Structural Engineering Problems Research Articles

A Review on the Discrete Singular Convolution Algorithm and Its Applications in Structural Mechanics and Engineering

The discrete singular convolution (DSC) algorithm is a relatively new numerical method. It has not only the advantage of high accuracy of global methods but also the advantage of the flexibility of local methods, and thus has been projected by its proponents as one of the potential alternative approaches to the conventional finite element method, especially for solving problems of structures in high frequency vibrations. Although the progression on the DSC algorithm and its applications is clear from past researches, but this has been scattered over lots of published articles. This paper presents a comprehensive review on the DSC algorithm and the application of the DSC for solutions of a variety of problems in structural mechanics and engineering, which should be of general interest to the computational community. The DSC algorithm is introduced first in a concise and clear manner to help the beginners and engineers who are going to use the DSC algorithm for structural analysis. Emphasis of the review is placed on the DSC analysis of bending, buckling, free vibration and dynamic analysis of structural elements and structures, although the DSC algorithm with a proper singular kernel can solve a wide range of science and engineering problems.

Multi-criteria shape optimization of open-spandrel concrete arch bridges: Pareto front development and decision-making

PurposeThis paper aims to study the shape of the concrete arched bridge by particle swarm optimization algorithm.Design/methodology/approachFinite element model of open-spandrel concrete arch bridges was constructed using a number of parameters. Design variables of optimization problem include height of skewback abutment, height of arch crown, position of crown with respect to global axes and left and right radius of up and down arches. After parametric modeling of bridge geometry and application of multi-objective particle swarm optimization, the shape optimization of bridge arch was determined. The concrete volume used in bridge substructure construction and maximum principal tensile stress of concrete arch body was adopted as two objective functions in this study. The optimization problem aims to minimize the two objective functions. Geometric and stress constraints are also included in the problem.FindingsBased on the results presented in the paper, the Pareto front is generated which helps the decision-maker or designer to pick the compromise solution from among 20 optimum designs according to their subjective preferences or engineering judgment, respectively. Moreover, to help the decision-maker, the two multiple objective decision-making methods were used for selection of the best solution from among nondominated solutions.Originality/valueThis research aims to solve an interesting optimization problem in structural engineering. Optimization of arch bridges structure was done for reducing construction costs and increasing safety for the first time.

Toward holistic tension- or compression-biased structural designs using topology optimization

The efficiency of a structural design relies considerably on the knowledge and experience of its design engineer. It is critical to choose the proper structural system, cross-sections, and materials as these elements significantly influence the design’s performance. Establishing computational methods that support the engineer in the design process and facilitate more objective solutions therefore represents an important step in creating proficient structural designs.In this paper, a novel approach is introduced to generate topology optimization results with an intended bias toward tension or compression for practical application in structural engineering. The results are deduced from the example of continuum elements and extended to cover truss topology optimization – assuming a linear-elastic, isotropic material model – in addition to combined truss–continuum topology optimization with bilinear, orthotropic material behavior. The effects of creating such solutions are analyzed at a simple theoretical problem. For the purposes of practically designing reinforced concrete structures, the preferable scope of application of each of the three methods is evaluated. The benefit of creating biased solutions is demonstrated by assessing their structural performance, robustness, and overall suitability on both global (system) and local (cross-section) scales and by applying them to common problems in structural engineering.

Non-probabilistic Integrated Reliability Analysis of Structures with Fuzzy Interval Uncertainties using the Adaptive GPR-RS Method

Due to its weak dependence on the quantity of variable samples, the non-probabilistic reliability analysis method based on the convex set model is applicable to practical problems in structural engineering with inherent uncertainties. However, when dealing with the black-box limit-state function issues in practical complex structural engineering, the traditional quadratic polynomial response surface (QP-RS) method has the problem of insufficient precision in approximating a highly nonlinear function. Meanwhile, fixing the limits of interval variables is trickier in case of scant samples and meager statistical information. To remedy the above deficiencies, this paper introduces a reasonable integrated reliability analysis approach. First, an adaptive Gaussian process regression response surface (GPR-RS) method that dynamically improves the fitted accuracy near the design point of the black-box limit-state function is formulated. Furthermore, the integrated reliability index with consideration of fuzzy interval uncertainties is presented. Three validation and two application examples are employed, which have justified the approach as a more reasonable assessor of practical complex structural reliability with safer results.

