Analysis Of Engineering Structures Research Articles

A Case Study of Problem-Based Learning from a Civil Engineering Structural Analysis Course

A Case Study of Problem-Based Learning from a Civil Engineering Structural Analysis Course

Microscopic deformation mechanism and characteristics of carbonaceous slate during the creep process

Carbonaceous slate exhibits a significant creep deformation that seriously affects the construction and operation of underground projects. To investigate the microstructural changes characteristics and reveal the microscopic deformation mechanism of the carbonaceous slate during the creep process, multiple methods were performed, including the triaxial creep test, SEM and MIP. The following conclusions were drawn: The rock samples underwent three stages during the creep test: microporosity closure at a low-stress level, material densification at an intermediate stress level, and microcracks emerging and expanding to failure at the high stress. The creep deformation was particularly significant in the first and third processes. The lamellar particles are compressed or bent under stress in parallel and vertical directions, showing the anisotropic properties of deformation. The deformation of the rock sample is related to the angle between the bedding and the orientation of major principal stress, and the effect of the anisotropy decreases with the increased stress level. The sprouting and expansion of microfractures occur at high-stress levels, showing pressure dissolution of mineral particles, migration of very fine particles, and cement damage between lamellar particles. Finally, the horizontal samples formed a combined rupture surface composed of the laminar surface and the fracture surface intersecting it, showing brittle damage, while the vertical samples formed a fracture surface parallel to the laminar surface, showing a ductile damage pattern. Those results could provide the basis for a further understanding of the mechanical properties of carbonaceous slate and the improvement of its creep model and parameters. It was significant for the stability analysis and deformation prediction of engineering structures using numerical simulation.

Simulation of Near-Fault Pulse-Like Ground Motion and Investigation of Seismic Response Characteristics in Subway Stations

ABSTRACT The availability of reasonable input ground motions is a prerequisite for investigating the seismic response of near-fault structures. Currently, realistic near-fault pulse-like seismic records are lacking internationally. Moreover, the existing records exhibit distinct regional characteristics, which hardly meet the demand for seismic analysis of various engineering structures. Collecting and analyzing the pulse-like seismic records, a linear attenuation expression is proposed in this study to describe the time-varying frequency amplitude decay over duration. On this basis, a new model compatible with horizontal and vertical components is developed for near-fault ground motions. The model parameters are estimated for 100 near-fault records and regressed against the prediction equations. The variability and correlation of the ground motion in two directions are analyzed through the residual correlation matrix. Finally, taking a subway station as the engineering background, the effects of velocity pulse and vertical seismic motions on the underground structures are addressed through simulated motions and realistic records. Compared with existing approaches, the proposed method not only aligns with the time-frequency distribution characteristics of near-fault ground motions but also considers the correlation between horizontal and vertical components. In addition, consistent with the results from recorded ground motions, the synthetic motions yield enlarged seismic responses of the underground structure, thereby guaranteeing analysis accuracy for the engineered structures subjected to pulse-like ground motions. The inclusion of vertical excitation prominently amplifies the vertical displacement of the slab under pulse-like ground motions.

Analysis of engineering structures and components created using the generative design

Generative design is an AI-driven process to explore all the possible design variants, it mimics nature’s evolutionary approach to create an optimised final design. Generative design and additive manufacturing are interlinked because additive manufacturing is able to fabricate complex geometries. Generative algorithms have demonstrated their effectiveness in reducing costs, weight and development time across various industries including automotive and aerospace. This paper analysed components and structures to show the advantages of using the generative design method, problems encountered and issues that could be addressed to improve the final designs. Loading conditions, stress concentrations and manufacturability of the generative-designed components were discussed in the paper.

Partitioned analysis of acoustic fluid–solid-saturated porous medium interaction problems by a generalized saturated porous medium model and localized Lagrange multipliers

A partitioned formulation of acoustic fluid–solid-saturated porous medium interaction problems is presented based on a generalized saturated porous medium (GSPM) model and a new localized Lagrange multiplier (LLM) method. In this formula, all various media are modeled as the GSPM and a novel, powerful continuous condition is proposed for the interaction interfaces. This condition applies not only to interfaces of two partitions but also to intersections of simultaneous interactions of any number of media with different porosities. A (u, p) frame is introduced between partitions and the LLM fields are used to connect partitions with the frame. Furthermore, finite element spatial discretization and time integration strategies are provided for both matching meshes and nonmatching meshes. This partitioned formulation is validated numerically by computing the dynamic response of a horizontally layered seawater-seabed-bedrock model subjected to plane waves. The application scenarios for this method include, but are not limited to, the dynamic coupling analysis of marine engineering structures and hydraulic engineering structures. Future work will present and discuss a set of benchmarks and applications that involve the responses of existing dams to seismic excitation.

