Design Of Engineering Structures Research Articles

Active Materials for Functional Origami.

In recent decades, origami has been explored to aid in the design of engineering structures. These structures span multiple scales and have been demonstrated to be used toward various areas such as aerospace, metamaterial, biomedical, robotics, and architectural applications. Conventionally, origami or deployable structures have been actuated by hands, motors, or pneumatic actuators, which can result in heavy or bulky structures. On the other hand, active materials, which reconfigure in response to external stimulus, eliminate the need for external mechanical loads and bulky actuation systems. Thus, in recent years, active materials incorporated with deployable structures have shown promise for remote actuation of light weight, programmable origami. In this review, active materials such as shape memory polymers (SMPs) and alloys (SMAs), hydrogels, liquid crystal elastomers (LCEs), magnetic soft materials (MSMs), and covalent adaptable network (CAN) polymers, their actuation mechanisms, as well as how they have been utilized for active origami and where these structures are applicable is discussed. Additionally, the state-of-the-art fabrication methods to construct active origami are highlighted. The existing structural modeling strategies for origami, the constitutive models used to describe active materials, and the largest challenges and future directions for active origami research are summarized.

Generative adversarial surrogate modeling framework for aerospace engineering structural system reliability design

To effectively realize the reliability design of engineering structural system, a generative adversarial surrogate modeling (GASM) concept is proposed by innovating generative adversarial theory into surrogate modeling methods, which realize the aerospace engineering structural system reliability design adversarial modeling. In this concept, the surrogate model is adopted as a basis function to describe the functional relationship between input variables and output response; the generative adversarial theory is employed to obtain hyper-parameters of basis functions by continuous confrontation and evolution between the generation model and discrimination model. Under the GASM concept, the generative adversarial polynomial chaos expansion (GAPCE) method is developed to achieve aerospace engineering structural system reliability design. The effectiveness of presented GAPCE is verified by three examples including the nonlinear functions approximation, reliability design of flap deflection angle, and reliability estimation for turbine blisk radial deformation. The reliability level of flap deflection angle and turbine blisk radial deformation are 0.9998 and 0.9984, when the allowable values are 9° and 1.9222 × 10−3 m. Besides, the introduced approach possesses advantages of modeling performance (i.e., modeling accuracy and efficiency) and simulation properties (i.e., simulation precision and efficiency) by comparing with various methods. The efforts of this paper can provide a valuable reference for the operational safety of flap systems and engine structures and the development of reliability design theory.

Investigation on compressive strength of coral aggregate concrete: Hybrid machine learning models and experimental validation

The compressive strength of coral aggregate concrete (CAC-CS) holds importance in structural engineering and architectural design. Thus, this study establishes machine learning models for the prediction of CAC-CS and assesses their accuracy and generalization capability. The importance of input variables is analyzed by using Shapley additive explanations (SHAP) and single-variable fluctuations. A user-friendly graphical user interface (GUI) for CAC-CS prediction is developed to facilitate practical use. The accuracy of the GUI is subsequently validated through experiments. Results demonstrate that the genetic algorithm-optimized backpropagation neural network (GA-BP) model provides predictions that are closely aligned with experimental values. The GA-BP model minimizes the mean and standard deviation of its residual distribution. The performance indicators, including correlation coefficient, mean absolute error, mean absolute percentage error, mean square error, and root mean square error, of the GA-BP model are 0.98, 3.78, 10.95, 11.08, and 3.33, respectively, and are superior to those of the other tested models. The analysis of SHAP values and single-variable fluctuations reveals that among the factors influencing CAC-CS, water-binder ratio and curing age are the most sensitive. Reducing the water-binder ratio and increasing the curing age enhances CAC-CS. Additionally, the sensitivity analysis of mineral admixtures and the water-binder ratio in experiments aligns with the analysis of SHAP values and single-variable fluctuations. The error between the experimental values and GUI predictions for 14 mixture designs is less than 5 %, thus validating the accuracy and generalization capability of the GUI proposed in this study. In conclusion, this research provides practical guidelines for preparing CAC and contributes to the conservation and development of island resources.

