Model Structures Research Articles

Theoretical and numerical modeling of a shallow foundation stiffness based on the theory of elastic half-space

Abstract The paper presents the derivation of equations for the calculation of the spring constants ky, kz, and kϕ and the spring coefficients βy, βz , and βϕ used in the modeling of a foundation with a rectangular base on elastic soil. All the equations were derived based on information provided in literature, for example, Barkan, Gorbunov-Possadov, and Whitman et al. To demonstrate the application of these equations in practice, two numerical models of a reinforced concrete frame structure that was built on soil were created using Abaqus FEA software. Model A represents a complex three-dimensional numerical model that consists of a reinforced concrete frame structure, which has a soil layer beneath it. Model B represents a simple three-dimensional numerical model consisting of a reinforced concrete frame structure, where the stiffness of the soil layer beneath the structure was modeled with vertical, horizontal, and rocking spring constants applied to the bottom of each foundation. Due to the nonlinear boundary condition used in the supports of the concrete frame model, such as contact and friction, all the involved loads were incorporated into a single load case, and a large displacement formulation was used in the analysis. The authors focused on the method of a simplified modeling of frame structures founded on soil. To conduct comparative analyses, two columns and two beams from each model were selected, from which the internal forces and displacements were compared. The findings of the comparative analysis are presented in tables and then discussed.

Electromechanical modelling and vibration control analysis of cyclic symmetric structures with a piezoelectric vibration absorber

This study explored the application of piezoelectric vibration absorbers (PVAs) equipped with synthetic circuits for the vibration control of cyclic symmetric structures. A cyclic symmetric electromechanical model (CSEM) integrated with PVAs was introduced to facilitate the design and optimization of the PVAs. This model was formulated analytically based on the circulant matrix theory, and the dynamic response was computed using the complex mode superposition method with explicit consideration of the intrinsic resistance within the PVA. A case study involving a simplified blisk model and an experimental test rig was conducted to validate the proposed CSEM. The validity of the model was confirmed through a comparative analysis of both the natural and dynamic characteristics obtained using the finite element model and experimental results. Furthermore, the impact of synthetic circuit parameters (inductance and negative capacitance values) and the placement of piezoelectric patches on the vibration control performance of PVA were investigated. The results suggested that the optimal inductance coincide with the analytical value, whereas the optimal negative capacitance of the series was slightly greater than the intrinsic capacitance of the patch. Additionally, mounting the piezoelectric patch on the blade root improved the vibration control.

Lessons learned in 3D modelling of underground structures

Gloria Wong and Trieska Wahyudi share their experiences in the 3D modelling of underground structures (primarily stations) for design, including the key lessons they have learned and the process flows they have developed consequently.

Predicting resilient modulus for pavement design: a comprehensive review of artificial neural network applications

ABSTRACT The Resilient Modulus (M R) is an essential parameter describing the mechanical behaviour of pavement materials, but the tests to obtain it are time-consuming and costly. Therefore, machine learning, especially Artificial Neural Networks (ANN), has recently been an effective alternative for predicting M R. Although there has been an increase in articles on ANN for predicting M R, no systematic review has yet categorised the publications, algorithm structures, predictive parameters, and model accuracy. This review provides a comprehensive overview of studies using ANN to predict M R for pavement materials. It examines various ANN subtypes and model structures, addressing a gap in understanding these models and identifying prevalent predictive parameters. Twenty-five peer-reviewed articles published in English-language journals were identified using keywords related to neural networks and M R. For fine soils, all models reviewed use moisture content as a predictive parameter. In contrast, granular soils models typically include stress state and physical characteristics of the materials. For recycled or stabilised materials, there is significant variability in model inputs. This review underscores the potential of ANN in enhancing predictive models for M R in pavement engineering, pointing towards future research and application refinement in this area of study.

