Test Frame Research Articles

A coupled non-orthogonal hypoelastic constitutive model for woven fabrics

Wrinkling, a common manufacturing-induced defect, can be delayed in dry woven fabrics by applying tension during processing. Such a known solution in industrial practice, is still not sufficiently implemented in the modeling and simulation of woven fabrics. This study proposes a new constitutive model considering fabric’s inherent coupling, non-orthogonality, and non-linearity. First, the intrinsic coupling in question is distinguished from the typical coupling presented by the Hook’s law. Then, the most influential forms of coupling are chosen and introduced to the model. Moreover, via experimental evidence, we reveal that the concept of inherent coupling raises a new issue: the load history dependency of the fabric. Accordingly, the base model was evolved to a hypoelastic form to capture this dependency. The stiffness functions of the introduced model are determined, considering the mechanical behavior of a typical polypropylene (PP)/glass plain weave and the underlying meso-scale sources of the coupling. An inverse method was employed to identify the unknown model parameters. While the basic loading modes such as the uniaxial tension and picture frame tests are utilized for model calibration, the more complex loading conditions such as the simultaneous biaxial tensile-shear test, which would be more comparable to actual forming processes, are selected as the independent datasets to validate accuracy of the model. Comparisons with the experimental results demonstrated that the coupled model predicts the behavior of the fabric more accurately than the uncoupled model.

Forecasting High Cycle and Very High Cycle Fatigue Through Enhanced Strain-Energy Based Fatigue Life Prediction

Abstract This study applies a novel strain-energy based fatigue life prediction method to low cycle fatigue (LCF) data additive manufactured (AM) Inconel 718 round dog-bones, specifically focusing on cycles to failure less than 105. The objective is to forecast high cycle fatigue (HCF, 106–107) and very high cycle fatigue (VHCF >107), aiming to approximate stress versus cycles to failure (SN) behavior and the inherent variability in the fatigue life of the material system. This tool is motivated by the need to characterize an expanding and diverse array of materials produced by additive manufacturing methods, process parameter modifications, and optimizations, which can significantly impact fatigue life and overall structural reliability. The fatigue life prediction method achieves this by generating a fatigue life distribution as a function of stress amplitude. Bayesian statistical inference and stochastic sampling techniques are employed iteratively for each test data point, incorporating stress, cycles to failure, and total strain-energy dissipated leading to fatigue failure. Experimental LCF fatigue data are acquired using a standard axial servohydraulic load frame equipped with an axial extensometer for strain and strain-energy data collection. HCF and VHCF data is generated utilizing an ultrasonic fatigue test frame capable of cycling at 20 kHz. The fatigue data obtained from both servohydraulic and ultrasonic fatigue testing are presented and compared. The energy-based fatigue life prediction framework, utilizing only LCF data, is deployed to forecast HCF and VHCF fatigue behavior for AM Inconel 718 fatigue. The HCF and VHCF data generated are then used to validate the forecasts generated from the novel energy-based fatigue life prediction method. Comparisons are made against Random Fatigue Limit curve estimations to assess the effectiveness and accuracy of the proposed methodology.

The effect of virtual visual scene inclination transitions on gait modulation in healthy older versus young adults-A virtual reality study.

Bipedal locomotion requires body adaptation to maintain stability after encountering a transition to incline walking. A major part of this adaptation is reflected by adjusting walking speed. When transitioning to uphill walking, people exert more energy to counteract gravitational forces pulling them backward, while when transitioning to downhill walking people break to avoid uncontrolled acceleration. These behaviors are affected by body-based (proprioception and vestibular) cues as well as by visual cues. Since older age adversely affects walking, it is unclear whether older adults rely on vision during locomotion in a similar manner to younger individuals. In this study, we tested whether the influence of visual cues on these walking speed modulations in healthy older adults (60-75 years old, N = 15) were comparable to those found in healthy young adults (20-40 years old, N = 12). Using a fully immersive virtual-reality system embedded with a self-paced treadmill and projected visual scene, we manipulated the inclinations of both the treadmill and the visual scene in an independent manner, and measured participants walking speed. In addition, we also measured individual visual field dependency using the rod and frame test. The older adults presented the expected braking (decelerating) and exertion (accelerating) effects, in response to downhill and uphill visual illusions, respectively, in a similar manner to the young group. Furthermore, we found a significant correlation between the magnitude of walking speed modulation and visual field dependency in each of the groups with significantly higher visual field dependency in older adults. These results suggest that with aging individuals maintain their reliance on the visual system to modulate their gait in accordance with surface inclination in a manner similar to that of younger adults.

