Engineering

Abstract: Marbleized limestone processing waste (MLPW) was used as the primary raw material in this study. White Portland cement (CEM I 52.5, M500) and gray Portland cement (CEM I 42.5, M450) were employed as binders. The mixtures were prepared with an MLPW content ranging from 70 to 85 wt.% while maintaining a constant water-to-cement ratio (w/c) of 0.25. The specimens were manufactured by hyperpressing under compaction loads of 200 and 300 kN and subsequently cured for 7 days in a sealed moist environment. Specimens compacted at 200 kN achieved compressive strengths corresponding to brick strength grades M350 and M400. Increasing the compaction load to 300 kN resulted in specimens meeting the requirements of strength grades M400 and M500. When gray Portland cement (M450) was used as the binder at identical MLPW contents and water-to-cement ratio, specimens compacted at 300 kN attained compressive strengths corresponding to grades M400 and M450. The mechanical performance of the hyperpressed bricks produced with both white and gray Portland cement correlated well with the microstructural characteristics observed by scanning electron microscopy (SEM). The average density of all specimens exceeded 2100 kg/m³, allowing the developed materials to be classified as heavyweight concrete. The study aims to develop optimized compositions and a manufacturing technology for hyperpressed bricks based on marbleized limestone processing waste, thereby promoting the sustainable utilization of industrial by-products in the production of high-performance masonry materials.

Abstract: Daily precipitation projections for tropical high-altitude stations are critical for water resource management and flood risk assessment yet remain limited in data-scarce mountain environments. This study applies a chronology-preserving quantile delta mapping framework to five CMIP6 general circulation models under three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP5-8.5), comparing a historical baseline (1981–2010) with a future period (2051–2080) at a high-altitude tropical station (2160 m a.s.l.) in southern Ecuador. Observations derive from the SC-PREC4SA homogenized daily precipitation product. Pre-correction evaluation across nine performance metrics shows that all raw CMIP6 outputs yield negative Nash-Sutcliffe Efficiency when compared against point-scale observations over complex terrain, consistent with the systematic wet biases intrinsic to coarse-resolution GCM grids. Composite ranking by KGE, NSE, and Pearson r identified MPI-ESM1-2-LR as the best-performing model (r = 0.830). Independent split-sample validation (calibration 1981–2000, evaluation on the withheld 2001–2010 period) confirms genuine out-of-sample skill, with Kling–Gupta efficiency improving from negative values for every raw model to 0.60–0.76 after correction. Ensemble-mean annual precipitation changes are modest and directionally mixed (−1.3% to +5.1%), while extreme-event indices show consistent tail intensification: 50-year daily return levels rise from 67.6 mm day⁻¹ to 76.3–115.2 mm day⁻¹ across models and scenarios. A two-way variance decomposition shows that inter-model structural uncertainty exceeds scenario uncertainty in every calendar month, averaging 75% of total variance against 8% for scenario choice and 17% for their interaction. A Mann–Whitney comparison pooling bootstrap replicates across models detects a statistically significant difference in return levels between SSP2-4.5 and SSP5-8.5, , in contrast with the inconclusive result obtained from the four-to-five point estimates alone, illustrating the sensitivity of such tests to sample construction in small multi-model ensembles. The fully documented computational pipeline constitutes a transferable methodological framework for probabilistic precipitation projection in data-scarce tropical highland stations across the Andes.

Abstract: High water-cement (w/c) ratio concrete is widely used in non-structural applications but suffers from poor durability due to its porous microstructure. This study investigates ultrasonic surface treatment (UST) as a physical method to improve the durability of concrete with w/c ratios of 0.55 and 0.60. UST was applied externally to the formwork during the plastic stage to densify the surface layer. Treated and control specimens were tested under freeze–thaw cycling (0–100 cycles), sulfate dry-wet cycling (0–125 cycles), and accelerated carbonation (7–28 days), with ¹H NMR relaxometry and SEM used to characterize microstructural changes. UST reduced the ¹H NMR total porosity signal by 17.8% (w/c = 0.55) and 27.7% (w/c = 0.60), preferentially eliminating capillary pores (T₂ = 1–10 ms) by up to 65.8%. These pore structure changes translated into clear durability improvements. Freeze–thaw resistance gained 31–33 additional cycles before the 60% relative dynamic elastic modulus failure threshold, with 100-cycle mass loss reduced by 45.3% and 41.0% at w/c = 0.55 and 0.60. Sulfate attack mass loss at 125 cycles dropped by 48.0% and 41.9%, and the carbonation coefficient K decreased by 42–43%. The UST-to-control degradation rate ratio remained consistently near 0.57–0.58 across all test types, indicating that UST produces a permanent surface densification effect governed by capillary pore connectivity reduction. These findings suggest that UST offers a practical, low-cost approach for extending the service life of high w/c ratio concrete in aggressive environments.

