Search Results (49)

As an essential medium for the cultural narrative of architectural space, studying the cognitive transformation mechanisms of traditional ethnic decorative patterns is critical for their effective preservation and innovative application. This research focuses on typical decorative motifs found in She ethnic architectural heritage, systematically classifying them into five categories—animal, plant, human figure, totem, and geometric—based on symbolic themes, formal structure, and cultural function. Correspondingly, 20 sets of standardized black-and-white line drawing stimuli were developed for experimental use. Methodologically, this study utilized the EyeLink 1000 eye-tracking system to acquire real-time gaze metrics, including fixation duration and saccadic amplitude, as well as pupil dilation responses from participants engaged in a controlled pattern observation task. Immediately after observation, participants completed a semantic differential assessment using a five-point Likert scale. Data analysis employed descriptive statistics, analysis of variance (ANOVA), Kruskal–Wallis tests, and Bonferroni-adjusted post hoc comparisons (α = 0.05). Attention allocation was further examined through heatmaps and gaze trajectory visualizations to provide comprehensive insight into visual engagement. Two principal findings were identified: first, male participants showed a predominant focus on holistic structural composition and cultural symbol representation, whereas female participants exhibited a processing bias towards fine details; second, concrete symbols imbued with historical significance elicited more pronounced emotional responses, while abstract geometric patterns necessitated formal reconstruction to enhance cognitive accessibility. These findings offer empirical support for gender-inclusive architectural design strategies and inform practical approaches for safeguarding cultural heritage within contemporary architectural environments. Consequently, modern reinterpretation of traditional decorative patterns should balance cultural narrative fidelity with functional adaptation, achieving inclusive expression through contextual reconstruction and interactive design strategies. Future research directions include expanding participant demographics to encompass cross-cultural cohorts and incorporating multimodal neuroimaging techniques to elucidate the underlying cognitive and affective mechanisms, thereby advancing the sustainable transmission and innovation of ethnic cultural heritage. Full article

Because artificially cemented granular (ACG) materials employ diverse combinations of aggregates and binders—including cemented soil, low-cement-content cemented sand and gravel (LCSG), and concrete—their stress–strain responses vary widely. In LCSG, the binder dosage is typically limited to 40–80 kg/m³ and the sand–gravel skeleton is often obtained directly from on-site or nearby excavation spoil, endowing the material with a markedly lower embodied carbon footprint and strong alignment with current low-carbon, green-construction objectives. Yet, such heterogeneity makes a single material-specific constitutive model inadequate for predicting the mechanical behavior of other ACG variants, thereby constraining broader applications in dam construction and foundation reinforcement. This study systematically summarizes and analyzes the stress–strain and volumetric strain–axial strain characteristics of ACG materials under conventional triaxial conditions. Generalized hyperbolic and parabolic equations are employed to describe these two families of curves, and closed-form expressions are proposed for key mechanical indices—peak strength, elastic modulus, and shear dilation behavior. Building on generalized plasticity theory, we derive the plastic flow direction vector, loading direction vector, and plastic modulus, and develop a concise, transferable elastoplastic model suitable for the full spectrum of ACG materials. Validation against triaxial data for rock-fill materials, LCSG, and cemented coal–gangue backfill shows that the model reproduces the stress and deformation paths of each material class with high accuracy. Quantitative evaluation of the peak values indicates that the proposed constitutive model predicts peak deviatoric stress with an error of 1.36% and peak volumetric strain with an error of 3.78%. The corresponding coefficients of determination R² between the predicted and measured values are 0.997 for peak stress and 0.987 for peak volumetric strain, demonstrating the excellent engineering accuracy of the proposed model. The results provide a unified theoretical basis for deploying ACG—particularly its low-cement, locally sourced variants—in low-carbon dam construction, foundation rehabilitation, and other sustainable civil engineering projects. Full article

