Abstract: This study examines the impact of rolling direction on Barkhausen noise emission from the low-alloyed steel MC 500 during a uniaxial tensile test. The samples of gauged shape were cut along both the rolling and transverse directions to investigate the process of magnetic anisotropy alterations, as expressed in terms of Barkhausen noise and the extracted features. Barkhausen noise was studied as a function of both elastic and plastic straining, and the role of domain wall realignment with respect to the rolling direction, as well as the direction of the tensile load, was analysed. Barkhausen noise emission is linked to both the stress state and the microstructure, and the role of external stressing is contrasted with the residual stress state. Barkhausen noise in measured directly during tensile test (in situ) as well as after unloading (post situ). It was found that Barkhausen noise is significantly affected by stress directly during the tensile test (in situ), whereas the contribution of residual stresses is less pronounced. Barkhausen noise measured in situ during the tensile test in the direction of the tensile load is higher compared to the transverse direction. However, this relationship is reversed for the post situ measurements, especially for the more developed plastic strains. The influence of rolling direction on Barkhausen noise is relatively minor, and Barkhausen noise after matrix yielding is primarily affected by increasing dislocation density.

Abstract: Porous eco-materials—such as perlite, pumice composites, foamed concretes, and bio-derived cellular solids—are increasingly used in sustainable construction due to their low density, thermal insulation capacity, and reduced environmental impact. However, their mechanical characterization remains incomplete, particularly with respect to transverse shear behavior. Classical formulas for the shear correction factor ks, typically derived for homogeneous continua, are unsuitable for porous media exhibiting local density gradients, irregular pore morphologies, and spatially varying stiffness. This paper presents a generalized analytical–numerical methodology for evaluating the shear correction factor in a wide class of porous eco-materials. The approach is based on the strain-energy equivalence principle and uses a continuous stiffness model that reflects density-dependent elastic properties. A voxel-based microstructural representation is employed to validate the analytical predictions and to quantify the influence of heterogeneity on the shear stress distribution. Perlite is used as a representative case study, demonstrating how classical homogeneous formulas may produce errors exceeding 40%, while the proposed method provides significantly improved agreement with numerical benchmarks. The framework is applicable to a broad range of porous materials and offers a consistent basis for predicting transverse shear stiffness in lightweight fillers, thermal barriers, and fire-protective building components where shear deformation is critical.

Abstract: Land subsidence is a critical geohazard that threatens infrastructure stability, environmental sustainability, and public safety, particularly in data-scarce and terrain-sensitive regions. This study presents a GIS-based framework for mapping land subsidence susceptibility using a slope-based polynomial regression approach. Twenty national and international case studies were reviewed to identify key environmental and hydrological factors influencing subsidence. Statistical correlation and regression analysis revealed slope as the most influential parameter, exhibiting a strong non-linear relationship with subsidence occurrence. The developed regression model was applied to a selected road corridor in Meghalaya using slope data derived from Digital Elevation Models (DEMs) within a GIS environment. The resulting susceptibility map categorizes the study area into low, moderate, and high-risk zones, which show strong agreement with known patterns of terrain instability. The proposed approach offers a cost-effective and scalable method for preliminary subsidence risk assessment in regions lacking extensive field monitoring and provides valuable insights for infrastructure planning and hazard mitigation.

Abstract:

The construction environment of hydraulic engineering is complex, while traditional safety monitoring methods suffer from low efficiency and delayed response. Although static recognition models based on improved YOLOv5s have enhanced detection accuracy, they still cannot assess behavioral persistence and struggle to achieve proactive early warning. To address this, this study integrates the improved YOLOv5s with the DeepSORT algorithm to construct an integrated real-time "detection-tracking-warning" system. The system utilizes DeepSORT to achieve stable personnel tracking in complex scenarios and triggers dynamic warnings based on spatiotemporal behavioral logic. A desktop prototype system was developed using PyQt5/PySide6. Experimental results show that the system achieves a Multiple Object Tracking Accuracy (MOTA) of 86.2% in multi-object occlusion scenarios; the accuracy of unsafe behavior warning exceeds 95%, with an average delay of less than 1.5 seconds. This research accomplishes a transition from passive recognition to proactive warning, providing an intelligent solution for safety management in hydraulic construction.