Softening index‐based equivalent linear modeling approach for bolt‐connected precast concrete frame

AbstractPrecast concrete structures with bolt‐connected joints have been widely constructed in the world, and their prospective application market is still considerable. Considering the clear load transferring path and damage mechanism, simplified analysis method is feasible for the bolt‐connected precast concrete frame. This paper proposes an equivalent linear modeling approach (ELMA) for this type of structure, which is convenient and high efficiency to help the preliminary design of structures and seismic assessment of existing buildings. In this method, a series of linear models are established to simulate the real structure in different seismic intensity levels. The softening indices summarized from the variation of modal frequencies are defined to represent the stiffness degradation of structures. In order to obtain specific softening indices, a shaking table test for a bolt‐connected precast concrete frame was implemented. Based on the test results, the recommended softening indices, applicable to bolt‐connected frames with similar design performance objectives, are 0.325, 0.713, and 0.816 for the minor, moderate, and major earthquake levels, respectively. Following the ELMA, the numerical model of the test frame was constructed and its equivalent seismic behaviors were analyzed. By comparison with the test results, the convenience and effectiveness of this method is validated, and the precision of simulation meets the requirement of engineering application. The ELMA is also applicable for precast frames with different joints or design requirements, if the softening indices are appropriately defined by shaking table test of the structure or cyclic loading test of its single‐connection model.

In rod we trust-The evaluation of a virtual rod and frame test as a cybersickness screening instrument.

Although Virtual Reality (VR) holds massive potential, its applicability still faces challenges because some individuals experience cybersickness. This phenomenon includes general discomfort, disorientation, and/or nausea, and it threatens not only a pleasant user experience but also the user's safety. Thus, predicting a user's susceptibility without relying on screening questionnaires that focus on past experiences, would enable more pleasant, safer VR experiences, especially for first-time users. Hence, the current study uses the participant's controller input in a virtual Rod and Frame Test (RFT) as an effortlessly trackable performance measure. The RFT is an established method for measuring an individual's sense of verticality in visually displaced fields. It has been used in the context of simulator sickness and cybersickness. In line with the literature and the subjective vertical mismatch theory, a lower visual dependency is expected to be correlated positively with cybersickness. To evaluate the potential of the RFT as a screening method for cybersickness, a cybersickness-inducing virtual environment (the City) was deployed. In total, data from 76 participants were eligible for the statistical analysis. The study finds a positive correlation between lower visual dependency and cybersickness, but only for the group that took the RFT after experiencing the City and only for the post-RFT cybersickness ratings. As cybersickness symptoms were VR environment-specific, the predictive validity of the RFT considering the VR-specific attributes is limited. Further, other studies attributed different working mechanisms to explain the connection between visual dependence and cybersickness with conflicting evidence. Although the RFT is not applicable as a cybersickness screening method, the effect sizes suggest that the RFT could serve as an additional objective assessment of the individuals' current state during VR exposure. Future research should systematically explore interconnections between the various factors that contribute to cybersickness, pursuing the idea of open science for context sensitivity.

A Study of Fit and Friction Force as a Function of the Printing Process for FFF 3D-Printed Piston–Cylinder Assembly

Current 3D printing processes for polymer material extrusion are limited in their accuracy in terms of dimension, form, and position. For precise results, post-processing is recommended, like with assembled parts such as pistons and cylinders wherein axial mobility is desired with low friction force and limited radial play. When no post-processing step of the printed parts is accomplished, the fit and the friction force behavior are strongly dependent on the process performances. This paper presents a study on parameters of significance and their effects on sliding and running fits as well as their friction forces for fused filament fabrication of such assemblies. A series of experiments were performed with multiple factors and levels, including the position or layout of printed objects, their layer thickness, the material used, the use of aligned or random seam, and the printer type. Piston–cylinder pairs were printed, measured, assembled, and tested using a tensile test frame. A mathematical model was developed to describe the oscillating friction force behavior observed. This study presents the feasibility and limitations of producing piston–cylinder assemblies with reduced play and friction when using appropriate conditions. It also provides recommendations to obtain and better control a desired running and sliding fit.

