Load-bearing Capability Research Articles

A numerical study on bending behavior of sandwich beam with novel auxetic honeycomb core

To further improve the load-bearing capability and anti-impact properties of the auxetic structure, a novel enhanced auxetic honeycomb core (NEH) was introduced, and the bending resistance and crashworthiness of this sandwich beam (NEH-SWB) were analyzed through numerical simulation. First, bending tests on sandwich beams under different loading situations revealed that NEH-SWB was more susceptible to impact damage in three-point bending situations than those under four-point bending, and the influence of structural parameters were more pronounced. In addition, the impact of load position and structural parameters on the bending response and deformation pattern of NEH-SWB were investigated. NEH-SWB exhibited superior bending resistance when the punch was located in the S position. Furthermore, increasing the panel thickness and altering the panel thickness configuration both contributed to improving the flexural resistance of NEH-SWBs. At a certain mass, the configuration with T f / T b > 1 exhibited better bending resistance. Meanwhile, increasing both the cell angle (θ) and wall thickness-to-length ratio (t/l) enhanced load-carrying capability and energy absorption of NEH-SWB. Subsequently, further analysis showed that NEH had better bending performance compared to cell structures with reentrant (RH), star-shaped (SSH), and reentrant-star (RSH) honeycombs. Finally, the complex proportion assessment method was employed to examine the significance of structural parameters regarding the flexural behavior of the NEH-SWB. It was concluded that increasing the t/l value could be the best solution to enhance the bending performance of the NEH-SWB.

Crack Detection and Analysis of Concrete Structures Based on Neural Network and Clustering.

Concrete is extensively used in the construction of infrastructure such as houses and bridges. However, the appearance of cracks in concrete structures over time can diminish their sealing and load-bearing capability, potentially leading to structural failures and disasters. The timely detection of cracks allows for repairs without the need to replace the entire structure, resulting in cost savings. Currently, manual inspection remains the predominant method for identifying concrete cracks. However, in today's increasingly complex construction environments, subjective errors may arise due to human vision and perception. The purpose of this work is to investigate and design an autonomous convolutional neural network-based concrete detection system that can identify cracks automatically and use that information to calculate the crack proportion. The experiment's findings show that the trained model can classify concrete cracks with an accuracy of 99.9%. Moreover, the clustering technique applied to crack images enables the clear identification of the percentage of cracks, which facilitates the development of concrete damage level detection over time.

PENGUJIAN KINERJA WEB SERVER ELASTIC CLOUD COMPUTE (EC2) FREE TIER PADA AMAZON WEB SERVICE (AWS) MENGGUNAKAN JMETER

The research aims to evaluate the capacity of Amazon Web Service's (AWS) Elastic Cloud Compute (EC2) Free Tier Web Server using the JMeter testing tool. The objective is to ascertain the extent of the server's load-bearing capabilities. Tests were conducted, loading 500 users within a 10-second range and 1000 users within a 1-second range. Despite being a free service, test results showed the server's capability to handle high loads without errors (0% error rate). These findings illustrate the free service's ability to maintain stable performance under significant user loads. The utilization of EC2 Free Tier on AWS demonstrates its capacity to handle heavy tasks without compromising service quality. From about 5 steps in each of two tests, the value for sample time / response time in the initial test of the Amazon Web Service Web server is 59 / ms (millisecond) and the average latency value in the second test, namely 7.213 /ms (millisecond). Both of these gained CPU Utilization from minum, average and maximum with final rate are zero Error. This outcome concludes that the free server possesses robust and reliable capabilities, instilling confidence in an effective and efficient server solution even under high usage levels without additional costs.