Behavior of P-Delta Effect in High- Rise Buildings With and Without Shear Wall

The high rise structures are proposed for residential and commercial purposes. They may easily effect by seismic as well as wind loads, so the buildings get deformed and collapsed easily. To avoid these problems we consider p-delta effect in designing. As the number of stories increases p-delta effect becomes very important. The P-Δ effect is relevant in structural engineering problems, especially in civil engineering, where we’re dealing with large structures with proportionally decreasing small moments of inertia as they continue to be extended in absolute height. When designing structures, we may consider that they’re immune to lateral deformation, and may therefore not account for their behaviour when sudden buckling or beam-column-like behaviour is introduced. In this study p- delta (P-Δ) effect on high-rise building studied for the analysis of G+29 RCC framed building and models were done by ETABS2016. Seismic and wind loads are applied to model as per IS-1893 (2002) and IS-875 (PART-III). The displacements, storey drifts, Bending Moments and Shear Forces are compared to the different models by considering with and without P-delta effect and by providing shearwalls at different locations.

An adaptive order response surface method for structural reliability analysis

Purpose Balancing accuracy and efficiency is an important evaluation index of response surface method. The purpose of this paper is to propose an adaptive order response surface method (AORSM) based on univariate decomposition model (UDM). Design/methodology/approach First, the nonlinearity of the univariate function can be judged by evaluating the goodness of fit and the error of curve fit rationally. Second, combining UDM with the order analysis of separate component polynomial, an easy-to-implement AORSM is proposed. Finally, several examples involving mathematical functions and structural engineering problems are studied in detail. Findings With the proposed AORSM, the orders of component functions in the original response surface can be determined adaptively and the results of those cases in this paper indicate that the proposed method performs good accuracy, efficiency and robustness. Research limitations/implications Because just the cases with single failure mode and single MPP are studied in this paper, the application in multi-failure mode and multi-MPP cases need to be investigated in the coming work. Originality/value The nonlinearity of the univariate in the response surface can be determined adaptively and the undetermined coefficients of each component function are obtained separately, which reduces the computation dramatically.

Geometrically nonlinear simulation of textile membrane structures based on orthotropic hyperelastic energy functions

New hyperelastic orthotropic models are proposed for the simulation of textile membranes used in civil engineering applications. In contrast to published models, part of the new models is polyconvex and ensures thereby a physically meaningful and mathematically sound formulation. The models are adjusted to uniaxial tension tests performed in warp and fill direction, where not only the stress-strain response in tension direction is accounted for but also the lateral contraction. Thereby, the crosswise interaction between the warp and fill direction is captured. In a series of different boundary value problems the new models as well as a competitive formulation given in literature are compared with respect to the accuracy to represent the experimental data, the mathematical properties as well as the numerical robustness. As it turns out, most formulations including the model from the literature show a loss of material stability and non-converging Newton iterations in structural simulations. Only one of the proposed polyconvex formulations works robustly in numerical simulations of realistic structural engineering problems. Thereby, this new orthotropic model enables realistic simulations of textile membranes in a fully geometrically nonlinear setting, which does not require simplifications based on linearized strains, which are currently used as standard in engineering practice.

Classification of failure modes in ductile and non-ductile concrete joints

In reinforced concrete structures, the behavior of beam-column connections has a significant effect on the overall behavior of the structure. Therefore, knowledge of the failure mode in these elements can be essential. In this article, two methods are proposed that can determine the failure mode type of the reinforced concrete joints, those with sufficient transverse reinforcement (ductile joints) and in concrete joints with no or little transverse reinforcement (non-ductile joints), in a probabilistic manner with high accuracy. For this purpose, two classification structures based on a number of classifiers for exterior and interiors concrete joints were utilized to determine the type of failure modes. A collection of laboratory tests were used to create and validate the classifications. It was concluded from the results that the models presented in this paper have the ability to determine the failure mode. The use of these models are simple and do not need any complex calculation. They also can be useful tools in structural engineering problems such as the initial investigation of damages in existing concrete structures or for retrofitting applications.