Modal Analysis of a High-Speed Train Gearbox Housing Using Smoothed Finite Element Method

The gearbox is a crucial component of the power transmission system in high-speed trains, and realistically calculating its natural frequency is vital in avoiding system resonance. In recent decades, numerical techniques have become essential in designing engineering structures, and the finite element method (FEM) has gained widespread recognition and acceptance for its robustness and effectiveness. However, the FEM technique using linear 4-node tetrahedral elements (T4) performs poorly, leading to low accuracy and unreliable results. Consequently, complex hexahedral or second-order elements are often required for better performance. In this paper, we employ the smoothed finite element method (S-FEM), which combines the gradient smoothing technique with the standard FEM to improve calculation accuracy while still using linear tetrahedral elements. We apply S-FEM to accurately obtain the natural frequencies and mode shapes of a high-speed train gearbox using automatically generated linear tetrahedral elements. A test of tolerance to mesh distortion is also carried out on the gearbox. The results demonstrate that S-FEM can significantly enhance the computational accuracy of low-order 4-node tetrahedral elements without altering the mesh topology. It is expected that S-FEM can be an alternative to FEM in the modal analysis of complex structures in the railroad industry.

Multi-step deformation lattice structures from the rotation of unit cell

A multi-step deformation lattice structure (MSLS) is introduced. MSLS can exhibit multiple deformation pathways under compression. It leads to multi-stress plateaus in the stress–strain relationship. The underlying mechanism is caused by the rotation of the unit cell that composes the structures. Analytical models of plateau stress is first established based on the plastic hinge dissipation principle and is validated by experiments. By studying the MSLS composed of different unit cells rotated with different angles, four stress–strain relationships are obtained. Furthermore, the Young's modulus and the yield strength of MSLS show a tendency of decreasing and then increasing with the increase of rotation angle. Moreover, the rotation angle can lead to a transition of the MSLS between bending-dominated behavior and stretching-dominated behavior. Finally, it is found that MSLS has a negative Poisson's ratio and a minimum value is −0.9, and Poisson's ratio tends to zero rapidly when strains is larger than 0.3. This work provides new insights into the use of rotating unit cell to create multi-step pathways and auxetic lattices that can be used to design engineering structures with multiple tasks and application for impact protection.

Functional capabilities of the calculation methodology of railway approach embankment to engineering structures taking into account temperature effect and interaction of reinforcing piles with soil

For the smooth operation of the railway infrastructure, it is necessary to ensure the proper functioning of the roadbed embankment and engineering structures (pipe-culverts, bridges and overpasses), which corresponds to the long-term development programme of the open joint-stock company «Russian Railways» approved by the Government of the Russian Federation dated 19 March 2019 N 466-R. In the interface zones of nonhomogenous structures, namely roadbed embankments and engineering structures, ground distortion (soil settlement) occur, which leads to a change in the dynamic impact on engineering structures (pipe-culverts, bridges and overpasses) and, as a consequence, to their accelerated damage. The timeliness of the topic is determined by the target programmes for railway transport infrastructure optimisation, ensuring uninterrupted operation, reliability of engineering structures and roadbed. When designing engineering structures, a number of solutions are used to ensure reliable functioning of the roadbed — engineering structure system (pipe-culvert, bridge substructure, tunnel, laying of communications). The problem is that their application is ineffective without scientific and regulatory support and is limited by the lack of effective solutions to ensure the rigidity of the roadbed structure on the approaches to the engineering structure. In this connection, new constructive and technical solutions development for the interface of the roadbed — engineering structure system is an urgent task. The article considers railway transportations as a production process, the smooth functioning of which is ensured by smooth conjugation of approach embankments with engineering structures (bridges, pipe-culverts, underground communications). In the interface zone there are soil settlements, damage to the roadbed integrity, deformations leading to the appearance of additional dynamic forces and the emergence of irregularities and even destruction of the road pavement (on motorways), as well as damage to the engineering structures themselves.

Large Scale Shear Box Testing of Interface Between Construction Materials and Soils

Abstract The interaction between soil and building structures of various materials is defined on the basis of certain assumptions, but these are shown in many cases to be not accurate from the point of view of safe, reliable and economic design of engineering structures. Therefore, as part of our research activities, we decided to better understand the transfer of shear forces and the interaction between soil and other materials. We focused on testing materials in a shear box apparatus, where 3 types of tests were carried out: in the first stage, we tested the shear parameters of the soil in a 900 mm2 box apparatus; in the second stage, the properties of the interaction between soil and concrete were tested, and in the third stage, soil was in contact with the steel plate. The results of the tests are within the expected range of the interface friction angle between the structures and the soils.