Reliability of Monte Carlo simulation approach for estimating uniaxial compressive strength of intact rock

The strength of rock has significant influence on its performance, and is, therefore, a key input during modelling and analysis of mining and geotechnical engineering structures. The uniaxial compressive strength (UCS), which is a popular parameter to quantifying rock strength can be determined in the laboratory using suggested method by International Society of Rock Mechanics (ISRM). However, the laboratory determination of UCS consumes time, it is costly, and sometimes may not be feasible to perform because of different conditions of rock. Hence, this study attempts to employ Monte Carlo simulation (MCS) approach to estimate UCS, and to overcome various uncertainties associated with UCS estimation. To use MCS approach for UCS estimation, block punch index (BPI), Brazilian tensile strength (BTS), point load index (IS(50)), and P-wave velocity (Vp) were selected as the model inputs. A multiple linear regression (MLR) equation was developed and used to predict UCS by the MCS approach. The methodology was applied to estimate UCS using real BPI, BTS, Is(50), and Vp data as inputs. The proposed approach simulated UCS values that are consistent with UCS values measured in the laboratory. The mean of the UCS values simulated through the MCS approach is 119.10 MPa, while the mean of the UCS values measured in the laboratory is 118.42 MPa. In addition, hypothesis testing revealed that the Brazilian tensile strength (BTS) is the parameter with the most influence on UCS of rock for the site investigated.

Stochastic simulation of earthquake ground motions based on improved finite-fault model

In the seismic reliability analysis of engineering structures, it is often necessary to adopt stochastic ground motions as inputs. However, the current methods used to generate stochastic samples, such as the spectral representation method, the spectral transfer method, the dynamic source method, the physical source method, and the comprehensive method, etc., are not very convincing since they do not contain the complete information of seismic occurrence and propagation. In this study, an improved finite-fault model for stochastic simulation of ground motions is proposed. Compared to existing stochastic finite-fault model, the improved model considers the propagation law of phase spectrum, local site effect, and the impact of wave velocity on the arrival time of seismic waves. Besides, identification and statistics of model parameters are carried out based on ground motion records. Reasonable samples of stochastic ground motions are then obtained. In addition, parameter sensitivity analysis is conducted to determine critical random variables, which reduces the complexity of stochastic simulation. Simulation results are compared with recorded accelerograms from the 1999 Chi-Chi earthquake. It is shown that the improved stochastic finite-fault model is reliable, and the acceleration response spectra of simulated samples are consistent with those of station records collected at various site classes and the design spectra.

Estimating IDF curves under changing climate conditions for different climate regions

Abstract Although climate models can highlight potential shifts in intensity–duration–frequency (IDF) curves, their limited geographical and temporal resolutions limit their direct use in predicting sub-daily heavy precipitation. To use global or regional model outputs to predict urban short-term precipitation, approaches that give the requisite level of spatial and temporal downscaling are required, and these processes remain one of the difficulties that have demanded intensive effort in recent years. Although no novel methods are given in this work, there are few studies in the literature that investigate the impact of climate change on the analysis and design of infrastructure-related engineering structures. Therefore, the purpose of this research is to determine the potential changes in IDF curves because of climate change. The equidistance quantile matching method was used to turn future rainfall forecast data from global climate models (HadGEM2-ES, MPI-ESM-MR, and GFDL-ESM2M) corresponding to RCP4.5 and RCP8.5 scenarios into standard duration rainfall data, and new IDF curves were generated. These IDF curves corresponded very well with those generated from observed data (R2 ≈ 1). The HadGEM2-ES model predicts up to a 25% rise in rainfall intensity, whereas the other two models expect up to a 50% drop.