Conducting biointerface of spider-net-like chitosan-adorned polyurethane/SPIONs@SrO2–fMWCNTs for bone tissue engineering and antibacterial efficacy

In pursuit of enhancing bone cell proliferation, this study delves into the fabrication of porous scaffolds through the integration of nanomaterials. Specifically, we present the development of highly conductive chitosan (CS) nanonets on fibro-porous polyurethane (PU) bio-membranes. These nanofibers comprise functionalized multiwall carbon nanotubes (fMWCNTs), well-dispersed superparamagnetic iron oxide (SPIONs), and strontium oxide (SrO2) nanoparticles. The resulting porous scaffold exhibits remarkable interfacial biocompatibility, antibacterial properties, and load-bearing capability. Through meticulous in vitro investigations, the CS-PU/SPIONs/SrO2-fMWCNTs nanofibrous scaffolds have demonstrated a propensity to promote bone cell regeneration. Notably, the integration of these nanomaterials has been found to upregulate crucial bone-related markers, including ALP, ARS, COL-I, RUNX2, and SPP–I. The evaluation of these markers, conducted through quantitative real-time polymerase chain reaction (qRT-PCR) and immunocytochemistry, substantiates the improved cell survival and enhanced osteogenic differentiation facilitated by the integrated nanomaterials. This comprehensive analysis underscores the efficacy of CS-PU/SPIONs/SrO2-fMWCNTs bioscaffolds in promoting MC3T3-E1 cell regeneration within, thereby holding promise for advancements in bone tissue engineering and regenerative medicine.

Load-bearing tests and simulation analysis of compacted loose snow

To explore the load-bearing capacity of compacted loose snow, a loading experimental equipment was developed. Load-bearing tests and finite element simulation research on compacted loose snow were conducted. The research shows that the compacted loose snow undergoes punching shear damage under vertically uniform loads. The P-s curve demonstrates three phases, corresponding to slow development, rapid development, and failure stages respectively. Based on this, a relationship between the critical edge load Pcr and ultimate load Pu of the compacted snow and the unconfined compressive strength fu were proposed. Under vertically uniform loads, the snow's deformation depth increases with the density, and the bearing plate's width has a minimal effect on deformation depth. The comparison between the simulation and theoretical conclusion reveals that Boussinesq's stress solution is applicable for analyzing the additional stress in low-density snow. The results are helpful for the scientific understanding of the load-bearing capabilities of compacted snow and snow engineering.

Computer vision-based real-time deflection monitoring of complex and sizeable steel structures

Deflection, as an intuitive index, plays a pivotal role in assessing the load-bearing capability and structural integrity of complex and sizable steel structures. Notwithstanding, the conventional contact measurement is limited to static deflection and necessitates work stands and manual readings. Therefore, it hinders the attainment of dynamic deflection and fails to cater to the engineering demands of real-time and prolonged monitoring. By leveraging the advancements in computer vision technology, we propose an innovative system for real-time deflection monitoring of complex and sizable steel structures, particularly suitable for monotonic deformation induced by prolonged loading. Specifically, the off-axis-based displacement measurement method was adopted to surmount the constraint of requiring the optical axis of the camera to be perpendicular to the target. Additionally, the inverse compositional Gauss-Newton (IC-GN) algorithm and parallel computation based on seed point diffusion were exploited to boost the matching and computing efficiency for attaining real-time monitoring of multi-points. To validate the efficacy of the proposed system, we conducted static load monitoring tests on a Bailey beam, with a height of 29.5 m and length of 16.5 m per span, as part of the Alibaba Jiangsu headquarters project in Nanjing. The collected test data were compared with the results from a laser displacement sensor and the ABAQUS model. The outcomes substantiate that the system is capable of non-contact, expeditious, and straightforward installation, besides achieving high accuracy and real-time deflection monitoring. This system serves as a sophisticated solution for health monitoring in constructing complex and sizeable steel structures and further contributes to realizing construction intelligence.

Optimized systems of multi-layer perceptron predictive model for estimating pile-bearing capacity

The primary goal of this research is to leverage the advancements in machine learning techniques to forecast the bearing capacity of piles effectively. Accurately predicting load-bearing capability is an indispensable aspect in the field of substructure engineering. It is worth noting that determining load-bearing capability via in-place burden tests is a resource-intensive and labor-intensive process. This study presents a pragmatic soft computing methodology to tackle the aforementioned challenge, employing a multi-layer perceptron (MLP) for the estimation of load-bearing capacity. The dataset employed in this research encompasses a multitude of field-based pile load tests, with a meticulous selection of the most impactful factors influencing pile-bearing capacity as input variables. For a comprehensive comparative analysis, genetic algorithm-based optimizers (Crystal Structure Algorithm (CSA) and Fox Optimization (FOX)) were incorporated with MLP, leading to the development of hybrid models referred to as MLFO and MLSC, both structured with three layers. The performance of these models was rigorously evaluated using five key performance indices. The findings indicated a consistent superiority of MLFO over MLSC across all three layers. Remarkably, MLFO exhibited exceptional performance in the second layer (MLFO (2)), boasting an impressive R2 value of 0.992, an RMSE of 33.470, and a minimal SI value of 0.031. On the other hand, MLCS (1) registered the lowest accuracy in predicting the process with the least R2 value related to the validation phase of the model with 0.953. Taken together, these results affirm that the optimized MLP model stands as a valuable and practical tool for accurately estimating pile-bearing capacity in civil engineering applications.