Abstract: In ultrasonic testing, full waveform inversion (FWI) is employed to recover internal material perturbations by fitting the simulated wave fields with sparsely measured wave signals at sensor locations using gradient-based optimization. Since the underlying optimization problem is inherently ill-posed, the resulting material fields without regularization contain substantial artifacts. Neural network parameterizations have been shown to produce superior reconstructions. Incorporating prior knowledge through data-driven transfer learning can further accelerate the reconstruction. To date, such approaches have been limited to uniform grids treated using convolutional neural networks. In this work, we extend this methodology to arbitrarily shaped domains using graph convolutional networks (GCN). The GCN-based FWI exhibits strong generalization capabilities. The proposed approach for the 3D elastic wave equation can be accelerated through inexpensive pre-training on a scalar 2D dataset, resulting in faster training and more accurate reconstructions. Numerical experiments demonstrate that the proposed method can generalize well to complex 2D and 3D geometries with diverse experimental setups involving different sensor positions, sources, and material properties.

Abstract: SLAM-based laser scanners generate extremely dense point clouds burdened with a high level of surface noise arising from random measurement errors and repeated scanning of identical regions. This increases data volume and complicates subsequent processing. The present study introduces four novel noise filtering and subsampling algorithms that selectively preserve the points closest to the true surface. Each algorithm assigns a filtering characteristic to individual points based either on their distance from a locally estimated (planar or quadratic) surface or on the degree of local eccentricity in the spherical neighborhood of the point. The proposed methods were tested on point clouds acquired using three SLAM scanners (Emesent Hovermap ST-X, FARO Orbis, and ZEB Horizon) in three different scenes with reference data acquired by a static terrestrial scanner Leica P40. All four proposed methods effectively reduced surface noise and data volume without compromising the cloud quality, clearly outperforming the standard subsampling tools (random, octree, or spatial subsampling). The most reliable surface noise removal in point clouds dominated by planar surfaces (building interior with planar walls) was achieved using the method based on local plane fitting. In contrast, the use of a quadratic surface proved more effective for uneven or rugged surfaces.

Abstract: Despite high-resolution topographic advances, abstracting static 3D data into physically meaningful indicators remains critical for river management. This study introduces a geometric moment technique to reflect river curvature and hydraulic characteristics within an integrated framework. Analysis was conducted on the Nakdong River reach using first, second, and third-order moments, W/D ratios, asymmetry indicators, and D50 data. Key findings are: First, the moment-based approach precisely quantified asymmetric variations and localized bed changes—captured via centroid deviation (M1), dispersion (M2), and mass bias (M3)—which traditional average-based indices fail to represent. This effectively transforms vast 3D datasets into "compressed records" for tracing hydraulic drivers. Second, sinuosity (S) analysis revealed that reaches with higher curvature (S ≥ 1.5) exhibited intensified variability in third-order moments and asymmetry due to imbalanced hydraulic forcing. Specifically, the horizontal misalignment between the velocity core and the thalweg was identified as a key mechanism driving geometric imbalance in curves. Third, a W/D-asymmetry quadrant analysis categorized reach-scale morphological types and identified hydraulically vulnerable zones. By integrating sectional geometry, velocity distribution, and sinuosity into a unified system, this study provides a quantitative framework for scientific river management and decision-making.

Abstract: Malaysia's 2012 amendment to the Uniform Building By-Laws introduced mandatory water efficiency requirements for new construction, yet the extensive inventory of public buildings constructed before this regulatory milestone remains largely uncharacterized in terms of water consumption patterns and efficiency potential. This study develops a comprehensive assessment framework specifically designed for evaluating water supply and demand in four critical public building types, namely government offices, hospitals, police stations, and mosques, constructed before the UBBL 2012 amendment. Through systematic analysis of international water benchmarking literature and synthesis of building-specific consumption patterns, an integrated assessment methodology is proposed combining water auditing protocols, high-resolution metering strategies, cluster-based benchmarking approaches, and building-type-specific performance indicators. Literature synthesis reveals substantial variability in public building water consumption internationally, with hospitals demonstrating consumption ranging from 103 to 458 cubic meters per bed per year, government offices showing documented savings potential of 31 to 82 percent through systematic monitoring programs, and mosques achieving approximately 45.5 percent fresh water savings through greywater reuse from ablution facilities. However, police stations represent a critical research gap with zero documented consumption studies in the available literature. The proposed framework establishes building-type-specific indicators, standardized data collection protocols, and benchmarking clusters to enable systematic assessment and prioritization of retrofitting interventions for Malaysia's pre-2012 public building stock.