An efficient model for vehicle-track-soil dynamic interaction based on Green's function, cyclic calculation and multi-time-step solution methods

To meet the demand for dynamic analysis of railway engineering structures accurately, the numerical model considering the coupled vibrations of the vehicle, track and substructure becomes a research tendency. However, it generates low-efficiency risk due to high degrees of freedom of the model. In this paper, a hybrid integration method based on the implicit scheme and Green's function is introduced and combined with the cyclic calculation method and the multi-time-step solution method to optimize the dynamic procedure for high-efficient solution of vehicle-track-soil (VTS) dynamic interaction. The VTS system is divided into two sub-models: vehicle-rail sub-model and the track slab-soil sub-model. The compatibility between two sub-models is maintained by the equilibrium mechanism of the fasteners’ interaction forces. Through detailed validation, discussion and numerical studies, the feasibility and efficiency of the proposed model combing the hybrid integration method and two optimized dynamic solution strategies are fully demonstrated in the dynamic assessment of VTS dynamic interaction.

The influence of load correlation on vibration fatigue damage of symmetrical notched cantilever beam structures

AbstractThis paper aims to develop an innovative multiaxial fatigue criterion based on the stress invariant, in order to perform the fatigue damage analysis of engineering structures subjected to random vibrations. First, the change rule of structural vibration fatigue lifetime under different load correlation conditions is systemically studied by experiments. Then, the finite element analysis of such a structure, including the modal analysis and the spectral analysis, is implemented in order to compute the response stress power spectral density (PSD) functions at the critical fatigue‐prone location under coupled random acceleration base excitations. Finally, the influence rule of load correlation on the vibration fatigue damage of such a symmetrical structure is theoretically illustrated through formula derivation. Moreover, combined with the critical distance theory point method, the proposed time/frequency‐domain multiaxial fatigue criterion is applied for vibration fatigue lifetime evaluation of the structure. The lifetime prediction accuracy of the proposed multiaxial fatigue criterion is verified by comparing with the vibration fatigue test results.

Bayesian model averaging for probabilistic S-N curves with probability distribution model form uncertainty

Reliability analysis of engineering components or structures heavily relies on accurately estimating the fatigue properties of materials. However, significant uncertainty exists regarding the distribution form and value in fatigue data, posing significant challenges in constructing a robust probability fatigue model. To address this challenge, we propose a Bayesian model averaging (BMA) method to incorporate model form uncertainty into the estimation of the probability density of fatigue life. The performance of BMA was verified through numerical experiments using both simulated and experimental data. The results highlight the robustness and reliability of BMA compared to individual models, as it effectively incorporates model form uncertainty. The proposed BMA model offers a general framework for developing probabilistic fatigue models with high robustness and accuracy in their predictions. This model contributes to advancing the field of reliability analysis by addressing the challenges posed by uncertainty and enhancing the understanding of fatigue properties for engineering components and structures.

Analytical study on the propagation of semi-infinite crack due to SH-wave in pre-stressed magnetoelastic orthotropic strip

This study deals with the propagation of semi-infinite crack impacted by SH-wave propagation in a pre-stressed magnetoelastic orthotropic strip (PMOS). The proposed analytical model makes use of the Wiener-Hopf (WH) approach and two-sided Fourier integral transforms (FIT). The exact formulation of the stress intensity factor (SIF) for constant concentrated force (CCF) has been established in closed form. The effect of influencing factors, including the crack length, crack speed, magnetoelastic parameter, horizontal compressive pre-stress (HCP)/horizontal tensile pre-stress (HTP), vertical compressive pre-stress (VCP)/vertical tensile pre-stress (VTP), and anisotropy parameter on the SIF in the considered strip has been unraveled. The proposed model is compared with the case of a pre-stressed magnetoelastic isotropic strip (PMIS) and some peculiarities, along with the influence of orthotropy have been reported. The significant results reported in the present manuscript reveal that magnetoelasticity and pre-stress is a prominent factor impacting crack propagation in elastic material with orthotropy configuration properties and has a significant impact on the SIF, which has not been taken into account in the existing literature on the subject of fracture mechanics. The present investigation helps with structural and reliability analysis, fracture propagation, material durability and failure of engineering structures.