In this research project a large linear electromagnetic actuator (LLEA) was designed and manufactured. The electromagnetic performance was published in previous works, but in this paper we focus on the technological challenges related to the manufacturing in particular. This LLEA was based on the magnet-free switched-reluctance principle, having six effective energised stator “teeth” and four passive mover parts (4:6 ratio). Various aspects and challenges encountered during the manufacturing, transport, and assembly are discussed. Thermal expansion of steel contributed to the decision of the modular design, with each module having 1.3 m in length, with a 2 mm longitudinal dilatation gap. The initial prototype was tested with a 10.6 m length, with plans to extend the test track to 60 m, which was fully achievable due to the modular design and required 29 tons of electrical steel to be built. The stator laminations were cut by a bespoke progressive tool with stamping, and other parts by a CO₂ laser. Mounting was based on welding (back of the stator) and clamping plates (through insulated bolts). The linear longitudinal force was on the order of 8 kN, with the main air gap of 7.5–10 mm on either side of the mover. The lateral forces could exceed 40 kN and were supported by appropriate construction steel members bolted to the concrete floor. The overall mechanical tolerances after installation remained below 0.5 mm. The technology used for constructing this prototype demonstrated the cost-effective way for a semi-industrial manufacturing scale. Full article

The accurate measurement of a crack width in concrete infrastructure is essential for structural safety assessment and maintenance. However, existing image-based methods either suffer from overestimation in complex geometries or are computationally inefficient. This paper proposes a novel hybrid approach combining a fast skeleton-pruning algorithm and a crack-width measurement technique called edge-OrthoBoundary (EOB). The skeleton-pruning algorithm prunes the skeleton, viewed as the longest branch in a tree structure, using a depth-first search (DFS) approach. Additionally, an intersection removal algorithm based on dilation replaces the midpoint circle algorithm to segment the crack skeleton into computable parts. The EOB method combines the OrthoBoundary and edge shortest distance (ESD) techniques, effectively correcting the propagation direction of the skeleton points while accounting for their width. The validation of real cracks shows the skeleton-pruning algorithm’s effectiveness, eliminating the need for a specified threshold and reducing time complexity. Experimental results with both actual and synthetic cracks demonstrate that the EOB method achieves the smallest RMS, MAE, and R values, confirming its accuracy and stability compared to the orthogonal projection (OP), OrthoBoundary, and ESD methods. Full article

Access to significant amounts of data is typically required to develop structural health monitoring (SHM) systems. In this study, a novel SHM approach was evaluated, with all training data collected solely from a validated finite element analysis (FEA) of a reinforced concrete (RC) beam and the structural health based on the tension side of a rebar under flexural loading. The developed SHM system was verified by four-point bending experiments on three RC beams cast in the dimensions of 4000 mm × 200 mm × 400 mm. Distributed optical fibre sensors (DOFS) were mounted on the concrete surface and on the bottom rebar to maximise sample points and investigate the reliability of the strain data. The FEA model was validated using a single beam and subsequently used to generate labelled SHM strain data by altering the dilation angle and rebar sizes. The generated strain data were then used to train an artificial neural network (ANN) classifier using deep learning (DL). Training and validation accuracy greater than 98.75% were recorded, and the model was trained to predict the tension state up to 90% of the steel yield limit. The developed model predicts the health condition with the input of strain data acquired from the concrete surface of reinforced concrete beams under various loading regimes. The model predictions were accurate for the experimental DOFS data acquired from the tested beams. Full article

Understanding the structural behaviour of flat plate systems during fire exposure is critical for ensuring the safety of occupants and emergency personnel. Flat slabs, a widely used structural system, undergo significant thermal deformations in fire, which increase demands on supporting columns and reduce the stiffness and strength of concrete and steel. While experimental fire tests have provided valuable data to understand the behaviour of isolated components of flat slabs, numerical analysis is the only route to comprehending the structural behaviour of full-scale flat plate structures during fire exposure. ABAQUS is commonly used for modelling reinforced concrete (RC) structures under fire, with two prevailing techniques: (1) solid element modelling for concrete and truss reinforcement and (2) shell element modelling with embedded steel layers and line-column elements. However, uncertainties remain regarding the influence of modelling parameters such as dilation angle and concrete tensile stress, and the impact of surface fire exposure has not been comprehensively studied. This study presents a novel contribution by conducting a detailed numerical investigation of a full-scale flat plate structure exposed to fire using both modelling approaches. The shell-element model was validated against experimental data and used to evaluate the effect of dilation angle and tensile strength assumptions. A unique aspect of this work is the assessment of fire exposure on different slab surfaces, including bottom, top, and both, which provides insights into slab deflections and column displacements under different surface fire exposure scenarios. The structure was then modelled using solid elements to systematically compare modelling techniques. The results highlight key differences between approaches and guide for selecting the most suitable modelling strategies for fire-exposed flat plate systems. Full article