Abstract:

This study presents a multi-index performance system that is systematically used to assess the binder synergy and fly ash reactivity of eco-sustainable cementitious composite (ESCC) using the Strength Activity Index (SAI) as a reference in line with ASTM C618. The partial replacements of fly ash with high and low calcium fly ash (HCFA and LCFA) were added to the fly ash to sand (FA/S) ratios of 0, 10, 20, and 30% with a constant mix parameter, such as a 50% ratio of water to slag and a 20% ratio of activator to slag. Initial Flow Index (IFI) and Flow Retention Index (FRI) were used to measure fresh-state performance, and compressive-, tensile-, and flexural-based indices, i.e., SAI, Tensile Strength Index (TSI), and Flexural Strength Index (FSI), were used to measure mechanical performance. The results indicate that flowability and workability retention decrease with an increase in FA/S ratio, with LCFA-based mixtures having better flow retention than HCFA systems. The optimum mechanical performance at a replacement level of 20% FA/S produced the maximum SAI values of about 112% HCFA and 110% LCFA with a consistent increase in TSI and FSI values at 28 days. When the replacement levels were increased (30% FA/S), all strength indices decreased with the effect of dilution and decreased the packing efficiency of the binder. Comparisons of SAI with the respective TSI and FSI values through correlation analysis showed that the quantitative relationship between compressive, tensile, and flexural behavior was definite and showed that compressive strength alone is not enough to extrapolate mechanical performance. Collectively, the proposed framework provides a reasonable performance-based basis for the manner in which fly ash could be utilized in the most effective way in eco-sustainable cementitious compositions.

Abstract: The precise determination of the rheological properties of road bitumen types is essen-tial for the reliable prediction of long-term pavement behaviour. The aim of this study is to compare different viscosity determination methods – approximations, capillary vis-cosity, Brookfield measurement, and complex viscosity determined by dynamic shear rheometer (DSR) – and to analyse their relationships with each other. Furthermore, the European and Australian bitumen classification standards are compared in terms of dynamic viscosity and penetration, according to which Australian bitumen types show more stable results. The statistical evaluation of the results obtained with the different methods was based on Pearson correlation analysis and relative deviation analysis. The results obtained show that the DSR measurements at 1.6 Hz are most closely related to capillary viscosity and best correlated with the other measurements, while the Heuke-lom equation relationship overestimates the dynamic viscosity. The Brookfield method provided higher viscosities for all tests. The study highlights that the results of different measurement methods can only be compared under shear conditions, and that the DSR-based approach can be more suitable for the introduction of a new European bi-tumen classification.

Abstract: Performance based evaluation of reinforced soil retaining structures often relies on numerical analyses that demand substantial time and expert effort, largely due to the complex interactions among soils, reinforcements, facings, and seismic loading. This study introduces an efficient approach for developing seismic resisting capacity curves for geosynthetic reinforced slopes with rigid facings, using a computer program built on the Force Equilibrium based Finite Displacement Method (FFDM). Positioned between conventional, non performance based limit equilibrium methods (LEM) and the more computationally intensive finite element method (FEM), the FFDM offers a practical platform for performance based seismic assessment in engineering design. The method is demonstrated through a re examination of the Tanada Wall, a geosynthetic reinforced soil retaining wall with a full-height rigid panel facing (GRS-FHR) that experienced strong shaking during the 1995 Hyogoken Nambu earthquake (ML = 7.2). Using only parameters available in published databases, the FFDM generates realistic seis-mic resistance curves and directly computes seismic displacements. Three advantages distinguish the FFDM from traditional LEM based Newmark approaches: (1) explicit incorporation of peak soil strength and post peak degradation along the slip surface, eliminating the need for empirical “operational” strength adjustments; (2) direct use of peak ground acceleration (HPGA/g) as input, avoiding reliance on empirically selected seismic coefficients; and (3) capability for back analysis, enabling soil strength and de-formation parameters to be calibrated from small observed displacements (on the order of 10⁻³ m) during medium scale earthquakes and subsequently used to predict structural response under more severe ground shaking.