Direct and iterative methods for interval parametric algebraic systems producing parametric solutions

SummaryThis paper deals with interval parametric linear systems with general dependencies. Motivated by the so‐called parameterized solution introduced by Kolev, we consider the enclosures of the solution set in a revised affine form. This form is advantageous to a classical interval solution because it enables us to obtain both outer and inner bounds for the parametric solution set and, thus, intervals containing the endpoints of the hull solution, among others. We propose two solution methods, a direct method called the generalized expansion method and an iterative method based on interval‐affine Krawczyk iterations. For the iterative method, we discuss its convergence and show the respective sufficient criterion. For both methods, we perform theoretical and numerical comparisons with some other approaches. The numerical experiments, including also interval parametric linear systems arising in practical problems of structural and electrical engineering, indicate the great usefulness of the proposed methodology and its superiority over most of the existing approaches to solving interval parametric linear systems.

An improved reliability analysis approach based on combined FORM and Beta-spherical importance sampling in critical region

The reliability analysis of structural systems is generally difficult when limit state function (LSF) is implicitly defined especially by black-box models in engineering. To balance analysis efficiency and accuracy, an improved approach named BIS-FC is proposed in this paper based on the Beta-spherical importance sampling (BIS) framework with the innovative concept of critical region defined by combination with First Order Reliability Method (FORM). The critical region is defined by the hyper-tangent plane of LSF at the Most Probable Point (MPP) and its parallel hyperplanes according to the convex or concave features of LSF, wherein the samples are of both high occurrence probability and high misjudgment risk due to the linearization assumption of FORM. BIS-FC only conducts LSF analysis for the BIS samples located in the critical region, and the other samples are directly identified as safe or failure according to the linearized hyperplanes. Thus large computational cost can be saved compared to the original BIS, and meanwhile, the analysis accuracy can be greatly enhanced compared to FORM. An iterative process is proposed to properly define the critical region, based on which reliability is analyzed sequentially until the stopping criteria for desired estimation error level and stable convergence are satisfied. The algorithms of BIS-FC for both single and multiple MPP situations are developed and testified with six numerical examples and one satellite structural engineering problem. The results demonstrate the effectiveness of BIS-FC regarding good balance between efficiency and accuracy.

Efficient uncertainty quantification method applied to structural fire engineering computations

Probabilistic Risk Assessment methodologies are gaining traction in fire engineering practice as a (necessary) means to demonstrate adequate safety for uncommon buildings. This induces a need to apply uncertainty quantification to structural fire engineering problems. Yet, the combination of probabilistic methods and advanced numerical fire engineering tools has been limited due to the absence of a methodology which is both efficient (i.e. requires a limited number of model evaluations) and unbiased (i.e. without prior assumptions regarding the output distribution type). In this paper, the recently proposed MaxEnt method is combined with the dedicated structural fire engineering software SAFIR to evaluate the ability of the method to achieve efficient, unbiased assessments of structural fire performance. The case studies include the probability density function (PDF) of (i) the standard fire resistance of a composite column; (ii) the load bearing capacity of a composite floorplate exhibiting tensile membrane action, after 90 min of standard fire exposure; (iii) the load bearing capacity of the same composite floorplate, considering a parametric fire exposure including cooling phase; and (iv) the maximum temperature reached in a protected steel element under realistic fire exposure. In the first application, the MaxEnt PDF correctly and efficiently captures the distribution obtained using Monte Carlo Simulations. The floorplate example under parametric fire exposure shows the true strength of the MaxEnt method as an unbiased assessment, as different failure modes are observed in the cooling phase resulting in an irregular shape of the load-bearing capacity PDF. For this case, reliance on a traditional assumption of lognormality for the capacity would result in an overestimation of the capacity at lower quantiles. The last case study yields a bi-modal output due to the physics-based duality between localized (traveling) and post-flashover fire development. While the MaxEnt captures this bi-modality, it does not accurately reproduce the distribution obtained by Monte Carlo Simulation. Limitations of the MaxEnt method and needs for further research are discussed at the end of the paper.