Dynamic Analysis and Vibration Control of Additively Manufactured Thin-Walled Polylactic Acid Polymer (PLAP) and PLAP Composite Beam Structures: Numerical Investigation and Experimental Validation

This study primarily presents a numerical investigation of the dynamic behavior and vibration control in thin-walled, additively manufactured (AM) beam structures, validated through experimental results. Vibration control in thin-walled structures has gained significant attention recently because vibrations can severely affect structural integrity. Therefore, it is necessary to minimize these vibrations or keep them within acceptable limits to ensure the structure’s integrity. In this study, the AM beam structures were made of polylactic acid polymer (PLAP), short carbon fiber reinforced in PLAP (SCFR|PLAP), and continuous carbon fiber reinforced in PLAP (CCFR|PLAP), with 0°|0° layer orientations. The finite element modeling (FEM) of the AM beam structures integrated with macro fiber composite (MFC) was carried out in Abaqus. The initial four modal frequencies of bending modes (BMs) and their respective modal shapes were acquired through numerical simulation. It is crucial to highlight the numerical findings that reveal discrepancies in the 1st modal frequencies of the beams, ranging up to 1.5% compared to their respective experimental values. For the 2nd, 3rd, and 4th modal frequencies, the discrepancies are within 10%. Subsequently, frequency response analysis (FRA) was carried out to observe the frequency-dependent vibration amplitude spectrum at the initial four BM frequencies. Despite discrepancy in the amplitude values between the numerical and experimental datasets, there was consistency in the overall amplitude behavior as frequency varied. THz spectroscopy was performed to identify voids or misalignment errors in the actual beam models. Finally, vibration amplitude control using MFC (M8507-P2) was examined in each kinematically excited numerical beam structure. After applying a counterforce with the MFC, the controlled vibration amplitudes for the PLAP, SCFR|PLAP, and CCFR|PLAP configurations were approximately ±19 µm, ±16 µm, and ±13 µm, respectively. The trend in the controlled amplitudes observed in the numerical findings was consistent with the experimental results. The numerical findings of the study reveal valuable insights for estimating trends related to vibration control in AM beam structures.

Method for Constructing a Compact Component Mode Synthesis Model for Analyzing Nonlinear Normal Modes of Structures with Localized Nonlinearities

Abstract Nonlinear dynamics analysis is a crucial topic in mechanical and aerospace engineering. The analysis of nonlinear normal modes (NNMs) provides an effective mathematical tool for interpreting complex nonlinear vibration phenomena. Unlike the invariant normal modes of linear systems, NNMs often exhibit frequency-energy dependence and cannot be computed using the traditional eigen-decomposition method. Calculating NNMs relies on numerical methods that involve expensive iterative computations, especially for systems with numerous degrees-of-freedom. To relieve computational costs, this paper proposes a mode selection method for the component mode synthesis (CMS) technique to enable compact reduced-order modeling of structures with localized nonlinearities. The reduced-order model is then combined with a numerical continuation scheme to establish a low-cost NNM analysis framework. This framework can efficiently predict the NNMs of high-dimensional finite element models. The proposed framework is demonstrated on an I-shaped cantilever beam with localized nonlinear stiffness. The results show that the proposed approach can identify the key modes in the CMS modeling procedure. The NNMs of the nonlinear I-shaped beam structure can then be analyzed with a significant reduction in computational costs.

SEC-SAXS/MC Ensemble Structural Studies of the Microtubule Binding Protein Cdt1 Show Monomeric, Folded-Over Conformations.

Cdt1 is a mixed folded protein critical for DNA replication licensing and it also has a "moonlighting" role at the kinetochore via direct binding to microtubules and the Ndc80 complex. However, it is unknown how the structure and conformations of Cdt1 could allow it to participate in these multiple, unique sets of protein complexes. While robust methods exist to study entirely folded or unfolded proteins, structure-function studies of combined, mixed folded/disordered proteins remain challenging. In this work, we employ orthogonal biophysical and computational techniques to provide structural characterization of mitosis-competent human Cdt1. Thermal stability analyses shows that both folded winged helix domains1 are unstable. CD and NMR show that the N-terminal and linker regions are intrinsically disordered. DLS shows that Cdt1 is monomeric and polydisperse, while SEC-MALS confirms that it is monomeric at high concentrations, but without any apparent inter-molecular self-association. SEC-SAXS enabled computational modeling of the protein structures. Using the program SASSIE, we performed rigid body Monte Carlo simulations to generate a conformational ensemble of structures. We observe that neither fully extended nor extremely compact Cdt1 conformations are consistent with SAXS. The best-fit models have the N-terminal and linker disordered regions extended into the solution and the two folded domains close to each other in apparent "folded over" conformations. We hypothesize the best-fit Cdt1 conformations could be consistent with a function as a scaffold protein that may be sterically blocked without binding partners. Our study also provides a template for combining experimental and computational techniques to study mixed-folded proteins.