Abstract: Current international standards (EN 12004; SI 4004) are testing ceramic tile adhesives under post-cure thermal aging. However, the standards do not address UV radiation exposure during the fresh-adhesive phase. This research investigated the bond strength of three commercial polymer-modified cement adhesives (C2TE, C2TE-S2, C2T) to porcelain stoneware tiles under simulated Eastern Mediterranean and desert conditions. Three commercial adhesives were exposed during the initial (uncured) period to elevated temperature (30°C), humidity variation (40-65% RH), and UV radiation (295-365 nm, 1.5-2.0 mW/cm²) for 20 minutes, followed by 28 days of curing. Pull-off testing and scanning electron microscopy, combined with quantitative directionality analysis, were used to characterize the mechanical performance and microstructural degradation. UV exposure of adhesives during tiling working time caused a drop of mean bond strength from 1.77 to 0.26 MPa (85% reduction) compared with 1.77 to 0.64 MPa (36% reduction) under hot-arid conditions. Microstructural analysis of the hardened pull-off adhesives revealed that exposure of the fresh adhesive to UV radiation causes thinning and degradation of the interfacial layer (15-40 µm), leading to a drop in macroscopic strength. In contrast, hot-arid exposure induces adhesive bulk cracking while preserving interface integrity. Fracture surface directionality (goodness parameter), crack density, and delamination percentage together distinguish interface failure from adhesive bulk degradation and provide a forecast of long-term durability. This combined SEM-mechanical approach identified critical gaps in testing protocols and enables evidence-based adhesive selection, as current EN 12004 classifications, which are based solely on mechanical properties, prove insufficient.

Abstract: Accurate detection of buried utilities and reliable characterization of shallow subsurface conditions are critical requirements in civil and industrial engineering projects, particularly in urban areas developed over conductive clay–marl formations. In such environments, commonly used electromagnetic techniques often fail due to severe signal attenuation, increasing uncertainty during excavation and infrastructure planning. This study presents a high-resolution engineering workflow based on Electrical Resistivity Tomography (ERT) for the simultaneous detection of buried stormwater and sewer pipes and the geotechnical characterization of shallow subsurface materials. The methodology was applied in an industrial area southwest of Pamplona (Navarra, Spain), where Eocene marls and clays dominate the geological setting. Three ERT pro-files, each 23.5 m long, were acquired using a pole–dipole array with a dense electrode spacing of 0.5 m, allowing decimetric-scale resolution and investigation depths of up to 7–8 m. Data were processed and inverted using both smooth (L2-norm) and robust (L1-norm) inversion schemes to evaluate their influence on anomaly detection and stratigraphic imaging. The resulting resistivity models clearly identified elongated conductive and resistive anomalies corresponding to known buried sewer and stormwater pipes, despite the highly conductive background. In addition, the ERT sections revealed lateral and vertical variations within the clay–marl sequence, including sandy and compact detrital facies of direct relevance for foundation design and excavation planning. Borehole data available in the study area corroborated the geophysical interpretation. A complementary Ground Penetrating Radar (GPR) survey confirmed the ineffectiveness of electromagnetic methods under the same conditions due to rapid signal attenuation. Rather than focusing solely on utility detection, the proposed approach frames ERT as a dual-purpose engineering tool capable of providing continuous subsurface infor-mation that bridges the gap between sparse borehole data and construction needs. The workflow presented here is transferable and scalable, offering a practical protocol for urban and industrial projects in conductive soils where conventional techniques are limited.

Abstract: Cracks and holes are commonly found in wooden components, and ancient Chinese wooden buildings represented by Yingxian Wooden Pagoda demonstrate the ability to work with defects. This study systematically investigated the effects of longitudinal cracks and circular holes on the load-bearing capacity of wooden beams through four point bending experiments on 1580 samples. The study focuses on load-bearing capacity as the core indicator and provides calculation formulas for section weakening coefficient and damage tolerance coefficient to quantitatively evaluate the impact of cracks. Research has found that the harmfulness of damage strongly depends on its position within the wooden beam. In the horizontal direction, when the longitudinal crack is located in the pure bending section of the wooden beam, it has little effect on the load-bearing capacity of the wooden beam. Once it deviates to the transverse bending section, the load-bearing capacity of the wooden beam significantly decreases. The hole is most dangerous when it is located in the horizontal center of the wooden beam, and it is also dangerous when it is near the loading point. In the vertical direction, the crack has the greatest impact on the load-bearing capacity of the wooden beam when it is located in the neutral layer, while its impact decreases when it is close to the upper and lower surfaces of the wooden beam. Holes have the least impact when approaching the neutral layer, which is different from the impact pattern of cracks. In addition, the hazard increases when the hole is located in the tension zone of the wooden beam, and decreases when it is located in the compression zone. The anisotropy and fiber structure of wood are the microscopic basis for the damage tolerance mechanical behavior of timber beams.