Collagen Networks under Indentation and Compression Behave Like Cellular Solids.

Simple synthetic and natural hydrogels can be formulated to have elastic moduli that match biological tissues, leading to their widespread application as model systems for tissue engineering, medical device development, and drug delivery vehicles. However, two different hydrogels having the same elastic modulus but differing in microstructure or nanostructure can exhibit drastically different mechanical responses, including their poroelasticity, lubricity, and load bearing capabilities. Here, we investigate the mechanical response of collagen-1 networks to local and bulk compressive loads. We compare these results to the behavior of polyacrylamide, a fundamentally different class of hydrogel network consisting of flexible polymer chains. We find that the high bending rigidity of collagen fibers, which suppresses entropic bending fluctuations and osmotic pressure, facilitates the bulk compression of collagen networks under infinitesimal applied stress. These results are fundamentally different from the behavior of flexible polymer networks in which the entropic thermal fluctuations of the polymer chains result in an osmotic pressure that must first be overcome before bulk compression can occur. Furthermore, we observe minimal transverse strain during the axial loading of collagen networks, a behavior reminiscent of open-celled cellular solids. Inspired by these results, we applied mechanical models of cellular solids to predict the elastic moduli of the collagen networks and found agreement with the moduli values measured through contact indentation. Collectively, these results suggest that unlike flexible polymer networks that are often considered incompressible, collagen hydrogels behave like rigid porous solids that volumetrically compress and expel water rather than spreading laterally under applied normal loads.

Experimental and numerical study on the dynamic behavior of prestressed concrete panels suffering low speed impact

Bidirectional bonded prestressed concrete (PS) structures have widely been used in energy infrastructure. Nonetheless, there has been a lack of research on the effect of the reinforcement ratio of longitudinal bars on the dynamic behavior of PS structures. Therefore, the low speed drop hammer impact tests of 4 reinforced concrete (RC) and 9 PS panels have been completed in this paper, firstly. The dynamic behavior of PS panels has been investigated through work, and the working mechanism of the reinforcement ratio on the dynamic behavior of the specimens is analyzed. The results show that the panels mainly suffer plastic deformation and damage; increasing the reinforcement ratio could improve the membrane force effect and enhance the confinement effect on concrete to improve the load-bearing capability of the specimen. However, it will expand the plastic damage area of the specimen. Furthermore, the numerical models have been verified based on the test results to deeply analyze the influence of reinforcement ratio on the energy dissipation mechanism of the specimen. It shows that the increase of the reinforcement ratio of reinforcement will increase the energy dissipation proportion of reinforcement, but it will also increase the plastic energy consumption ratio of the concrete and increase the prestress loss after the impact; while, the rise of the reinforcement ratio of prestressing bars is beneficial to use the reinforcement, prestressing bars and tensile bars fully. Therefore, the designer could increase the reinforcement ratio of prestressing bars to enhance the impact resistance of the structures.

Integrated behavioural analysis of FRP-confined circular columns using FEM and machine learning