Development of Double Frame Method to improve the seismic performance of low-rise reinforced concrete buildings

ABSTRACT The current paper proposes a seismic reinforcement method that uses a double frame and is called the Double Frame Method (DFM). Performance tests are conducted to assess the material suitability of a post-installed anchor that directly connects the existing frame to the double frame, while a shear performance test is conducted to examine the vertical connector. The performance of column members with and without DFM reinforcement was compared and evaluated. Ultimately, we have fabricated a two-story, one-span reinforced concrete existing frame test specimen and a DFM reinforced frame test specimen that we can use to examine the effect of improving seismic performance through repeated loading tests. The result of the anchor’s pull-out test show that the average tensile strength is 27.9 kN, exceeding the average design strength of 21.7 kN suggested by the manufacturer. The shear strength test results of the anchor show an average value of 28.7 kN. Meanwhile, the vertical connector test show that the internal force per anchor is 46.83 kN, which is approximately 163% more than the single anchor design shear strength of 28.7 kN. As a result of the DFM-reinforced column test, in the actuator direction, the yield strength is improved by about 120%, while the maximum strength is improved by about 150–180%. As a result of the two-story frame experiment, the double frame reinforced specimen shows a maximum strength of about 4.0 to 4.5 times that of the non-seismic specimen.

Full-Scale Testing of Two-Tiered Steel Buckling-Restrained Braced Frames

Full-Scale Testing of Two-Tiered Steel Buckling-Restrained Braced Frames

Design and validation of an automated and remote free space measurement system for nondestructive testing of fiber composites

Assessing electromagnetic constitutive parameters is crucial to prescribe the macroscopic properties of composites and their prospective applications. Free space methods are widely used for this purpose, due to their nondestructive/noncontact nature and their applicability on composites incorporating large inclusions or over-frequency bands where waveguide measurements are impractical. However, there still exists issues associated with automation, accurate calibration, remote controlling, and multifunctional characterization. Here, we designed and implemented a microwave-integrated laboratory including a test bench for permittivity/permeability and impedance measurements of individual inclusions and a free space setup for transmission/reflection measurements of fiber-based composites. Easy switching between the bench and antenna measurements was enabled by a homemade RF multiplexer. A three-stage calibration was applied: 2-port error correction (12-term model) of the vector network analyzer and the cables connecting it to the multiplexer, de-embedding of the cables connecting the multiplexer to the switches within the antenna pillars, and thru, reflect, and line (TRL) error correction for the antennas and free space. Exploiting robotics for precise antenna movement and TRL calibration enabled adjustment of the antenna distance to the test frame to a maximum of 2.5 m with a 100 μm accuracy. A multifunctional frame for external stimuli application was also designed. Apart from automation, remote control was realized through user-friendly graphical interfaces and remote access software allowing to swiftly respond to challenges faced during the global pandemic. The free space setup effectiveness was then validated by measuring the transmission/reflection of microwire-based composites from 0.5 to 20 GHz under various magnetic fields.

ISRM Suggested Method for Determining the Properties of Rock Bolt Assemblies by Mass Freefall Impact Testing in the Laboratory

AbstractThis Suggested Method proposes impact tests to determine selected properties of full-scale rock bolt assemblies subject to an axis-aligned impact force. These properties include the stiffness of the bolt assembly, the rise time, the tensile strength, the ultimate displacement, and the energy absorption of the rock bolt assembly. These properties may be used to design or select ground support for burst-prone ground conditions. This Suggested Method is limited to the mass freefall impact test method. It is performed using a test frame in the laboratory. The Method specifies the requirements for the testing apparatus and measurement sensors, the test procedures, the data processing methods and the reporting items.

MEMS-Based Vibration Acquisition for Modal Parameter Identification of Substation Frame

As a critical component of substations, the substation frames are characterized by significant height and span, which presents substantial challenges and risks in conducting dynamic response tests using traditional sensors. To simplify these difficulties, this paper introduces an experimental method utilizing MEMS sensor-based vibration acquisition. In this approach, smartphones equipped with MEMS sensors are deployed on the target structure to collect vibration data under environmental excitation. This method was applied in a dynamic field test of a novel composite substation frame. During the test, the proposed MEMS-based vibration acquisition method was conducted in parallel with traditional ultra-low-frequency vibration acquisition methods to validate the accuracy of the MEMS data. The results demonstrated that the MEMS sensors not only simplified the testing process but also provided reliable data, offering greater advantages in testing convenience compared with traditional contact methods. The modal parameters of the substation frame, including modal frequencies, damping ratios, and mode shapes, were subsequently identified using the covariance-driven stochastic subspace identification method. The experimental methodology and findings presented in this paper offer valuable insights for structural dynamic response testing and the wind-resistant design of substation frames.