Crack detection in precast concrete components aims to achieve precise extraction of crack features within complex image backgrounds. Current computer vision-based methods typically conduct limited local searches at a single scale, constraining the model’s capacity for feature extraction and fusion in information-rich environments. To address these limitations, we propose PC3D-YOLO, an enhanced framework derived from YOLOv11, which strengthens long-range dependency modeling through multi-scale feature integration, offering a novel approach for crack detection in precast concrete structures. Our methodology involves three key innovations: (1) the Multi-Dilation Spatial-Channel Fusion with Shuffling (MSFS) module, employing dilated convolutions and channel shuffling to enable global feature fusion, replaces the C3K2 bottleneck module to enhance long-distance dependency capture; (2) the AIFI_M2SA module substitutes the conventional SPPF to mitigate its restricted receptive field and information loss, incorporating multi-scale attention for improved near-far contextual integration; (3) a redesigned neck network (MSCD-Net) preserves rich contextual information across all feature scales. Experimental results demonstrate that, on the self-developed dataset, the proposed algorithm achieves a recall of 78.8%, an AP@50 of 86.3%, and an AP@50-95 of 65.6%, outperforming the YOLOv11 algorithm. Furthermore, evaluations on the CRACKS_MANISHA and DECA datasets also confirm the proposed model’s strong generalization capability across different data domains. Full article

Ultra-high-performance concrete (UHPC) has been following economic and environmental trends for the past two decades. Limited research has been conducted on the significance of superplasticizers in UHPC products, despite the high costs they entail for projects. The current study assesses UHPC based on rheological properties and mechanical characteristics considering different factors. In this study, the effects of different levels of superplasticizer derived from sulfonated naphthalene formaldehyde (SNF: 0.7%, 0.8%, and 0.9%), silica fume (SF: 15%, 20%, and 25%), and the water-to-binder ratio (w/b: 0.18, 0.20, and 0.22) were examined. Fresh tests such as slump flow, Vicat needle, and squeezing, as well as hardened tests like compressive strength, flexural strength, and electrical resistivity, were conducted. In the analysis, an artificial neural network (ANN) model and a fuzzy logic (FL) model were employed to forecast compressive strength results at 7 and 28 days. The results indicated that a higher SF dosage reduced slump flow and set time, whereas the opposite was observed for SNF and the w/b ratio. Three distinct behaviors were identified in the squeezing flow test findings: (1) specific elastic behavior and low plasticity, (2) extensive plastic behavior and significant dilatancy, and (3) heightened responsiveness to compressive flow rate and material ratio. SNF demonstrated promise in enhancing compressive, flexural, and electrical strength. The prediction models suggested that the FL (error range 3.18–4.36%) and ANN (0.74–1.03%) models performed well in predicting compressive strength at 7 and 28 days. The encouraging findings from this study set the stage for further sustainable and cost-effective construction methods. Full article

The paper examines the impact of passive restraint on fire-induced spalling in concrete, utilizing a concrete mixture to minimize compositional variability. A variety of specimen geometries was prepared, including standard cubes and cylinders for the determination of mechanical properties and slabs of different dimensions for fire spalling tests conducted under controlled conditions. A top-opening Dragon furnace, which applies ISO 834-1 fire curves, was used to evaluate the influence of “cold rim” boundaries, where slab edges were insulated to create thermal restraint. The cold rims were categorized as 0 cm, 10 cm, and 20 cm, with each modification representing a different degree of thermal expansion restraint. Digital image correlation (DIC) was utilized to monitor the strain fields on the unheated slab surfaces. The findings demonstrated that increasing the cold rim width implies a rise in compressive stress and strain in the central zone, thereby precipitating a more pronounced spalling behaviour. The unrestrained slabs (cold rim 0 cm) exhibited minimal spalling, whereas the restrained slabs (cold rim 20 cm) demonstrated significant spalling depths and volumes. The study confirms that thermal dilation restraint intensifies the severity of spalling and provides a quantitative framework that links stress evolution, strain distribution, and spalling depth. The findings emphasize the necessity of managing thermal restraint to properly assess fire-induced concrete spalling in material screening tests. Full article