A response surface modelling approach for multi-objective optimization of composite plates

Despite the rapid advancement in computing resources, many real-life design and optimization problems in structural engineering involve huge computation costs. To counter such challenges, approximate models are often used as surrogates for the highly accurate but time intensive finite element models. In this paper, surrogates for first-order shear deformation based finite element models are built using a polynomial regression approach. Using statistical techniques like Box-Cox transformation and ANOVA, the effectiveness of the surrogates is enhanced. The accuracy of the surrogate models is evaluated using statistical metrics like R2, R2adj, R2pred, and Q2F3. By combining these surrogates with nature-inspired multi-criteria decision-making algorithms, namely multi-objective genetic algorithm (MOGA) and multi-objective particle swarm optimization (MOPSO), the optimal combination of various design variables to simultaneously maximize fundamental frequency and frequency separation is predicted. It is seen that the proposed approach is simple, effective and good at inexpensively producing a host of optimal solutions.

Structural efficiency of various strengthening schemes for cold-formed steel beams: Effect of global imperfections

Cold-formed steel (CFS) has a great potential to meet the global challenge of fast-track and durable construction. CFS members undergo large buckling instabilities due to their small wall thickness. CFS beams with corrugated webs have shown great resistance towards web buckling under flexure, when compared to the conventional I-sections. However, the magnitude of global imperfections significantly affects the performance of CFS members. This paper presents the first attempt made to experimentally study the effect of global imperfections on the structural efficiency of various strengthening schemes implemented in CFS beams with corrugated webs. Different strengthening schemes were adopted for two types of beams, one with large global imperfections and the other with small imperfections. Strength and stiffness characteristics of the beams were used to evaluate the structural efficiency of the various strengthening schemes adopted. Six tests were performed with simply supported end conditions, under four-point loading conditions. The load vs. mid-span displacement response, failure loads and modes of failure of the test specimens were investigated. The test results would compensate the lack of experimental data in this area of research and would help in developing numerical models for extensive studies for the development of necessary guidelines on the same. Strengthening schemes assisted in enhancing the member performance significantly, both in terms of strength and stiffness. Hence, providing an economic and time saving solution to such practical structural engineering problems.

State-of-the-art review on Bayesian inference in structural system identification and damage assessment

Bayesian inference provides a powerful approach to system identification and damage assessment for structures. The application of Bayesian method is motivated by the fact that inverse problems in structural engineering, including structural health monitoring, are typically ill-conditioned and ill-posed when using noisy incomplete data because of various sources of modeling uncertainties. One should not just search for a single “optimal” value for the vector of model parameters but rather attempt to describe the whole family of plausible model parameters based on measured data using a Bayesian probabilistic framework. In this article, the fundamental principles of Bayesian analysis and computation are summarized; then a review is given of recent state-of-the-art practices of Bayesian inference in system identification and damage assessment for civil infrastructure. Discussions of the benefits and deficiencies of these approaches, as well as potentially useful avenues for future studies, are also provided. Our focus is on meeting challenges that arise from system identification and damage assessment for the civil infrastructure but our presented theories also have a considerably broader applicability for inverse problems in science and technology.

Accelerated Particle Swarm Optimizer for Optimizing Problems of Structural Engineering

The aim of the present work is to find a solution to non-linear constrained optimization problems of structure. Constrained optimization difficulties are practical shortcomings. The loopholes of traditional numerical methods are being removed by heuristic methods as no requirement of the functional derivatives is desired and approaches to the global way out. This article presents a “penalty guided Accelerated Particle Swarm Optimization (APSO) algorithm” to search the problem’s optimal solution in the feasible region of whole search domain. There is numerical result and comparison of the structural design optimization problems. The way out by the current perspective proves to be the better than other techniques and it can be said that our findings show better solutions to engineering problems than those earlier obtained using current algorithms.

TOSCA: a Tool for Optimisation in Structural and Civil engineering Analyses

Many structural engineering problems, e.g. parameter identification, optimal design and topology optimisation, involve the use of optimisation algorithms. Genetic algorithms (GA), in particular, have proved to be an effective framework for black-box problems and general enough to be applied to the most disparate problems of engineering practice. In this paper, the code TOSCA, which employs genetic algorithms in the search for the optimum, is described. It has been developed by the authors with the aim of providing a flexible tool for the solution of several optimisation problems arising in structural engineering. The interface has been developed to couple the programme to general solvers using text input/output files and in particular widely used finite element codes. The problem of GA parameter tuning is systematically dealt with by proposing some guidelines based on the role and behaviour of each operator. Two numerical applications are proposed to show how to assess the results and modify GA parameters accordingly, and to demonstrate the flexibility of the integrated approach proposed on a realistic case of seismic retrofitting optimal design.