Microscopic characteristics of peri- and postmortem fracture surfaces

This study investigated if microscopic surface features captured with a scanning electron microscope (SEM) effectively discriminate fracture timing. We hypothesized that microscopic fracture characteristics, including delamination, osteon pullout, and microcracks, may vary as bone elasticity decreases, elucidating perimortem and postmortem events more reliably than macroscopic analyses. Thirty-seven unembalmed, defleshed human femoral shafts from males (n=18) and females (n=2) aged 33–81 years were fractured at experimentally simulated postmortem intervals (PMIs) ranging from 1 to 60 warm weather days (250–40,600 ADH). A gravity convection oven was used to approximate tissue decomposition at 37 C and 27 C, and the resulting heat-time unit (accumulated degree hours, or ADH) was used to examine fractures in elastic/wet versus brittle/dry bone. The bones were fractured with a drop test frame using a three-point bending setup, sensors were used to calculate fracture energy, and high-speed photography documented fracture events. The following data were collected to relate fracture appearance to the biomechanical properties of bone: PMI (postmortem interval) length in ADH, temperature, humidity, collagen percentage, water loss, bone mineral density, cortical bone thickness, fracture energy, age, sex, cause of death, and microscopic fracture feature scores. SEM micrographs were collected from the primary tension zones of each fracture surface, and three microscopic fracture characteristics were scored from a region of interest in the center of the tension zone: percentage of delaminated osteons, percent osteon pullout, and number of microcracks. Multiple linear regression showed that microscopic fracture surface features are strong predictors of ADH (adjusted R-squared=0.67 for the 0 – 40,000 ADH samples; adjusted R-squared=0.92 for the 0–16,000 ADH samples). Osteon pullout is the single best predictor of ADH. Additionally, water loss is the primary driver of bone elasticity changes in low ADH samples, while collagen fibers appear to remain intact until later in the postmortem interval (approximately 40,000 ADH in this study). The results of this study indicate microscopic fracture surface analysis detects the biomechanical effects of decreased elasticity more reliably and with greater sensitivity than macroscopic analysis.

Native anterior talo-fibular ligament tensile characteristics compared to allograft, suture tape, and copolymer augmentation elements: A biomechanical study

Surgical augmentation methods have been introduced to the Modified Broström (MB) technique to support native anterior talo-fibular ligament (ATFL) healing and function. This study aimed to investigate the isolated biomechanical performance of common MB augmentation elements, including allograft, suture tape, and copolymer, compared to native ATFL.Six cadaveric feet were dissected, isolating the ATFL from all surrounding soft tissue. The fibula and talus were clamped on the testing frame so that the ligament was in line with the load cell. Six samples per augment group were fixed on a test frame with a gauge length of 20 mm to replicate ATFL length. All samples were pulled to failure at 305 mm/min. Biomechanical outcomes included stiffness, elongation, and ultimate load.Mean ± standard deviation was reported. Stiffness was highest for suture tape (246.4 ± 52.1N/mm), followed by allograft (114.2 ± 26.2 N/mm), native ATFL (78.6 ± 31.8 N/mm), and copolymer (9.4 ± 2.9 N/mm). Significant differences in stiffness were observed between all groups except when comparing ATFL stiffness to allograft (P = 0.086). Copolymer resulted in significantly larger elongation at ultimate load compared to native ATFL, suture tape, and allograft (P < 0.001). Elongation at ultimate failure was highest for copolymer (30.0 ± 8.7 mm) and significantly greater than all other groups (P < 0.001). Ultimate load was highest for suture tape (544.1 ± 59.7 N), followed by native ATFL (338.5 ± 63.7 N), allograft (308.3 ± 98.5 N) and copolymer (146.7 ± 8.9 N). Suture tape ultimate load was significantly greater than copolymer (P < 0.001).Isolated biomechanical data of augment materials can be utilized by foot and ankle surgeons when considering appropriate ligament augmentation options.Level of clinical evidence 5, controlled laboratory study

Multimodal Convolutional Neural Network-Based Algorithm for Real-Time Detection and Differentiation of Malignant and Inflammatory Biliary Strictures in Cholangioscopy: a Proof of Concept Study with Video