The development of self-compacting lightweight concretes is associated with solving two conflicting tasks: achieving a structure with both high flowability and homogeneity. This study aimed to identify the technological and rheological characteristics of the flow of concrete mixtures D1400…D1600 based on hollow microspheres in comparison with heavy fine-grained D2200 concrete and to establish their structural and physico-mechanical characteristics. The study of the concrete mixtures was carried out using the slump flow test and the rotational viscometry method. The physical and mechanical properties were studied using standard methods for determining average density and flexural and compressive strength. According to the results of the research conducted, differences in the flow behaviors of concrete mixtures on dense and hollow aggregates were found. Lightweight concretes on hollow microspheres exhibited better mobility than heavy concretes. It was shown that the self-compacting coefficients of the lightweight D1400...D1600 concrete mixtures were comparable with that of the heavy D2200 concrete. The rheological curves described by the Ostwald–de Waele equation showed a dilatant flow behavior of the D1400 concrete mixtures, regardless of the ratio of quartz powder to fractionated sand. For D1500 and D1600, the dilatant flow behavior changed to pseudoplastic, with a ratio of quartz powder to fractional sand of 25/75. The studied compositions of lightweight concrete can be described as homogeneous at any ratio of quartz powder to fractional sand. It was shown that concrete mixtures with a pronounced dilatant flow character had higher resistance to segregation. The value of the ratio of quartz powder to fractional sand had a statistically insignificant effect on the average density of the studied concretes. However, the flexural and compressive strengths varied significantly more in heavy concretes (up to 38%) than in lightweight concretes (up to 18%) when this factor was varied. The specific strength of lightweight and heavy concrete compositions with a ratio of quartz powder to fractional sand of 0/100 had close values in the range of 20.4...22.9 MPa, and increasing the share of quartz powder increased the difference between compositions of different densities. Full article

Four-pile caps made from concrete are essential elements for the force transfer from the superstructure to piles or pipes. Due to the difficulties in carrying out full-scale tests and all the instrumentation involved, the use of numerical models as a way to study the mechanical behavior of these elements presents itself as a good alternative. Such numerical studies usually provide useful information for the update and improvement of normative standards and codes. The concrete damaged plasticity (CDP) constitutive model, which combines damage and plasticity with smeared-crack propagation, stands out in the simulation of reinforced concrete. This model is composed of five parameters: dilatation angle (ψ), eccentricity (ϵ), ratio between biaxial and uniaxial compressive strength (σ_bo/σ_co), failure surface in the deviator plane normal to the hydrostatic axis (K_c), and viscosity (μ). For unidimensional elements, the values of the CDP parameters are well defined, but for volumetric elements, such as concrete pile caps, there is a gap in the literature regarding the definition of these values. This fact ends up limiting the use of the CDP on these structural elements due to the uncertainties involved. Therefore, the aim of this research was to calibrate two numerical models of concrete four-pile caps with different failure modes for the evaluation of the sensitivity of the CDP parameters, except for ϵ, which remained constant. As a result, the parameters σ_bo/σ_co and K_c did not significantly influence the calibration of the force × displacement curves of the simulated structures. Values of ψ and μ equal to 36° and 1 × 10⁻⁴, respectively, are recommended for “static” analysis, while for “quasi-static” analysis, ψ values ranging between 45° and 50° are suggested according to the failure mode. The results also showed to be sensitive to the constitutive relation of concrete tensile behavior in both modes of analysis. For geometric parameterization, the “static” analysis is recommended due to the lower coefficient of variation (3.29%) compared to the “quasi-static” analysis (19.18%). This conclusion is supported by the evaluation of the ultimate load of the numerical models from the geometrically parametric study compared to the results estimated by an analytical model. Full article