Determination of load combination factors for code calibration

Semi-probabilistic methods are widely used for structural engineering design and assessment. In such methods, the safety and performance adequacy of structures are typically tied to partial safety factors for load and resistance, and load combination factors. There exist some heuristic strategies for estimating these partial factors, such as the design value method and first order reliability coefficient method. However, when adopted for the estimation of load combination factors, these strategies are either inaccurate or have non-unique estimates. Excessive conservatism in factor estimates is not desirable, particularly for the performance assessment of existing structures. In this study we propose a method for estimating load combination factors using a matrix linear algebra approach. Specifically, we develop a closed-form analytical expression to estimate unique load combination factors for typical code calibration problems. A comparative study of the existing heuristic approaches with the new approach is presented and demonstrated on two case studies. It is shown that the proposed method offers a valuable means of deriving load combination factors: one that avoids hueristics, and conservatism.

Nonlocal strain gradient-based isogeometric analysis of graphene platelets-reinforced functionally graded triply periodic minimal surface nanoplates

Recent developments in additive manufacturing (AM) technologies have empowered the design and fabrication of intricate bioinspired engineering structures at the nano/micro scale. However, mathematical modeling and computation of these structures are still challenging. The main target of this study is to address an efficient computational approach for predicting the mechanical behavior of graphene platelets (GPLs)-reinforced functionally graded triply periodic minimal surface (FG-TPMS) nanoplates. The computational model integrates both nonlocal elasticity and strain gradient effects into the NURBS-based isogeometric analysis of these small-scale structures. We establish advanced nanoplate models by combining three sheet-based TPMS architectures with two new porosity distribution patterns and three distribution patterns of GPLs across the thickness direction. Moreover, the present work makes a pioneering attempt to elucidate how the stiffness-hardening and stiffness-softening mechanisms influence FG-TPMS nanoplates reinforced with GPLs. Compared with two common cellular solids, the superiority of TPMS architectures' mechanical performance is demonstrated. Among all, P and IWP TPMS types along with symmetric porosity and GPLs distributions exhibit outstanding behaviors under static bending, free vibration, and dynamic instability. Furthermore, we conducted a performance analysis for the first time, showcasing the superior capabilities of TPMS architectures under dynamic in-plane compressive loads, especially when compared to isotropic plates of equal weight. The findings of this study greatly enhance our understanding of the intricate mechanical responses of GPLs-reinforced TPMS architectures at the small-scale level, contributing to future interdisciplinary applications.

Vibration and bandgap characteristics analysis of multiple beams with arbitrary connection angles

Multi-coupled beam structures can be encountered in a wide range of engineering disciplines. Due to the possible periodicity of structures, it is important to investigate their vibration and bandgap characteristics. In this study, an accurate wave-based analytical model for the in-plane vibration of a multi-coupled beam structure with arbitrary coupling angles is developed. The reflection and transmission matrices corresponding to the incident waves arriving at the angular junctions from different directions are derived. By combining these reflection and transmission matrices, the free and forced vibrations of the multi-coupled angle beam structures can be analyzed systematically and concisely. The theoretical analysis results are in good agreement with those calculated from the FEM analysis results, which verifies the accuracy of the current method. Moreover, the analytical expression of the bandgap characteristics of infinite multi-coupled beam structures is formulated. The bandgap characteristics results of the infinite multi-coupled beam structures coincide well with the frequency response function (FRF) of the finite multi-coupled beam structures, which verifies the correctness of the analytical expression of bandgap analysis. Further results show that a low-frequency and wide bandgap can be obtained by choosing the appropriate lattice constants and junction angles, which has positive significance for the efficient design and vibration attenuation of multi-coupled beam structures in practical engineering.

Field Phenotyping Monitoring Systems for High-Throughput: A Survey of Enabling Technologies, Equipment, and Research Challenges

High-throughput phenotype monitoring systems for field crops can not only accelerate the breeding process but also provide important data support for precision agricultural monitoring. Traditional phenotype monitoring methods for field crops relying on artificial sampling and measurement have some disadvantages including low efficiency, strong subjectivity, and single characteristics. To solve these problems, the rapid monitoring, acquisition, and analysis of phenotyping information of field crops have become the focus of current research. The research explores the systematic framing of phenotype monitoring systems for field crops. Focusing on four aspects, namely phenotyping sensors, mobile platforms, control systems, and phenotyping data preprocessing algorithms, the application of the sensor technology, structural design technology of mobile carriers, intelligent control technology, and data processing algorithms to phenotype monitoring systems was assessed. The research status of multi-scale phenotype monitoring products was summarized, and the merits and demerits of various phenotype monitoring systems for field crops in application were discussed. In the meantime, development trends related to phenotype monitoring systems for field crops in aspects including sensor integration, platform optimization, standard unification, and algorithm improvement were proposed.