Deep learning algorithms gained attention for detection (computer-aided detection [CADe]) of biliary tract cancer in digital single-operator cholangioscopy (dSOC). We developed a multimodal convolutional neural network (CNN) for detection (CADe), characterization and discriminating (computer-aided diagnosis [CADx]) between malignant, inflammatory, and normal biliary tissue in raw dSOC videos. In addition, clinical metadata were included in the CNN algorithm to overcome limitations of image-only models. Based on dSOC videos and images of 111 patients (total of 15,158 still frames), a real-time CNN-based algorithm for CADe and CADx was developed and validated. We established an image-only model and metadata injection approach. In addition, frame-wise and case-based predictions on complete dSOC video sequences were validated. Model embeddings were visualized, and class activation maps highlighted relevant image regions. The concatenation-based CADx approach achieved a per-frame area under the receiver-operating characteristic curve of .871, sensitivity of .809 (95% CI, .784-.832), specificity of .773 (95% CI, .761-.785), positive predictive value of .450 (95% CI, .423-.467), and negative predictive value of .946 (95% CI, .940-.954) with respect to malignancy on 5715 test frames from complete videos of 20 patients. For case-based diagnosis using average prediction scores, 6 of 8 malignant cases and all 12 benign cases were identified correctly. Our algorithm distinguishes malignant and inflammatory bile duct lesions in dSOC videos, indicating the potential of CNN-based diagnostic support systems for both CADe and CADx. The integration of non-image data can improve CNN-based support systems, targeting current challenges in the assessment of biliary strictures.

An artificial intelligence-based nerve recognition model is useful as surgical support technology and as an educational tool in laparoscopic and robot-assisted rectal cancer surgery

BackgroundArtificial intelligence (AI) has the potential to enhance surgical practice by predicting anatomical structures within the surgical field, thereby supporting surgeons' experiences and cognitive skills. Preserving and utilising nerves as critical guiding structures is paramount in rectal cancer surgery. Hence, we developed a deep learning model based on U-Net to automatically segment nerves.MethodsThe model performance was evaluated using 60 randomly selected frames, and the Dice and Intersection over Union (IoU) scores were quantitatively assessed by comparing them with ground truth data. Additionally, a questionnaire was administered to five colorectal surgeons to gauge the extent of underdetection, overdetection, and the practical utility of the model in rectal cancer surgery. Furthermore, we conducted an educational assessment of non-colorectal surgeons, trainees, physicians, and medical students. We evaluated their ability to recognise nerves in mesorectal dissection scenes, scored them on a 12-point scale, and examined the score changes before and after exposure to the AI analysis videos.ResultsThe mean Dice and IoU scores for the 60 test frames were 0.442 (range 0.0465–0.639) and 0.292 (range 0.0238–0.469), respectively. The colorectal surgeons revealed an under-detection score of 0.80 (± 0.47), an over-detection score of 0.58 (± 0.41), and a usefulness evaluation score of 3.38 (± 0.43). The nerve recognition scores of non-colorectal surgeons, rotating residents, and medical students significantly improved by simply watching the AI nerve recognition videos for 1 min. Notably, medical students showed a more substantial increase in nerve recognition scores when exposed to AI nerve analysis videos than when exposed to traditional lectures on nerves.ConclusionsIn laparoscopic and robot-assisted rectal cancer surgeries, the AI-based nerve recognition model achieved satisfactory recognition levels for expert surgeons and demonstrated effectiveness in educating junior surgeons and medical students on nerve recognition.

Evaluation of interactional behavior of suspended ceilings and partition walls through shake table tests

Earthquakes cause seismic losses due to direct and indirect effects of damage to structural and nonstructural elements in buildings. Suspended ceiling-to-partition wall (SCPW) systems, the most earthquake-vulnerable nonstructural elements, may suffer severe damage under earthquakes even with low and moderate intensities. However, there is still limited research on investigating the seismic performance of SCPW systems in buildings located at low- or moderate-seismicity regions. This study aims to experimentally evaluate the seismic response of SCPW systems using shake-table test program and to deeply investigate the interaction between a partition wall and a ceiling. To do this, the shake table program was carried out using a full-scale specimen employing SCPW systems and its results were summarized and analyzed. The frequencies measured at the partitions were similar to those measured at the test frame whereas the fundamental frequency of the suspended ceiling was decoupled to the floor acceleration of the test frame. The full-height partitions presented relatively good seismic response because of the sufficient lateral support at the top of the partition, whereas severe damage was mainly concentrated on the ceiling elements due to pounding effects. The partial-height partitions with improper anchor detailing to adjacent suspended ceiling suffered severe damage. Such poor seismic performance is because they suffer pounding effects resulting from the interactional behavior which are magnified in partition walls with lack of sufficient lateral supporting. Both partial-height partitions and ceiling elements were seriously damaged.

