shaped Memory Alloys Based Braces

Intelligent systems in structural engineering are systems that are capable of automatically adapting structural behavior in response to instantaneous loads, thereby ensuring the safety of extended structural life and performance. One of the new technologies that makes it possible to achieve these goals is the production and development of smart materials. Examples of these smart materials used in structural engineering include piezo-ceramics, magnetorheological fluids, electrorheological fluids, and form-memory alloys. Shaped Memory Alloys (SMAs) are new materials that have been used in various fields of science and engineering in recent decades. In recent years, these materials attracted the attention of researchers in the field of building and earthquake engineering due to their properties such as high damping capacity, low permanent displacement and structural fatigue resistance. One of the application areas of these materials is that they are used as a brace in the structures, so the research results have shown the acceptable performance and operability of such structural systems. In this study, shape memory bracelets and steel bracelets installed as structural brackets were used as a lateral load system in the seismic improvement of concrete bending frames and factors such as residual displacement and base shear in these two load-bearing systems are compared.  The model under studying is a 6-story frame that has been subjected to time history analysis. SeismoStruct software was used to analyze the model.

Comparison of a modified vibro-impact nonlinear energy sink with other kinds of NESs

Vibration mitigation is essential to many dynamical and engineering structures that are subjected to destructive vibration amplitudes induced by impulsive loading, seismic excitation, blasts, flutter, collisions, fluid–structure interaction and so on. Unprotected structures by vibration absorbers could be exposed to failure which lead to enormous losses in human lives, major equipment and economy. Employing the nonlinear targeted energy transfer (TET) concept in nonlinear vibration absorbers which are later called as nonlinear energy sinks has ignited a very rapid research interest since 2001. Up-to-date, considerable growth in the NESs field has taken place. Accordingly, various types of NESs have been introduced for vibration mitigation in variety of dynamical and structural engineering systems. The types of introduced NESs included, but not limited to, stiffness-based, rotating and the vibro-impact NESs. Among these common types of NESs, the most effective and efficient one is the single-sided vibro-impact (SSVI) nonlinear energy sink (NES). However, most of investigations has implemented a coefficient of restitution of 0.7 which closely corresponds to a steel-to-steel impact. Therefore, this paper is aimed to further improve the SSVI NES by including the coefficient of restitution in the performance optimization. Accordingly, significant improvement in the SSVI NES performance is obtained when the coefficient of restitution is found to be near 0.45. In addition, performance comparison between the enhanced SSVIe NES with several existing types of NESs is performed here where a nine-story large-scale structure is employed for this numerical comparison. Accordingly, the performance of the enhanced SSVIe NES of nearly 0.45 coefficient of restitution is found to be more robust to the initial impulsive energy levels and to its physical parameters variation than other kinds of existing NESs.

Reliability analysis of different systems using triangular multi-fuzzy sets estimated by statistical data

In this paper, the multi-fuzzy sets and its arithmetic operations are first introduced briefly then we describe an idea of using multi-fuzzy sets approach to reliability analysis of different types of unrepairable fuzzy systems as series, parallel, series-parallel and parallel-series systems consist of independent components. The knowledge about causes and effects of failures is usually described with large uncertainty content so the reliability of each component can be represented by triangular multi-fuzzy set. Each multi-fuzzy set is estimated by using the concept of confidence interval that calculated based on statistical data taken from random samples of each component. A numerical example is given to demonstrate the applicability of the multi-fuzzy sets in different structural engineering systems and the results were drawn by using MAPLE software program.