Abstract:

The recovery of water and other resources from urban water system (UWS) has long been practiced in many Mediterranean countries, but very little in Croatia, although EU policy is encouraging. The threats posed by climate change, the growing problem of water and food supply, the energy crisis, and environmental pollution encourage resources recovery by applying the circular economy principles within integrated resource management (IRM) framework. The paper analyzes UWS sustainable circulation processes of water, nutrients and energy and their components in coastal tourist areas that strengthening urban system (US) and environment sustainability. The concept that is explored in this paper use dissipative structures theory to analyze the complexity and sustainability of UWS, urban systems (US) and circular economy processes. The paper discusses the potential of UWS as a local resource of nutrients, water and energy, and considers a possible integrated approach to selecting a locally sustainable recovery concepts. It was established that at the heart of effective water, energy and nutrient management in urban areas lays the principle of IRM, which treats entire urban life support systems as an interconnected system. Fitting circular economy strategy within IRM framework increases efficiency of resource recovery, and overall sustainability of tourist environment, economy and ensure sustainable well-being.

Abstract: Snowpack plays a vital role in Earth’s water cycle, especially in mountain regions where it serves as a major source of freshwater. Accurate estimation of snowpack microwave backscatter is critical for retrieving key physical properties of snow, such as snow depth (SD) and snow water equivalent (SWE), typically modeled using radiative transfer models (RTMs). Among the various sources of uncertainty in RTM simulations, snow-ground reflectivity—used as a boundary condition—plays a critical role in influencing the ac-curacy of simulated backscatter. This study leverages high-resolution X- and Ku-band SAR backscatter aircraft measurements using SWESARR and SnowSAR from NASA’s SnowEx campaigns, co-located with in-situ snow pit observations in Grand Mesa, Colorado, to estimate the parameters governing the estimation of the snow-ground reflectivity and quantify the uncertainties associated with them. Focusing on the snow-ground interface, we compare multiple soil reflectivity models to assess the sensitivity of backscatter to key ground parameters such as surface roughness, moisture content, and specular to total reflectivity ratio (STRR). At X-band, increasing ground surface roughness reduced the simulated backscatter by ~1.5 dB across the tested range, and increasing the specular to total reflectivity ratio (STRR) produced an additional ~1.0 dB decrease. A Bayesian MCMC parameter optimization was used to estimate each parameter, and the posterior distributions were then analyzed to quantify the uncertainties. The retrieval sensitivity to the specular to total reflectivity ratio (STRR) is minimized in the 0.6-0.7 range and it can be fixed at 0.65 without having discernible impact. The Bayesian inversion reveals that extreme parameter values act as diagnostic indicators of unmodeled complexity rather than retrieval failures, with representativeness error often dominating over instrument noise. The study highlights the importance of the snow-ground backscatter boundary condition in forward modeling of snowpack backscatter and provides robust guidance on parameter ranges to reduce uncertainty in RTMs, ultimately aiding SWE and SD retrieval from active microwave observations. While this study relied on Grand Mesa, the framework developed here, along with the model uncertainty, is broadly applicable to other snow-dominated mountain regions where active microwave observations can be used for snowpack monitoring.