Abstract: Flood-risk index classification asks a narrower question than operational flood forecasting: given a vector of composite risk indicators describing a place, can a classifier tell high-risk from low-risk locations, and does that ability survive contact with real geography? This study answers the question in three successive steps, each building on the result of the one before it. The starting point is a synthetic benchmark of 1,117,957 records distributed through Kaggle and referred to here, following the reviewer’s correction, as Resurrectum Diluvium: twenty ordinal composite indices with no verifiable link to any real place. Six classifiers — Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, LightGBM, and CatBoost — were benchmarked on this dataset, and Logistic Regression won decisively (AUC-ROC = 0.9256), a result later traced to the linear, additive way the synthetic indices were themselves generated rather than to any property of composite indices in general. To test that explanation against real evidence, the identical pipeline was applied to the FEMA National Risk Index at county resolution (n = 3140U.S. counties), with the target rebuilt from a loss-per-exposure rate rather than raw dollar losses after the latter was found to correlate with population size (r = 0.63) almost as strongly as with hazard. On this first real dataset the ranking inverted: Random Forest reached AUC = 0.7855 against 0.6680 for Logistic Regression, and a leave-one-region-out cross-validation across the four U.S. Census regions collapsed every model toward chance (mean AUC 0.52–0.55), a result a stratified random split alone would never have revealed. Because one confirmation is an anecdote and two are a pattern, the same pipeline was run a third time at census-tract resolution (n = 84,034 tracts, roughly twenty-seven times the county sample), using an independently downloaded and merged extract to rule out a one-off artifact. The tract-level results repeat the county-level story point for point: Random Forest again leads under random splitting (AUC = 0.8507) and again loses the most ground under spatial cross-validation (mean AUC = 0.590), while Logistic Regression, the weakest model under random splitting at both real-world scales, is consistently the one that generalizes best across untrained regions (mean spatial AUC = 0.638). Taken across all three stages and roughly 1.2 million observations, the evidence points to a specific and testable conclusion rather than a general claim about algorithms: a classifier’s apparent superiority on pooled, randomly split data says little about whether it has learned anything that holds outside the region it was trained on, and this gap remains invisible unless geography is deliberately excluded from at least one evaluation fold.

Abstract: Non-destructive in-situ tests, such as the plate bearing (plate load) test, are widely used to estimate the equivalent deformation modulus (e.g., E_v2) of existing road embankments. However, the depth of influence sampled by such a test is governed by the loading plate diameter, so a single test yields only an average, diameter-dependent modulus rather than the actual variation of stiffness with depth. This study investigates whether systematically varying the plate diameter, and inverting the resulting settlement–diameter (dispersion) curves, can recover the full depth-dependent stiffness profile, E(z). Synthetic settlement–diameter curves were generated using a Boussinesq-based forward model for four families of reference stiffness profiles, representing normal (stiffness increasing with depth) and reverse (stiffness decreasing with depth) linear and exponential trends, combined with six Poisson’s ratios and five profile slopes/exponents (30 cases per profile family, 120 cases in total). Two inversion strategies were applied to back-calculate E(z) from each dispersion curve: a classical Occam-type, smoothness-constrained (Tikhonov-regularized) nonlinear inversion, and a direct, closed-form Simplified Inversion Method (SIM) based on differencing the apparent-modulus-versus-diameter curve. Results were benchmarked against the known reference profiles. Once calibrated so that its governing parameters depend only on Poisson’s ratio and the shape of the measured dispersion curve, SIM could be applied blindly, without prior knowledge of the true profile, and recovered E(z) with markedly lower error than Occam’s inversion (WAD = 2.2–4.7% and RMSPE = 2.6–5.8%, versus 7.4–19.1% and 9.4–28.3%, respectively, across the four profile families). For the Poisson’s ratio most typical of earth materials, ν = 0.3, the calibration further collapses to a universal parameter set (I = 0.66; c = 1.3 for stiffness increasing with depth, c = 2.5 for stiffness decreasing with depth), which attains WAD ≤ 4.2% across all four families with no calibration equation at all. Notably, Occam’s inversion reproduced the settlement–diameter curve itself with good accuracy in most cases, yet this close data fit did not guarantee an accurate stiffness profile—a direct manifestation of the intrinsic non-uniqueness of the settlement-based inverse problem. These findings support the use of a Poisson ratio-calibrated direct inversion method as a practical, robust alternative to generic regularized inversion for recovering stiffness profiles from multi-diameter plate loading data.