STRUCTURAL OPTIMIZATION USING AN ADAPTIVE RBF APPROACH

This paper presents an adaptive radial basis functions (RBFs) metamodeling method for design optimization of structures. Various numerical techniques have been developed and adopted in structural and multidisciplinary optimization. To evaluate responses of a structural or mechanical engineering system, finite element (FE) analyses are routinely used. An FE code shall be integrated with an optimization algorithm in a nested analysis and design of structures. Therefore, software input/output programming is required. A metamodeling method, on the contrary, expresses structural responses using an approximate function, so that the FE software is not directly coupled in the numerical optimization loop. Any optimization algorithm can be applied to find the optimal design, based on the explicit response functions. In this study, numerical examples were created and FE analyses were first performed at sample points. Subsequently, metamodels were constructed and a gradient-based optimization algorithm was applied. At the optimal point of one adaptive iteration, accuracy of the RBF metamodel was checked, and additional sample points were added to the sample pool to improve the model accuracy. The adaptive iterations continued, until the convergence of the objective function was achieved. The proposed optimization method worked well for a numerical example, and the optimal result was found within a few adaptive iterations.

Stochastic response determination of structural systems modeled via dependent coordinates: a frequency domain treatment based on generalized modal analysis

Generalized independent coordinates are typically utilized within an analytical dynamics framework to model the motion of structural and mechanical engineering systems. Nevertheless, for complex systems, such as multi-body structures, an explicit formulation of the equations of motion by utilizing generalized, independent, coordinates can be a daunting task. In this regard, employing a set of redundant coordinates can facilitate the formulation of the governing dynamics equations. In this setting, however, standard response analysis techniques cannot be applied in a straightforward manner. For instance, defining and determining a transfer function within a frequency domain response analysis framework is challenging due to the presence of singular matrices, and thus, the machinery of generalized matrix inverses needs to be employed. An efficient frequency domain response analysis methodology for structural dynamical systems modeled via dependent coordinates is developed herein. This is done by resorting to the Moore–Penrose generalized matrix inverse in conjunction with a recently proposed extended modal analysis treatment. It is shown that not only the formulation is efficient in drastically reducing the computational cost when compared to a straightforward numerical evaluation of the involved generalized inverses, but also facilitates the derivation and implementation of the celebrated random vibration input–output frequency domain relationship between the excitation and the response power spectrum matrices. The validity of the methodology is demonstrated by considering a multi-degree-of-freedom shear type structure and a multi-body structural system as numerical examples.

An Automated Adaptive Finite Element Mesh Generation for Dynamics

Structural analysis remains as an essential part of any integrated civil engineering system in today’s rapidly changing computing environment. Even with enormous advancements in capabilities of computers and mobile tools, enhancing computational efficiency of algorithms is necessary to meet the changing demands for quick real time response systems. The finite element method is still the most widely used method of computational structural analysis; a robust, reliable and automated finite element structural analysis module is essential in a modern integrated structural engineering system. To be a part of an automated finite element structural analysis, an efficient adaptive mesh generation scheme based on R-H refinement for the mesh and error estimates from representative strain values at Gauss points is described. A coefficient that depends on the shape of element is used to correct overly distorted elements. Two simple case studies show the validity and computational efficiency. The scheme is appropriate for nonlinear and dynamic problems in earthquake engineering which generally require a huge number of iterative computations.

An evaluation of modelling approaches and column removal time on progressive collapse of building

Over the last few decades, progressive collapse disasters have drawn the attention of codified bodies around the globe; as a consequence, there has been a renewed research interest. Structural engineering systems are prone to progressive collapse when subjected to abnormal loads beyond the ultimate capacity of critical structural members. Sudden loss of critical structural member(s) triggers failure mechanisms which may result in a total or partial collapse of the structure proportionate or disproportionate to the triggering event. Currently, researchers adopt different modelling techniques to simulate the loss of critical load bearing members for progressive collapse assessment. GSA guidelines recommend a column removal time less than a tenth of the period of the structure in the vertical vibration mode. Consequently, this recommendation allows a wide range of column removal time which produces inconsistent results satisfying GSA recommendation. A choice of a load time history function assumed for gravity and the internal column force interaction affects the response of the structure. This paper compares different alternative numerical approaches to simulate the sudden column removal in frame buildings and to investigate the effect of rising time on the structural response.