Abstract:

Accurately estimating actual evapotranspiration (ETa) is essential for sustainable water management, particularly in semi-arid regions. Although the SAFER algorithm provides a practical remote sensing-based approach, its sensitivity to parameter settings and reduced performance during dry periods limit its reliability. This study develops four parametric ETa models—two linear (LM-I, LM-II) and two nonlinear (NLM-I, NLM-II)—and recalibrates SAFER coefficients via a simulation/optimization (S/O) approach. Models were evaluated using Landsat-8 data (LST, NDVI, αₛ) and reference evapotranspiration (ETo), and compared with machine learning methods: Random Forest (RF), Bagged Trees (BT), Support Vector Machines (SVM), and Generalized Additive Models (GAM). Results indicate that nonlinear models better capture the physical behavior of ET processes and outperform linear models across key metrics. In particular, the NLM-II model achieved R² = 0.8295 and RMSE = 0.4913 on the test set, surpassing SAFER (R² = 0.8195, RMSE ≈ 0.5713), LM-II, and the best soft computing model, BT (R² = 0.8137, RMSE = 0.5084). Its physically grounded structure ensures stable, interpretable predictions that accurately reflect water–energy interactions and seasonal dynamics. These findings demonstrate that compact, physically based nonlinear parametric models provide a robust, operationally practical solution for ETa estimation under sparse Landsat-based datasets, outperforming both linear and black-box machine learning approaches.

Abstract: This study numerically examined the anchorage mechanism of rebar hooks under varying straight development lengths, including high stress levels. A Three-Dimensional Rigid Body Spring Model (3D-RBSM) was used for the investigation, which the model has successfully reproduced the experimental pullout test stress–slip relationships and inner–outer strain distributions for the case of bonded hook part with and without a straight development length. The numerical model, which considered both hook and straight development length was able to output local concrete stresses and internal crack propagation enabling a clear interpretation of how straight development length influences the anchor-age mechanism. The results revealed that increasing straight development length increases stiffness, reduces rebar strains and concrete stresses in the hook region, promotes crack formation around the rebar surface and forms maximum tensile stresses closer to the top surface, ultimately resulting in earlier splitting failure at high rebar stress levels. A comparison of cases with and without hooks shows that combining the hook with straight development length improves stress distribution, delays crack propagation and increases anchorage by reducing tensile stress concentrations near the top surface and side faces. The findings offer insights to support rebar hook anchorage design and review of existing standards.

Abstract: Web openings are created in reinforced concrete deep beams for various purposes. The CFRP strengthening technology is commonly employed to mitigate the adverse consequences of these openings. The impact of openings generated in areas of stirrupless or by arranging the stirrups at the bottom and top chords of the opening in a closed configuration has been examined in numerous studies. However, in reality, stirrup damage frequently occurs when openings are made due to the high number of stirrups employed in deep beams. In this study, three specimens tested in a previous experimental study were modeled via ABAQUS, and the results obtained were validated by comparing them with the experimental results. To create openings of varying sizes in the elements, the reinforcements were cut, and these beams were strengthened with CFRP laminates, followed by a parametric study. The findings indicated a 56% reduction in the load-carrying capacity of the unstrengthened beam (h = 500 mm) featuring a 300 mm diameter opening, alongside an 87% decrease in energy dissipation. Although the diameter of the opening, which was formed by cutting the stirrups, is less than one-third of the beam's height, the application of 1.8 mm thick laminates resulted in only limited improvement.

Abstract: This study presents a fundamental validation of an AI-based impact analysis framework for wooden structures, aiming to support efficient and automated engineering judgment in seismic design. Focusing on a single-story residential building, the proposed method quantitatively evaluates the influence of individual seismic elements and their spatial lo-cations on structural response. Numerical time-history analyses were conducted using a detailed three-dimensional nonlinear model, and parametric variations of stiffness and strength were systematically generated using an orthogonal array. Machine learning models were then trained to capture the relationship between these parameters and seis-mic responses, and explainable artificial intelligence (XAI) techniques were applied to in-terpret parameter influence. The results demonstrated that wall elements oriented parallel to the target inter-story drift consistently exhibited dominant influence, which is consistent with structural engineer-ing knowledge. In addition, model comparison revealed that linear regression achieved high accuracy in the elastic response range, while Gradient Boosting outperformed other models under strong excitation conditions involving plastic behavior. This difference re-flects the transition from approximately linear to highly nonlinear structural response. These findings suggest that a hybrid modeling strategy combining interpretable linear models and flexible nonlinear models is effective for impact analysis. Overall, this fundamental study demonstrates that the proposed AI-based framework provides a transparent, rational, and time-efficient tool for seismic performance evalua-tion of wooden structures, bridging data-driven analysis and practical engineering deci-sion-making.