Prediction of seismic performance of a masonry-infilled RC frame based on DEM and ANNs

In this paper, discrete element modelling and artificial neural networks (ANNs) were adopted to evaluate seismic performance of a masonry-infilled reinforced concrete (RC) frame. Discrete element models were developed to simulate quasi-static tests of infilled frames and were validated by using experimental data from the literature in terms of ultimate bearing capacities, initial stiffnesses and hysteresis curves. Parametric analysis was conducted based on the validated models to further investigate the influence factors of infilled RC frames and to collect a database for training ANN models. Subsequently, 440 datasets were divided into a training set (70 %), a validating set (15 %), and a testing set (15 %). Back propagation neural network (BPNN) and radial basis function neural network (RBFNN) were employed to predict the seismic behavior of a masonry-infilled RC frame. The BPNN model used the Levenberg-Marquardt algorithm exhibited the highest coefficient of determination, and it was better than the RBFNN model with more neurons. Eventually, a practical ANN model was proposed to evaluate the seismic performance of a masonry-infilled RC frame.

Pseudo-dynamic tests of steel frame with disc spring self-centering energy dissipation braces under doublet earthquake and mainshock-aftershock sequence

To provide valuable insights into the seismic performance and self-centering behavior of self-centering braced systems, a 1/2-scale 3-story 1-bay steel frame equipped with disc spring self-centering energy dissipation braces (DS-SCBs) was designed, fabricated, and subjected to pseudo-dynamic testing. This testing encompassed various earthquake hazard levels, a doublet earthquake, and a mainshock-aftershock sequence. The experimental findings revealed the stable hysteretic response of the DS-SCB, with no observed degradation in stiffness or activation forces. Additionally, the test frame exhibited outstanding seismic performance, sustaining only confined damage under very rare earthquakes. Due to the presence of the DS-SCBs, the test frame performed commendable self-centering behavior, characterized by minimal residual deformations. Peak story drifts and floor accelerations under two earthquakes of the same hazard level were regarded as equivalent. Furthermore, the subsequent aftershocks exhibited no discernible impact on the seismic performance of the test frame. Floor acceleration spikes were observed at locations where the brace stiffness underwent changes or where adjacent stories moved in opposite directions. The steel frame equipped with DS-SCBs proved to be an attractive, low-damage alternative to conventional steel systems for the development of resilient urban areas.

Evaluation of Seismic Demand on Nonstructural Elements Based on Shake-Table Testing of Full-Scale 2-Story Steel Moment Frame

ABSTRACT A shake table test using a full-scale 2-story steel moment frame was conducted to investigate the peak floor acceleration (PFA) and the peak component acceleration (PCA), which directly affect the seismic demand on nonstructural elements. The main focus of this study was to evaluate the effects of structural and component nonlinearity on the nonstructural seismic demand. The PFA reduction suggested by ASCE 7-22 to account for structural nonlinearity was shown to be larger than the experimental results for low-to-moderate ductility levels. The analysis on the resonant PCA showed the importance of structural nonlinearity on the elastic C AR (=PCA/PFA). The high resonant PCA around the fundamental period of the elastic test frame (7 times PFA) was effectively decreased as the test frame experienced a moderate level of ductility; PCA was lowered by 36% and 25% on the 2nd and roof floors, respectively. The effect of component yielding on the PCA was evaluated based on the test results of steel racks mounted on flexible and rigid access floors. The PCA reduction depending on the component ductility level was less than the relevant provisions of ASCE 7-22, and the reason was discussed from the perspective of the pinched hysteretic behavior of the tested specimens.

Experimental and finite element investigation on bolted connection in monolithic 3-dimensional cuff with pultruded box section

This article presents a concise and practical research study on the application of pultruded GFRP (Glass Fiber Reinforced Polymer) hollow sections in structural engineering. Experimental and computational investigations are conducted on the utilization of GFRP cuff components for joining tubular pultruded fiber-reinforced polymer (PFRP) beams and columns. Both adhesive bonding and bolted connections are explored for connecting PFRP box sections of beam-column interfaces. The study investigates twelve series of beam-to-column connections under monotonic loading conditions, varying cuff geometry. A finite element model is developed to represent the pultruded fiber-reinforced polymer box sections, columns, and cuff sections. The model’s accuracy in depicting frame stiffness and cuff damage patterns, considering different thicknesses and lengths of GFRP cuff connections, is validated against experimental test frame behavior. This research fills a gap in scientific inquiry regarding GFRP hollow profiles, providing valuable insights for structural engineering applications.