Collaborative and Adaptive Bayesian Optimization for bounding variances and probabilities under hybrid uncertainties

Uncertainty quantification (UQ) has been widely recognized as of vital importance for reliability-oriented analysis and design of engineering structures, and three groups of mathematical models, i.e., the probability models, the imprecise probability models and the non-probabilistic models, have been developed for characterizing uncertainties of different forms. The propagation of these three groups of models through expensive-to-evaluate simulators to quantify the uncertainties of outputs is then one of the core, yet highly challenging task in reliability engineering, as it involves a demanding double-loop numerical dilemma. For addressing this issue, the Collaborative and Adaptive Bayesian Optimization (CABO) has been developed in our previous work, but it only applies to imprecise probability models and is only capable of bounding the output expectation. We present a substantial improvement of CABO to incorporate all three categories of uncertainty models and to bound arbitrary probabilistic measures such as output variance and failure probability. The algorithm is based on a collaborative active learning mechanism, that is, jointly performing Bayesian optimization in the epistemic uncertainty subspace and Bayesian cubature in the aleatory uncertainty subspace, thus allowing to adaptively produce training samples in the joint uncertainty space. An efficient conditional Gaussian process simulation algorithm is embedded in CABO for acquiring training points and Bayesian inference in both uncertain subspaces. Benchmark studies show that CABO exhibits a remarkable performance in terms of numerical efficiency, accuracy, and global convergence.

Phase-field modeling of stochastic fracture in heterogeneous quasi-brittle solids

Owing to the random nature of heterogeneity, damage and fracture behavior of quasi-brittle materials exhibits a considerable degree of uncertainty. Computational modeling of stochastic fracture in quasi-brittle materials has become an indispensable tool for analysis and design of engineering structures. To this end, we present in this paper a computational framework to capture probabilistic fracture in heterogeneous quasi-brittle solids by combining the random field theory and the phase-field cohesive zone model (PF-CZM). The spatial variation of the material strength and fracture energy is represented by a cross-correlated bivariate random field generated by the Karhunen–Loève expansion. The recently proposed PF-CZM is employed to simulate the stochastic crack nucleation and propagation in quasi-brittle solids. The objectivity of the Monte-Carlo simulation is achieved by imposing a specific condition on the phase-field length scale parameter and the correlation length of the random field. In particular, upon this condition the width of the fracture process zone (FPZ) is considerably smaller than the correlation length of the random field such that the material inside the FPZ does not exhibit significant spatial variations of mechanical properties. As the fracture energy is intrinsically incorporated in the PF-CZM, it is unnecessary in this case to explicitly consider the FPZ width. The resulting probabilistic PF-CZM together is applied to the Monte-Carlo simulations of fracture in concrete structures of different geometries. It is shown that the stochastic simulation results are insensitive to both the phase-field length scale parameter and the finite element mesh discretization as in the previous deterministic analyses. Enhanced with the specific condition on the involved characteristic lengths, the PF-CZM provides a viable tool for stochastic simulations of damage and fracture in quasi-brittle structures.

Experimental analysis of the railway truss bridge using modern methods

Experimental analyses of civil engineering structures have been carried out for decades. While in the past, mainly static load tests of new structures were performed, a few severe accidents with fatal results caused a lot of emphasis to be given to monitoring existing structures recently. The need for continuous or periodical evaluation of static or dynamic behaviour of structures in operation led to a good progress in the development of modern methods for experimental analysis. The paper deals with one of the modern methods of experimental analysis – the DIC method. Possible application of the DIC method was verified by an experimental analysis of a railway truss bridge in Rataje nad Sázavou. The experiment was divided into two stages. During the experiment, deflections of the bridge measured by LVDT sensors and the DIC method were compared. Technological problems of the DIC method were found in the first stage. One of the problems was related to stability of the camera tripod. A solution was proposed and verified during the second stage.

An electro-mechanical shell model for predicting stochastic vibration behavior of smart piezoelectric structure utilizing the Spectral-Tchebyche method

Random vibration analysis is an important indicator for conducting fatigue analysis as well as reliability analysis of engineering structures. At present, the vibration analysis for plate and shell structures is mostly limited to simple plate and shell structures, while the analysis of random vibration of smart piezoelectric structures is really scarce. In this paper, an electro-mechanical coupling model is proposed to examine the stochastic vibration characteristics of smart piezoelectric structures based on the Spectral-Tchebychev method (S-T), whose type of random excitation is pseudo-excitation method (PEM). By comparing the FEM, the literature and experiments, it is observed that S-T electromechanical coupling model proposed in this paper consumes much less computational time in predicting the stochastic response than the existing methods. Secondly, we focus on the effect of the piezoelectric layer on the stochastic vibration characteristics of the host structure. Then, artificial spring technique is also adopted to simulate the general boundary of the coupled electromechanical model for the random vibration analysis. The research work on the random vibration of piezoelectric smart structures in this paper can provide practical engineering experience for the design and equipment of smart structures.