A Review on the Mathematical Modelling of the Grain Storage Ecosystemin Storage Structures and Bags

The grain storage ecosystem comprises of biotic and abiotic factors that interact during grain storage, and their interaction affects the quality of the stored grains over time. The understanding and management of the complex interaction of the biotic and abiotic factors in the stored grain ecosystem is crucial to decision-making and in the development of management strategies for grain farmers and elevators. Mathematical modelling has emerged as an essential approach in tackling complex systems such as the stored grain ecosystem. This review explores the application of mathematical modelling in the grain storage ecosystem and synthesizes the existing literature, highlighting the various mathematical models and approaches to solving issues relating to the grain storage ecosystem. The review will be helpful to researchers and stored grain experts in the grain industry on the various mathematical modelling approaches and equations that can be employed to solve problems associated with grain storage in order to better manage the quality of stored grain.

Low-Temperature Metallomesogen Model Structures and Mixtures as Potential Materials for Application in Commercial Liquid Crystal Devices

The present work was the preliminary study of phase diagrams and miscibilities of low-temperature metallomesogen (MOM) model structures based on rod-like palladium (Pd) alkyl/alkoxy-azobenzene metal complexes and their mixtures with commercial liquid crystal materials for potential application. The initial results indicated the accessible temperature range and mesgenic miscibility between parent ligand, MOMs and commercial liquid crystal mixtures. The eutectic ligand/MOM composition with other MOMs and commercial nematic liquid crystal materials exhibited complete mesogenic miscibility and wide low-temperature mesogenic stability for eventual utilization in commercial liquid crystal devices.

AI-integrated network for RNA complex structure and dynamic prediction.

RNA complexes are essential components in many cellular processes. The functions of these complexes are linked to their tertiary structures, which are shaped by detailed interface information, such as binding sites, interface contact, and dynamic conformational changes. Network-based approaches have been widely used to analyze RNA complex structures. With their roots in the graph theory, these methods have a long history of providing insight into the static and dynamic properties of RNA molecules. These approaches have been effective in identifying functional binding sites and analyzing the dynamic behavior of RNA complexes. Recently, the advent of artificial intelligence (AI) has brought transformative changes to the field. These technologies have been increasingly applied to studying RNA complex structures, providing new avenues for understanding the complex interactions within RNA complexes. By integrating AI with traditional network analysis methods, researchers can build more accurate models of RNA complex structures, predict their dynamic behaviors, and even design RNA-based inhibitors. In this review, we introduce the integration of network-based methodologies with AI techniques to enhance the understanding of RNA complex structures. We examine how these advanced computational tools can be used to model and analyze the detailed interface information and dynamic behaviors of RNA molecules. Additionally, we explore the potential future directions of how AI-integrated networks can aid in the modeling and analyzing RNA complex structures.

Reliability Analysis of Complex Structures Under Multi-Failure Mode Utilizing an Adaptive AdaBoost Algorithm

A reliability analysis can become intricate when addressing issues related to nonlinear implicit models of complex structures. To improve the accuracy and efficiency of such reliability analyses, this paper presents a surrogate model based on an adaptive AdaBoost algorithm. This model employs an adaptive method to determine the optimal training sample set, ensuring it is as evenly distributed as possible on both sides of the failure curve and fully contains the information it represents. Subsequently, with the integration and iterative characteristics of the AdaBoost algorithm, a simple binary classifier is iteratively applied to build a high-precision alternative model for complex structural fault diagnosis to cope with multiple failure modes. Then, the Monte Carlo simulation technique is employed to meticulously assess the failure probability. The accuracy and stability of the proposed method’s iterative convergence process are validated through three numerical examples. The findings of the study illuminate that the proposed method is not only remarkably precise but also exceptionally efficient, capable of addressing the challenges related to the reliability evaluation of complex structures under multi-failure mode. The method proposed in this paper enhances the application of mechanical structures and facilitates the utilization of complex mechanical designs.