This study investigates the structural behaviour of double-skin columns, introducing novel double-skin double filled tubular (DSDFT) columns, which utilise double steel tubes and concrete to enhance the load-carrying capacity and ductility beyond conventional double-skin hollow tubular (DSHT) columns, employing a combination of finite element model (FEM) and machine learning (ML) techniques. A total of 48 columns (DSHT+DSDFT) were created to examine the impact of various parameters, such as double steel tube configurations, thickness of fibre-reinforced polymer (FRP) layer, type of FRP material, and steel tube diameter, on the load-carrying capacity and ductility of the columns. The results were validated against the experimental findings to ensure their accuracy. Key findings highlight the advantages of the DSDFT configuration. Compared to the DSHT columns, the DSDFT columns exhibited remarkable 19.54 % to 101.21 % increases in the load-carrying capacity, demonstrating improved ductility and load-bearing capabilities. Thicker FRP layers enhanced the load-carrying capacity up to 15 %, however at the expense of the reduced axial strain. It was also observed that glass FRP wrapping displayed 25 % superior ultimate axial strain than aramid FRP wrapping. Four different ML models were assessed to predict the axial load-carrying capacity of the columns, with long short-term memory (LSTM) and bidirectional LSTM models emerging as superior choices indicating exceptional predictive capabilities. This interdisciplinary approach offers valuable insights into designing and optimising confined column systems. It sheds light on both double-tube and single-tube configurations, propelling advancements in structural engineering practices for new constructions and retrofitting. Further, it lays out a blueprint for maximising the performance of the confined columns under the axial compression.

Prediction of the axial compression capacity of stub CFST columns using machine learning techniques.

Concrete-filled steel tubular (CFST) columns have extensive applications in structural engineering due to their exceptional load-bearing capability and ductility. However, existing design code standards often yield different design capacities for the same column properties, introducing uncertainty for engineering designers. Moreover, conventional regression analysis fails to accurately predict the intricate relationship between column properties and compressive strength. To address these issues, this study proposes the use of two machine learning (ML) models-Gaussian process regression (GPR) and symbolic regression (SR). These models accept a variety of input variables, encompassing geometric and material properties of stub CFST columns, to estimate their strength. An experimental database of 1316 specimens was compiled from various research papers, including circular, rectangular, and double-skin stub CFST columns. In addition, a dimensionless output variable, referred to as the strength index, is introduced to enhance model performance. To validate the efficiency of the introduced models, predictions from these models are compared with those from two established standard codes and various ML algorithms, including support vector regression optimized with particle swarm optimization (PSVR), artificial neural networks, XGBoost (XGB), CatBoost (CATB), Random Forest, and LightGBM models. Through performance metrics, the CATB, GPR, PSVR and XGB models emerge as the most accurate and reliable models from the evaluation results. In addition, simple and practical design equations for the different types of CFST columns have been proposed based on the SR model. The developed ML models and proposed equations can predict the compressive strength of stub CFST columns with reliable and accurate results, making them valuable tools for structural engineering. Furthermore, the Shapley additive interpretation (SHAP) technique is employed for feature analysis. The results of the feature analysis reveal that section slenderness ratio and concrete strength parameters negatively impact the compressive strength index.

Multi-Objective Parametric Optimization of Micro-Electro Discharge Machining of Hastelloy C276 Super Alloy Using Response Surface Methodology and Particle Swarm Optimization

The need for the application of superalloys in aerospace industries in recent years has increased owing to its benefits such as extensive load-bearing capability under high temperatures. Hastelloy is one such superalloy that is extensively utilized in the aerospace sector because of its good corrosion and heat resistance among the other nickel-based superalloys. In this work, the investigation is conducted to understand the effects of input process parameters such as voltage, pulse off time (Toff), and pulse on time (Ton) on the response variables, namely Material removal rate (MRR), Tool wear rate (TWR), Overcut (OC), and Taper Ratio (TR) during micro-EDM of Hastelloy C276. For micro drilling the Hastelloy C276 material, a copper electrode with a diameter of 0.5 mm is utilized. To investigate the connections between the input and output characteristics, a technique known as the Response Surface Methodology (RSM), which is a collection of mathematical and statistical methodologies, is applied. The experimental runs are carried out with the help of the RSM-based Box-Behnken design (BBD). The experimental outcomes were computed, and ANOVA was used to identify the most influential variables. In addition, particle swarm optimization (PSO) was utilized to optimize the results, which were compared to the Response surface methodology approach. The outcomes of the PSO-optimized results revealed a strong correlation between expected and experimental outcomes over the RSM approach.