An efficient time-variant reliability analysis strategy embedding the NARX neural network of response characteristics prediction into probability density evolution method

Stochastic response analysis and first-passage reliability evaluation of multi-degree of freedom nonlinear systems subject to non-stationary seismic excitation have been a critical issue in the field of compound random vibrations, yet remain unsolved. This study extends the time-varying reliability analysis method based on the probability density evolution method, making it applicable to the problem of stochastic response analysis and first-passage reliability evaluation of complex engineering structures. To efficiently represent the response of representative samples, a nonlinear autoregressive with exogenous inputs neural network model is introduced in this study. After comparing three different optimization methods, the neural network model based on the Bayesian regularization algorithm is selected as the predictor for the response of complex nonlinear systems, enabling efficient stochastic response analysis and time-varying reliability evaluation. To this end, leveraging the high efficiency of the probability density evolution method, the analysis of time-varying reliability of large-scale structures has been successfully conducted. Furthermore, the proposed method can be easily expanded to dynamic reliability evaluation problems represented by extreme value theory. Three numerical examples are quoted to demonstrate the applicability and advantages of the proposed method in the field of first-passage and extreme-value problems of systems, which are also compared with PCE, SVR, and Kriging. The results from these numerical examples indicate that the proposed method can effectively reduce the required calculational cost for the reliability analysis of complex structures while maintaining high analytical accuracy.

An efficient approach for evaluating the reliability of engineering structures using support vector machine with clustering algorithm

ABSTRACT An implicit limit state function is predicted with a support vector machine (SVM) for reliability analysis of engineering structures to reduce the number of finite element analyses. The accuracy and predictability of the SVM method are reduced considerably by noise in data. In this paper, density-based spatial clustering of applications with noise (DBSCAN) is applied to reduce the noise in training samples for SVM regression. Then, the SVM model is linked with Monte Carlo simulation (MCS) to find out the reliability of the engineering structures. Four different examples of static and dynamic problems are solved to show acceptability and efficiency of the proposed method. It is observed that the proposed method is suitable for a smaller number of performance function calls. Direct MCS, artificial neural network-based MCS and response surface methods have been used to examine the effectiveness of the algorithm.

A dynamic Gaussian process surrogate model based on the grasshopper optimization algorithm for non-probabilistic reliability analysis of complex structures

In practical complex engineering structures, the performance function (PF) for reliability analysis is commonly implicit and highly nonlinear. Commonly used surrogate models are appropriate for structural reliability analysis with an implicit PF. However, these methods require hundreds of PF values, which are time-consuming to obtain by adopting numerical analysis, such as finite element analysis (FEA). Therefore, since non-probabilistic reliability analysis does not require a large number of samples, it has great development potential. In this paper, a dynamic Gaussian process (GP) surrogate model based on the grasshopper optimization algorithm (DGP-GOA) is proposed for the non-probabilistic reliability analysis. First, with the help of the scale factor of the convex set model, the non-probabilistic reliability analysis problem is transformed into an unconstrained optimization problem. Second, the DGP-GOA fits the PF by constructing a GP surrogate model with a small dataset. Third, the GOA is used to search for the global optimal solution to obtain a non-probabilistic reliability index. Then, a dynamic retraining strategy is proposed to improve the fitting accuracy and efficiency. The results demonstrate that the proposed method is highly applicable to the non-probabilistic structural reliability analysis of complex engineering structures.

Seismic response of a single-layer reticulated dome: Fault-to-structure simulation

Seismic response of large-span spatial structures is particularly susceptible to soil-structure interaction and spatial variability of ground motions. Yet existing seismic analysis fail to properly account for the foundation soil, and the ground motions are commonly determined based on empirical formulas. Physics-based 3D seismic simulation has recently been developed to provide site-specific ground motions, and the fault-to-structure simulation facilitates the incorporation of seismic wave field and soil-structure interaction into the analysis of engineering structures. This article innovatively implements the fault-to-structure simulation on a single-layer reticulated dome. Structural response of fault-to-structure model and two commonly used models is compared and discussed. Results show that the damping of soil reduces the stress and horizontal acceleration of the reticulated dome, while the soil movement leads to an increase in displacement. Noticeably, the seismic propagation characteristics increase vertical acceleration of the inner rings. Compared with fault-to-structure model, the multi-support excitation model and uniform excitation model reduce the vertical displacement of 29.1% and 50.1%, respectively, and reduce the vertical acceleration of the inner rings of 59.9% and 69.0%, respectively.