Extreme-oriented sensitivity analysis using sparse polynomial chaos expansion. Application to train–track–bridge systems

The use of sensitivity analysis is essential in model development for the purposes of calibration, verification, factor prioritization, and mechanism reduction. While most contributions to sensitivity methods focus on the average model response, this paper proposes a new sensitivity method focusing on the extreme response and structural limit states, which combines an extreme-oriented sensitivity method with polynomial chaos expansion. This enables engineers to perform sensitivity analysis near given limit states and visualize the relevance of input factors to different design criteria and corresponding thresholds. The polynomial chaos expansion is used to approximate the model output and alleviate the computational cost in sensitivity analysis, which features sparsity and adaptivity to enhance efficiency. The accuracy and efficiency of the method are verified in a truss structure, which is then illustrated on a dynamic train–track–bridge system. The role of the input factors in response variability is clarified, which differs in terms of the design criteria chosen for sensitivity analysis. The method incorporates multi-scenarios and can thus be useful to support decision-making in design and management of engineering structures.

Predicting fracture behavior of quasi-Brittle materials using quantitatively varied geometric specimens: A design method

Fracture toughness (KIC) and tensile strength (ft) are two important parameters for describing the fracture performance of quasi-brittle materials. The accurate determination of both parameters is crucial for the safety design and stability evaluation of real engineering structures. A model and method are developed in this paper for simultaneously determining the KIC and ft for quasi-brittle materials without size effects. And using the geometric structural parameter ae as a key design parameter. This method can get KIC and ft to be obtained by fitting curves to any two sets of quantitative specimens with different geometric structures with ae ratio equal to 3. Additionally, fracture full curves and peak load prediction lines with 95% reliability are established for different fracture modes using quantitative design specimens with an ae ratio of 3. These curves and lines can accurately predict fracture behavior and peak loads of large specimens or engineering structures. The proposed design method is verified by combining experimental studies conducted by other scholars on quasi-brittle materials and the simulation results of ABAQUS software. This paper achieves the objective of accurately predicting the fracture properties of quasi-brittle material structures in the field, using quantitative laboratory specimens of various geometries.

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.

Numerical modelling of moment-transmitting timber connections

In recent years, the use of timber in construction substantially increased due to the material’s renewable nature, lower climate impact and increased economic competitiveness. Another driving factor is great improvements in modelling techniques for the design of timber structures. Suitable prediction of the connection behaviour, as a fundamental part of the structural behaviour of timber structures, is crucial for a more economic and reliable design. However, the more realistic and complete connection models, the more complex and difficult to handle they are, which might hinder their practical application. A good trade-off between complexity and computational efficiency can be achieved with the so-called Beam-On-Foundation (BOF) method, which is applied herein in a 2-step hierarchical model to analyse and predict the rotational stiffness, ductile capacity and load distribution among fasteners of four different configurations of moment transmitting beam-to-column timber-to-timber connections. The connection model is validated with experiments on the global response of the connection as well as with a 3-D solid FEM model. The herein proposed connection model well predicted the overall connection response and provided insight into the local fastener behaviour. As compared to the 3-D solid model, which additionally gives access to more realistic local stresses in the timber, the 2-step model is however much more efficient with a great reduction of computation time. This makes the approach suitable for parametric studies and the analysis and engineering design of timber structures.