Several mutations in this gene for the α subunit of the cardiac sodium channel have been identified in a heterogeneous subset of cardiac rhythm syndromes, including Brugada syndrome, progressive cardiac conduction defect, sick sinus node syndrome, atrial fibrillation and dilated cardiomyopathy. The aim of our study was to associate some SCN5A polymorphic variants directly with confirmed coronary stenoses in patients with non-LQTS ventricular fibrillation/flutter treated by an implantable cardioverter defibrillator. Materials and Methods: A group of 32 unrelated individuals, aged 63 ± 12 years, was included in the study. All the patients were examined, diagnosed and treated with an implantable cardioverter defibrillator at the Department of Internal Cardiology Medicine, Faculty Hospital Brno. The control group included 87 persons of similar age without afflicted coronary circulation, which was confirmed coronagraphically. Genomic DNA was extracted from samples of peripheral blood according to the standard protocol. Two SCN5A polymorphisms—IVS9-3C/A (rs41312433) and A1673G (rs1805124, H558R)—were examined in association with coronary artery stenosis in the patients. Results: In the case–control study, no significant differences in genotype distribution/allelic frequencies were observed for IVS9-3c>a and A1673G gene polymorphisms between patients with severe arrhythmias and healthy persons. The distribution of SCN5A double genotypes was not significantly different among different types of arrhythmias according to their ejection fraction in arrhythmic patients (p = 0.396). The ventricular arrhythmias with an ejection fraction below 40% were found to be 10.67 times more frequent in patients with multiple coronary stenosis with clinically valid sensitivity, specificity and power tests. In the genotype–phenotype study, we observed a significant association of both SCN5A polymorphisms with the stenosis of coronary vessels in the patients with severe arrhythmia. The double genotype of polymorphisms IVS9-3C/A together with A1673G (CCAA) as well as their simple genotypes were associated with significant multiple stenosis of coronary arteries (MVS) with high sensitivity and specificity (p = 0.05; OR = 5 (95% CI 0.99–23.34); sensitivity 0.70; specificity 0.682; power test 0.359) Moreover, when a concrete stenotic coronary artery was associated with SCN5A genotypes, the CCAA double genotype was observed to be five times more frequent in patients with significant stenosis in the right coronary artery (RCA) compared to those without affliction of this coronary artery (p = 0.05; OR = 5 (95% CI 0.99–23.34); sensitivity 0.682; specificity 0.700; power test 0.359). The CCAA genotype was also more frequent in patients without RCA affliction with MVS (p = 0.008); in patients with ACD affliction but without MVS (p = 0.008); and in patients with both ACD affliction and MVS compared to those without ACD affliction and MVS (p = 0.005). Conclusions: Our study presents a highly sensitive and specific association of two polymorphisms in SCN5A with significant coronary artery stenoses in patients with potentially fatal ventricular arrhythmias. At the same time, these polymorphisms were not associated with arrhythmias themselves. Thus, SCN5A gene polymorphic variants may form a part of germ cell gene predisposition to ischemia. Full article

The stress–plastic dilatancy relationship was investigated for crushed concrete during drained and undrained triaxial compression tests in the light of the frictional state concept. The slope of the dilatant failure state line is greater than that of quartz sand for drained triaxial compression due to the crushing effect. The crushing effect parameters for drained and undrained conditions are very similar. Due to the very angular shape of crushed concrete grains, the crushing effect is observed at low stress levels. Some characteristic behaviors of geomaterials during shear are visible only in the stress ratio–plastic dilatancy plane and are very rarely presented in the literature. The stress ratio–plastic dilatancy relationship, which is basic in elastic–plastic modeling of geomaterials, can be described using the frictional state concept. Full article

The Concrete Damage Plasticity (CDP) model is a widely used constitutive model to represent the non-linear behavior of concrete in numerical analysis. However, a limited number of studies compared the level of accuracy of numerical models with the main code provisions from the literature. In addition, the influence of CDP material parameters on the structural behavior of corbels was scarcely studied. This study proposes to evaluate the ability of numerical models using CDP to represent the structural behavior of corbels regarding the ultimate load, reinforcement deformation and failure mechanism. In addition, we compared the predictions of the numerical models with the ones from design code expressions regarding the ultimate capacity. For this, three test results of corbels from the literature were evaluated with numerical models using the CDP, as well as with analytical models from different code provisions. A sensitivity analysis—by changing the dilation angle (ψ) and shape factor (K_c)—was performed. The comparison between tested and predicted resistances with the proposed numerical modeling choices was equal to 1.04 with a coefficient of variation of 11%. On the other hand, the analytical models evaluated overestimated the corbel capacity by more than 62%, on average. Therefore, the proposed modeling choices provide better predictions of ultimate capacity than the evaluated analytical models and can be used to assess the corbel design under more complex boundary conditions. Full article

Crack detection in structures is an important and time-consuming element of monitoring the health of structures and ensuring structural safety. The traditional visual inspection of structures can be unsafe and may produce inconsistent results. Thus, there is a need for a method to easily and accurately identify and analyze cracks. In this study, algorithms for automatically detecting the size and location of cracks in concrete images were developed. Cracks were automatically detected in a total of 10 steps. In steps 5 and 9, two user algorithms were added to increase crack detection accuracy, where 1000 crack images and 1000 non-crack images were used, respectively. In the crack image, 100% of the cracks were detected, but 95.3% of the results were very good, even if the results that were not bad in terms of quality were excluded. In addition, the accuracy of detecting non-crack images was also very good (96.9%). Thus, it is expected that the crack detection algorithm presented in this study will be able to detect the location and size of cracks in concrete. Moreover, these algorithms will help in observing the soundness of structures and ensuring their safety. Full article