Research on Digital Construction Technology for Special-Shaped Shell Pipe Structures

The aesthetic appeal of special-shaped shell pipe structures makes them highly favored by architects and holds promising prospects for various applications. In the detailed design stage, NURBS curves should be divided into multiple continuous arcs due to the limitations of current steel structure fabrication equipment, which can only accommodate pipes with equal-curvature bends. However, the traditional manual fitting methods suffer from several issues including low efficiency, undercutting at the interface, poor smoothness of curves, and lack of control over tolerances. Furthermore, the weaker out-of-plane stiffness and utilization of bending arc pipe sections pose significant challenges in terms of spatial positioning and installation accuracy that need to be addressed. The study focuses on addressing these challenges by investigating digital construction technology for special-shaped shell pipe structures and developing a parametric algorithm that enables automatic fitting of spatial NURBS curves into multiple arcs, thereby achieving seamless curve fitting. A post-processing program was developed to enable the parametric generation of fabrication and installation information for structural members, which can be seamlessly integrated into the BIM database. Finally, structural position control technology is proposed to improve assembly efficiency and ensure consistency between the completed construction state and the design shape. The above digital construction technology has been applied in projects such as the Haihua Island International Conference Center. It can provide complete technical solutions for modeling of special-shaped shell pipe structures, including establishment of a member information database, fabrication at the workshop and installation on site, construction organization management, as well as installation accuracy control.

Elastodynamics based Modelling of Acoustic Emission for Earlier Bearing Damage Detection

It is crucial for many applications to detect bearing damage as early as possible to allow for scheduling of maintenance with lead times that minimize operational disruption. State of the practice is the detection of spalling but damage initiates prior to spalling as subsurface and surface cracks. Such damage is much harder to detect and to model. This study proposes a unique application of the nanofrictional Prandtl-Tomlinson model to predict macroscopic acoustic emission (AE) signals that occur at cracked interfaces under relative motion. The study integrates large deformation modelling of structures with elastodynamic simulations to investigate early AE signals generated under different bearing rotational speeds. Experimental studies are carried out to measure acoustic vibrations from metal-metal surface friction using fiber optic sensors and compared to those predicted by the model. Broad agreement of results highlights the validity of this framework.

Robust evaluation of big data-driven winter weather traffic models using six weigh-in-motion sites as testbeds in Alberta's highway network

ABSTRACT This study assesses the temporal transferability of winter traffic models using dummy variable regression at six Weigh-in-Motion (WIM) sites on Alberta's highway network. Models for two vehicle classes were developed using five years of traffic and weather data. To evaluate transferability, an additional year of data was collected, and two model structures—dummy variable and naive—were tested. The models’ estimation accuracy was measured using R² and five error metrics. Results indicate successful transferability of the models to a different year, with performance varying by road function and vehicle class. The study highlights that different model types might be needed for each vehicle class to ensure high temporal transferability. Additionally, the quality of the test data is crucial for obtaining reliable transfer results. This research addressed gaps in previous studies by collectively testing the temporal transferability of winter traffic models across an entire highway network.

Costs of Care in Relation to Alzheimer's Disease Severity in Sweden: A National Registry-Based Cohort Study.

The advancement of diagnostic and therapeutic interventions in early Alzheimer's disease (AD) has demanded the economic evaluation of such interventions. Resource utilization and cost estimates in early AD and, more specifically, the amyloid-positive population are still lacking. We aimed to provide cost estimates in AD in relation to disease severity and compare these with the control population. We also aimed to provide cost estimates for a subset of the AD population with both clinical diagnosis and amyloid-positive confirmation. This was a retrospective longitudinal analysis of resource utilization using data from national registries. A cohort from the national Swedish registry for cognitive/dementia disorders (SveDem) includes all clinically diagnosed AD between 2013 and 2020. The study population included 31,951 people with AD and 63,902 age- and sex-matched controls (1:2). The population was followed until death, the end of December 2020, or 2years from the last clinic visit. Direct medical and social costs were estimated from other national registries. Direct medical costs include costs for medications and inpatient and outpatient clinical visits. Direct social costs include costs for institutionalization, home care, short-term care, support for daytime activities, and housing support. Mean annual costs and 95% confidence intervals were obtained by bootstrapping, presented in 2021 Swedish Krona (SEK) (1 SEK=0.117 USD, 1 SEK=0.0985 EUR in 2021), and disaggregated by AD severity, cost component, sex, age group, and care setting. Mean annual costs for individuals with clinically diagnosed AD were SEK 99,906, SEK 290,972, SEK 479,524, and SEK 795,617 in mild cognitive impairment (MCI), mild, moderate, and severe AD. The mean annual costs for the population with both clinical diagnosis and amyloid-positive AD confirmation (N=5610) were SEK 57,625, SEK 179,153, SEK 333,095, and SEK 668,073 in MCI, mild, moderate, and severe AD, respectively. The mean annual costs were higher in institutionalized than non-institutionalized patients, females than males, and older than younger age groups. Inpatient and drug costs were similar in all AD severity stages, but outpatient costs decreased with AD severity. Costs for institutionalization, home care, support for daytime activities, and short-term care increased with AD severity, whereas the cost of housing support decreased with AD severity. This is the first study estimating annual costs in people with AD from MCI to severe AD, including those for the amyloid-positive population. The study provides cost estimates by AD severity, cost components, care settings, sex, and age groups, allowing health economic modelers to apply the costs based on different model structures and populations.