The Effect of Cross Sectional Properties of Concrete Column on the Repair Material: A Review

The utilization of Ultra High Performance Fibre Reinforced Concrete (UHPFRC) as a material for repairing concrete structures holds immense promise and potential. UHPFRC usage in concrete has been a significant rise in the interest surrounding the enhancement and renovation of concrete structures, particularly focusing on columns. UHPFRC has showcased remarkable bonding potency with concrete, along with an impressive capacity to renew the load-bearing capability of compromised reinforced concrete columns. However, the limited application of UHPRFC as a repair material for concrete columns is primarily attributable to a lack of comprehension regarding the influence of shape and size effects, particularly as they pertain to square and rectangular columns. In this paper, the factors behind the impact of cross-sectional properties on the effectiveness of UHPFRC as a repair material is presented. Therefore, it can be adopted by researchers to explore the UHPFRC potential in structural rehabilitation applications especially for concrete columns. It is believed that the relationship between the cross-sectional area, aspect ratio, and corner radius ratio of the column and repair material is a vital parameter affecting the performance of repaired columns.

Modelling, optimization, and testing of novel cuboidal spherical plate lattice structures

ABSTRACT This study investigates the mechanical behavior of a novel set of Cuboidal Spherical Plate Lattice (CSPL) materials. The procedure of constrained-domain topology optimization is implemented with the aim of enhancing stiffness. The micromechanical finite element homogenization approach is used to evaluate the effective elastic-plastic properties of the CSPLs and their topologically optimized counterparts, TOCSPLs (Topologically Optimized Cuboidal Spherical Plate Lattices). The TOCSPLs demonstrate higher uniaxial, shear, and bulk moduli compared to the CSPLs, with an increase of 31%, 14%, and 36% respectively. Moreover, there is an increase in the yield strengths under uniaxial, shear, and hydrostatic loading conditions, with enhancements of 103%, 55%, and 62%, respectively. The topologies are additively manufactured through Fused Deposition Modeling (FDM) out of ABS thermoplastic material. The quasi-static compression experiments demonstrate the superiority of TOCSPL 111+100 over the other topologies in terms of uniaxial modulus. The suffix 111+100 denotes the crystallographic planar orientations in which the solid plate-like disks were formed within a cubic system. The topologies proposed herein outperform certain types of Triply Periodic Minimal Surface, honeycomb, truss-, and plate-based lattice materials. The proposed topologies offer a compelling justification for their utilization in applications that require load-bearing and impact absorption capabilities.

Treatment of along defect fracture of the right lower leg using medullary nailing, cage interposition and autologous spongiosa graft (MaCIS) in aUkrainian soldier

This article presents the MaCIS method for treatment of long defect fractures of long bones using the example of aUkrainian soldier. This method includes locking medullary nailing of the affected long bone by an individually tailored cage interposed through the defect and filling of the cage with autologous spongiosa. In comparison to bone segment transport this method results in immediate full load-bearing capability of the affected extremity with substantially higher patient comfort.

Nonlinear dynamics of full-ceramic bearing-rotor system considering structural defects in cage pockets

Full ceramic bearings provide exceptional characteristics, including elevated accuracy and substantial load-bearing capability, rendering them indispensable in high-end rotating machinery. Still, the long-term use of the bearing will lead to the gradual wear and defects of the cage pocket, which may affect the unstable operation of the rotor system. This study examines the vibration characteristics of cage failure through simulation and experiments. To study the dynamic characteristics of the bearing-rotor system, a dimensionless model is established by using the lumped mass method, and a cage failure model is added to the model, which can study the influence of cage failure on the system stability. Phase trajectories, Poincaré plots, and bifurcation diagrams are employed to analyze the impact of individual parameters on the nonlinear vibration of the system. Ultimately, to gather cage vibration data from two different types of faulty, the experimental approaches are used. The findings indicate that in the event of cage failure, the system’s amplitude amplifies and the time-domain vibration signal exhibits distinct periodicity. Then, the vibration energy gradually focuses on low-frequency vibrations. Additionally, the spectrograms allow for easier identification of frequency components associated with the cage frequency, such as nfc and mfs ± nfc, the largest error is 3.32% when comparing the two models’ simulated results with the experimental data. The model accurately depicts the frequency characteristics of failures resulting from wear and defects in full ceramic bearing cages. It offers crucial information for detecting failures and monitoring the health of full ceramic bearings.