Prediction of flexural and uniaxial compressive strengths of sea ice with optimized recurrent neural network

The mechanical properties of sea ice are important parameters for determining the ice load on engineering structures, such as offshore platforms, ships and ports. In the investigations of the mechanical properties of sea ice, experimental data samples are few, whereas the results predicted from an empirical formula feature a significant error and thus do not satisfy engineering requirements. Therefore, in this study, the stochastic gradient descent method combined with the Adam optimization algorithm is used to optimize a recurrent neural network (RNN), and an RNN model is established to predict the bending and compressive strengths of sea ice. This model can output the mechanical properties of sea ice using only physical parameters such as sea ice temperature, salinity, density, and loading rate. The overall average error in the prediction of the bending and compressive strengths of sea ice is less than 20%. The model is compared with an empirical formula, back-propagation neural network, and genetic-algorithm back-propagation neural network to predict the mechanical properties of sea ice. The results show that the model is optimized in terms of goodness-of-fit, mean relative error, mean absolute error, and mean square error, which implies that the optimized RNN is more suitable for predicting the bending and compressive strengths of sea ice. The model can be applied to rapidly and accurately predict the mechanical properties of sea ice using easily measured physical sea ice parameters, thus providing a reference for the anti-ice design of ocean engineering structures.

A dimensional analysis based multi-scale thermal damage framework of brittle materials at elevated temperatures

Research on macro-scale damage prediction and micro-scale damage mechanisms of brittle materials at elevated temperatures is extremelyimportantto the design and application of practical engineering structures. In this study, a multi-scale thermal damage framework is proposed for both describing macro-scale damage evolution and collective micro-scale cracks behavior of brittle materials atelevatedtemperatures. In the framework, the evolution equation of ideal micro-crack system is deduced to establish the relationship between the macro-scale damage and the micro-scale crack nucleation and growth based on dimensional analysis method. To support the multi-scale thermal damage framework, the multi-scale damage of the benchmark fracture test of the Brazilian disk at elevated temperatures is predicted and compared with the experimental results. The results support the effectiveness and ability of the multi-scale thermal damage framework, which can achieve thermal damage prediction of brittle materials in macro-scale as well as physical mechanisms description in micro-scale.

Nonlinear analysis of reinforced concrete buildings with different heights and floor systems

Most civil structures exhibit nonlinear behavior during moderate to severe earthquakes. Consequently, inelastic analysis is needed for seismic design. Several dynamic and static analysis methods are available for the assessment and design of engineering structures. Two of the available methods in terms of nonlinear dynamic time history analysis and nonlinear static analysis, which is known as pushover analysis, are employed herein to comprehensively study and investigate the seismic performance of multi-story building structures with different floor systems. Moreover, the study is extended to assess the actual values of the response reduction/modification factor (R-factor) for each building model, then evaluate the values with the code-recommended design values. Three-dimensional finite element building models with 5, 10 and 15 stories are developed for the evaluation process. The advanced computer program ETABS is used for developing and analyzing the buildings considering material and geometrical nonlinearity. A suit of seven earthquake records is considered and scaled according to the ASCE-16 seismic design code to excite the building models. The obtained results evidently reveal that the type of floor slab significantly impacts the seismic response of the building. More specifically, the effects of floor slabs on seismic demands are more evident in low- and mid-rise buildings. In addition, the type of slab system and height of the building have more influence on the response modification factors, especially for low-rise building models.

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.

A reliability-based design and optimization strategy using a novel MPP searching method for maritime engineering structures

PurposeIn order to solve the problems faced by First Order Reliability Method (FORM) and First Order Saddlepoint Approximation (FOSA) in structural reliability optimization, this paper aims to propose a new Reliability-based Design Optimization (RBDO) strategy for offshore engineering structures based on Original Probabilistic Model (OPM) decoupling strategy. The application of this innovative technique to other maritime structures has the potential to substantially improve their design process by optimizing cost and enhancing structural reliability.Design/methodology/approachIn the strategy proposed by this paper, sequential optimization and reliability assessment method and surrogate model are used to improve the efficiency for solving RBDO. The strategy is applied to the analysis of two marine engineering structure cases of ship cargo hold structure and frame ring of underwater skirt pile gripper. The effectiveness of the method is proved by comparing the original design and the optimized results.FindingsIn this paper, the proposed new RBDO strategy is used to optimize the design of the ship cargo hold structure and the frame ring of the underwater skirt pile gripper. According to the results obtained, compared with the original design, the structure of optimization design has better reliability and stability, and reduces the risk of failure. This optimization can also better balance the relationship between performance and cost. Therefore, it is recommended for related RBDO problems in the field of marine engineering.Originality/valueIn view of the limitations of FORM and FOSA that may produce multiple MPPs for a single performance function, the new RBDO strategy proposed in this study provides valuable insights and robust methods for the optimization design of offshore engineering structures. It emphasizes the importance of combining advanced MPP search technology and integrating SORA and surrogate models to achieve more economical and reliable design.