Customizing GPT for natural language dialogue interface in database access

The paper presents Anatomy3DExplorer, a customized ChatGPT designed as a natural language dialogue interface for exploring 3D models of anatomical structures. It illustrates the significant potential of large language models (LLMs) as user-friendly interfaces for database access. Furthermore, it showcases the seamless integration of LLMs and database APIs, within the GPTS framework, offering a promising and straightforward approach.

Temporal Homogenization Modeling of Viscoelastic Asphalt Concretes and Pavement Structures under Large Numbers of Load Cycles

Temporal Homogenization Modeling of Viscoelastic Asphalt Concretes and Pavement Structures under Large Numbers of Load Cycles

Reliability-based multi-objective optimization design of composite patch repair structure using artificial neural networks

Due to the parameter differences and the existence of random factors, the performance of composite repair structure shows a large dispersion. In order to obtain stronger, lighter and more reliable repair structure, multi-objective optimization design of carbon fiber reinforced polymers (CFRP) laminate repair structure is investigated by combining the reliability theory, artificial neural networks and the genetic algorithm. First, a 3D simulation model of the composite laminate patch repair structures is established using the 3D Hashin criterion and the cohesive region model. Then, the Latin Hypercube sampling (LHS) method is used to realize the random sampling, and the strength proxy model of the repair structure is established by using Back-propagation artificial neural network, further a multi-objective optimization model with tensile strength, reliability and weight as objective functions is built considering the design parameters and the random parameters. Finally, NSGAⅡalgorithm is used to solve the multi-objective optimization problem, and a set of solutions on the Pareto front surface are obtained, also the optimal design parameters of the composite repair structure meets the requirements is obtained.

Vibration fatigue of film cooling hole structure of Ni-based single crystal turbine blade: Failure behavior and life prediction

The vibration fatigue failure behavior and fatigue life of the film cooling hole (FCH) structure of Ni-based single crystal superalloy were investigated at high temperature. The vibration fatigue test of the FCH specimens were carried out based on the paired staircase method. The cracks are mostly initiated in the stress concentration area at the edge of the FCH, and may also be initiated in non-stress concentrated areas with large defects. At high temperature, the crack initiation mechanism of the FCH specimens of Ni-based single crystal superalloy is oxidative cracking nucleation in stress concentration area, and the crack propagation mechanism is the competition mechanism of dislocation slip and dislocation climbing. Based on the Fatigue Indicator Parameter (FIP) of the critical plane method, a vibration fatigue life prediction model for FCH structures considering stress concentration and high temperature oxidation damage is proposed. The life prediction model proposed in this paper is applied to the vibration fatigue life prediction of FCH specimens. The deviation between the predicted results and the experimental results is within 2.5 times at 850℃, and the deviation is within 2 times at 980℃. Besides, the life prediction method proposed is compared with other two FIP life prediction methods, and the high accuracy and effectiveness of the proposed life prediction method are verified.

Open source tool for Micro-CT aided meso-scale modeling and meshing of complex textile composite structures

Volumetric image-based modeling of textile reinforcements and composites is favored over ideal geometric modeling because of its ability to represent complex structures in sufficient detail. Although several approaches were devised, there is still a scarcity of dedicated tools capable of effectively transferring pertinent information from images to high-fidelity models. This work presents the open source project, PolyTex, a Python-based object-oriented application that establishes a streamlined and reproducible workflow for such tasks. Dual kriging serves as the foundational theory for the parametric approach developed to represent, simplify, and approximate the morphology and topology of fiber tows. The code takes two types of input, either an explicit representation of tow geometry using point clouds or implicit representations, such as image masks representing fiber tows separately with grayscale values. Tailored APIs allow for smooth integration between PolyTex’s modeling capabilities and the simulation environments offered by OpenFOAM and Abaqus. Case studies on virtual testing of textile permeability were presented to demonstrate this capability. The modular and object-oriented design makes PolyTex a highly reusable and extensible tool that allows users to create a customized pipeline.