Experimental Analysis and Finite Element Modeling of S-Core Sandwich Panel Composites Drop Impact Response

Sandwich panel composites have several applications in material technology. The sandwich panel composite material is constructed of stainless steel-316 for the top and bottom plates, aluminum 1050A-0 for the core, and DP-8405 acrylic adhesive for the binding element. The impact behavior of S-core composite sandwich panels was examined using low-velocity drop impact tests and finite element models. Finite element models have been created to characterize the influence of composite element bending behavior on variations. The specific flexural modulus and strength of composite S-core sandwich structures are equivalent to those found in the literature for core structures. As a result, the minimum weight design served as a guideline for producing weight and density-efficient hybrid composite sandwich panels. The energy absorbed in the test findings rose between 15.15% and 30% as the core thickness grew and between 3.571% and 41.34% as the core arrays changed. Impact load-bearing capability increases with varied core heights and array designs.

Semi-self-similar fractal cellular structures with broadband sound absorption

Honeycomb core materials, recognized for their excellent sound absorption and load-bearing capabilities, are widely employed in engineering applications. The back cavity shape of honeycomb structure absorbers plays a crucial role in their sound absorption performance. In this study, the honeycomb structure absorbers are re-designed using fractal design principles, enabling a modification of the acoustic properties of traditional honeycomb-type absorbers and a significant enhancement in the sound absorption frequency band. The theoretical predictions of the semi-self-similar fractal honeycomb absorber structure are validated through finite element simulations and experimental studies, exhibiting remarkable consistency. The findings indicate that compared to single-layer first-order structures, the performance of dual-layer second-order fractal element Helmholtz resonators is significantly improved. Relative to conventional honeycomb panel structures, the fractal design approach markedly enhances the semi-absorption bandwidth of the absorbers, with an increase in the fractal order further broadening the sound absorption frequency band. The innovation of achieving broadband absorption through fractal resonators not only represents a new direction in the design of advanced honeycomb core materials but also holds substantial promise in the field of noise control.

Thermoresponsive Injectable Hydrogel To Mimic the Heat- and Strain-Stiffening Behavior of Biopolymers toward Muscle Cell Proliferation.

Injectable hydrogels with nonlinear mechanical attributes to emulate natural biopolymers hold paramount significance in tissue engineering, offering the potential to create scaffolds that seamlessly mimic the biomechanical intricacies of living tissues. Herein, we unveil a synthetic design strategy employing Schiff base chemistry to furnish a peptide-polymer hierarchical contractile injectable hydrogel network. This innovative design demonstrates cross-linking of supramolecular peptide nanostructures such as nanofibers, 1NF, and twisted bundles, 1TB, with a thermosensitive aldehyde-functionalized polymer, PCHO. These networks exhibit interesting nonlinear mechanical stiffening responses to temperature and external stress. Furthermore, the hydrogels transform into a gel state at physiological temperature to exhibit injectable behavior and demonstrate compression load-bearing capabilities. Finally, the hydrogel network exhibits excellent biocompatibility and cell proliferation toward fibroblast, L929, and myoblast, C2C12, to validate their use as potential extracellular matrix mimetic injectable scaffolds.

Ultrastrong and High Thermal Insulating Porous High-Entropy Ceramics up to 2000°C.

High mechanical load-carrying capability and thermal insulating performance are crucial to thermal-insulation materials under extreme conditions. However, these features are often difficult to achieve simultaneously in conventional porous ceramics. Here, for the first time, it is reported a multiscale structure design and fast fabrication of 9-cation porous high-entropy diboride ceramics via an ultrafast high-temperature synthesis technique that can lead to exceptional mechanical load-bearing capability and high thermal insulation performance. With the construction of multiscale structures involving ultrafine pores at the microscale, high-quality interfaces between building blocks at the nanoscale, and severe lattice distortion at the atomic scale, the materials with an ≈50% porosity exhibit an ultrahigh compressive strength of up to ≈337MPa at room temperature and a thermal conductivity as low as ≈0.76W m-1 K-1. More importantly, they demonstrate exceptional thermal stability, with merely ≈2.4% volume shrinkage after 2000°C annealing. They also show an ultrahigh compressive strength of ≈690MPa up to 2000°C, displaying a ductile compressive behavior. The excellent mechanical and thermal insulating properties offer an attractive material for reliable thermal insulation under extreme conditions.