New SOCP relaxation and branching rule for bipartite bilinear programs

A bipartite bilinear program (BBP) is a quadratically constrained quadratic optimization problem where the variables can be partitioned into two sets such that fixing the variables in any one of the sets results in a linear program. We propose a new second order cone representable (SOCP) relaxation for BBP, which we show is stronger than the standard SDP relaxation intersected with the boolean quadratic polytope. We then propose a new branching rule inspired by the construction of the SOCP relaxation. We describe a new application of BBP called as the finite element model updating problem, which is a fundamental problem in structural engineering. Our computational experiments on this problem class show that the new branching rule together with an polyhedral outer approximation of the SOCP relaxation outperforms a state-of-the-art commercial global solver in obtaining dual bounds.

Accelerated Particle Swarm Optimizer for Optimizing Problems of Structural Engineering

The aim of the present work is to find a solution to non-linear constrained optimization problems of structure. Constrained optimization difficulties are practical shortcomings. The loopholes of traditional numerical methods are being removed by heuristic methods as no requirement of the functional derivatives is desired and approaches to the global way out. This article presents a “penalty guided Accelerated Particle Swarm Optimization (APSO) algorithm” to search the problem’s optimal solution in the feasible region of whole search domain. There is numerical result and comparison of the structural design optimization problems. The way out by the current perspective proves to be the better than other techniques and it can be said that our findings show better solutions to engineering problems than those earlier obtained using current algorithms.

Combination of line sampling and important sampling for reliability assessment of buried pipelines

In this article, a novel approach for estimating the time-dependent reliability of a buried pipe under the impact of internal and external stresses by combining line sampling and important sampling is proposed. The stresses are analysed given the severe effect of corrosion on the performance of underground pipeline. The effect of corrosion during the design life of buried pipe decreases the capability of the pipe wall to sustain the stresses that occur internally or externally. Herein, the failure conditions of total axial stress and the ovality–stress due to point load in conjunction with the adverse effect of corrosion are examined using the proposed computational framework. The quantification of pipe failure due to these stresses is usually challenging because of the imprecision in the determination of the structural parameters. Therefore, the approach is used to capture and evaluate the influence of randomness behaviour of the parameters of pipe and soil in estimating the structural reliability. The proposed method can be applied to any structural engineering problems. In this study, a buried pipe under a roadway is examined and the effect of the underground water table on the performance of buried pipe over time is investigated. The outcome shows that a continuous increase in underground water table can aggravate the likelihood of the buried pipeline to fail. A parametric and sensitivity assessment of corrosion parameters shows their significant contribution to the probability of failure.

Modelling benchmarks for steel shear connections in column removal scenario

Owing to recent advances in modern computing techniques, finite element analysis has become a practical tool for a variety of structural engineering problems, including research challenges involving extreme loading of steel structures. In order to investigate the structural integrity and robustness of the components of steel structures, including the connections, experimental studies are required. However, these experiments are costly and time-consuming, and can be supplemented economically by using high-fidelity finite element models that can accurately mimic the response of the connections. Highly nonlinear behaviour of steel connections in a column removal scenario makes the nonlinear finite element method a suitable option in evaluating the responses and failure modes. However, some challenges must be overcome in order to reach the accurate converged solution. In this research, the challenges involved in numerical studies for simulating the performance of steel shear connections when an adjacent column has been compromised are discussed. Modelling definitions for performing highly nonlinear analyses are addressed and recommendations are made regarding obtaining verified results using available experimental data. A shear tab, as one of the simplest shear connections, is utilized to establish benchmarks in the general process of three-dimensional finite element modelling of steel shear connections. The test results from an experimental program following the same loading pattern are utilized to validate the procedures. The validated model can then be used to mimic the behaviour of other common bolted steel shear connections, such as angles or tees, due to the similarities of the behaviours and failure modes under extreme loading conditions.