Static–Dynamic Relationship for Flexural Free Vibration of Extensible Beams

This technical note presents a static–dynamic relationship for the flexural free vibration analysis of beams in tension with some specific boundary conditions. It is shown to be possible that a free vibration system can be solved via a static analysis approach to determine the natural frequencies of the beam with tension forces. The key idea of this study is to substitute the real natural frequency parameters with zero or negative elastic foundation stiffness, thereby allowing one to obtain the natural frequencies by analyzing the case with negative foundation elastic constant. This static approach for vibration problems can be extended for more complicated engineering structural systems.

Fatigue life prediction of mechanical structures under stochastic loading

Problems of fatigue life prediction of materials and structures are discussed in the paper. Service loading is assumed as a continuous loading process with possible discontinuous events, which are caused by various operating conditions. The damage in a material is due to a cumulative degradation process. The damaging process is then represented either by rain-flow matrices or by a fatigue damage function which is derived using some hypothesis of a fatigue failure criterion. Presented theoretical procedure enables a very effective estimation of a service life and/or reliable evaluation of residual life of any structures under various types of loading and environmental conditions. This approach creates a good basis for powerful expert systems in structural and mechanical engineering. The aim of the paper is to present briefly some results of analysis of load-bearing steel structure loads of special railway crane PKP 25/20i which was utilized in some specific ad relatively hard operating conditions. Virtual models of the structure were being used in an analysis of acting working dynamics loads influence to be able to forecast fatigue life of load-bearing of the crane jib.

Pneumatic Formwork Systems in Structural Engineering

Concrete as a construction material is characterized by high compressive strength, low tensile strength, and good casting ability. In order to fully utilize the potential of this material, the form of load-carrying structures has to be designed according to the stress distribution in the structure. Partially hollow structures, such as hollow beams, or doubly curved structures, such as shells, have favorable characteristics. In hollow structures, material savings are achieved in the individual building components by locally reducing dimensions. Concrete shells, if designed properly, are able to span over large areas by transferring the loads mainly by membrane stresses. The main problem with these structures, however, is the high effort required for producing the complicated formwork. One possibility of reducing this effort is to use a pneumatic formwork. This paper describes different pneumatic formwork systems invented in the past 100 years and presents the latest developments in this area. The many types of possible applications are divided into three categories in order to obtain a clearer overview. Finally, a new construction method, called “Pneumatic Forming of Hardened Concrete (PFHC),” is presented. This method was invented at the TU Vienna and uses the pneumatic formwork in a novel way.

On damping created by heterogeneous yielding in the numerical analysis of nonlinear reinforced concrete frame elements

In the dynamic analysis of structural engineering systems, it is common practice to introduce damping models to reproduce experimentally observed features. These models, for instance Rayleigh damping, account for the damping sources in the system altogether and often lack physical basis. We report on an alternative path for reproducing damping coming from material nonlinear response through the consideration of the heterogeneous character of material mechanical properties. The parameterization of that heterogeneity is performed through a stochastic model. It is shown that such a variability creates the patterns in the concrete cyclic response that are classically regarded as source of damping.

Factor-analysis based questionnaire categorization method for reliability improvement of evaluation of working conditions in construction enterprises

This paper presents a factor-analysis based questionnaire categorization method to improve the reliability of the evaluation of working conditions without influencing the completeness of the questionnaire both in Taiwanese and Chinese construction enterprises for structural engineering applications. The proposed approach springs from the AI application and expert systems in structural engineering. Questions with a similar response pattern are grouped into or categorized as one factor. Questions that form a single factor usually have higher reliability than the entire questionnaire, especially in the case when the questionnaire is complex and inconsistent. By classifying questions based on the meanings of the words used in them and the responded scores, reliability could be increased. The principle for classification was that 90% of the questions in the same classified group must satisfy the proposed classification rule and consequently the lowest one was 92%. The results show that the question classification method could improve the reliability of the questionnaires for at least 0.7. Compared to the question deletion method using SPSS, 75% of the questions left were verified the same as the results obtained by applying the classification method.