Abstract: Fiber-Reinforced Polymers (FRPs) are increasingly adopted in structural rehabilitation due to their high strength-to-weight ratio, corrosion resistance, and ease of installation, making them suitable for extending the service life of reinforced concrete (RC) infrastructure. Studies on shear strengthening with CFRP was early focused on externally boning (EB) showed premature delamination between fiber and concrete which limits the bonding strength. Hence, This study experimentally evaluates the shear strengthening behavior of RC beams retrofitted using inside-groove bonded CFRP and hybrid techniques. A total of eight beam specimens with identical geometry, internal reinforcement layout, and concrete strength were fabricated and tested under four-point bending to generate a well-defined shear-critical region. The experimental program focused on directly comparing bonding configurations while also examining the influence of groove depth (10 mm and 15 mm) and steel anchorage detailing on structural response and failure mechanisms. The strengthened specimens achieved ultimate load increases ranging from approximately 10% to 23% relative to the control beam. Variation in groove depth within the investigated range did not significantly influence shear capacity, indicating that moderate groove penetration is sufficient to develop effective mechanical interlock. Steel anchors were introduced to restrain concrete cover separation and improve confinement of the bonded region. While anchorage did not substantially increase peak load, it successfully mitigated premature cover delamination near stirrup locations and altered the governing failure mode.

Abstract: Accurate prediction of residual flexural strengths is essential for the structural design and performance assessment of steel fibre reinforced concrete (SFRC). While machine learning models have recently demonstrated strong predictive capability, most of the existing approaches rely on heuristic feature selection, unconstrained architectures, and random validation splits that may overestimate generalization capacity. This study proposes a Hard-constrained Mechanics-Informed Neural Network framework for predicting SFRC residual strengths across independent experimental studies. A multi-study database comprising 860 observations from 18 experimental assemblies was analysed to identify statistically dominant and physically interpretable predictors. Based on exploratory statistical analysis and micromechanical assessment, a hierarchical shared-backbone neural network architecture was developed. Physical admissibility was enforced structurally through non-negativity constraints, monotonic dependence on effective fibre bridging capacity, and bounded signed transitions between CMOD stages. Model performance was evaluated using five-fold group-based cross-validation, where entire studies were excluded from training to rigorously assess generalization. The proposed architecture achieved predictive performance comparable to or exceeding conventional neural networks and ensemble models, while eliminating physically inadmissible predictions and reducing fold-to-fold variability. The results demonstrate that embedding mechanical principles directly into neural network architecture enhances robustness and interpretability without compromising accuracy. The proposed hard-constrained Physics-Informed Neural Networks (PINN) framework provides a transferable methodology for physics-guided machine learning in structural materials modelling.

Abstract: This paper presents an experimental study on extrudable cob-based and hemp-based materials, aiming to investigate their feasibility for 3D printing and their performance as sustainable construction materials. Cobcrete, a mixture of clay, sand, lime, straw, and water, and hempcrete, a composite material made from hemp hurd fiber and lime-based binders, have gained attention for their potential as a new construction material and possibly in 3D printing applications. The study focuses on assessing the workability, mechanical properties, and environmental sustainability aspects of these materials. A series of laboratory experiments are conducted to evaluate the printability, compressive strength, flexural strength, and water absorption characteristics of the extruded cobcrete and hempcrete specimens. Additionally, the impact of different mix ratios and curing conditions on the material performance is examined. The findings reveal that both cobcrete and hempcrete exhibit favorable printability potential and demonstrate promising mechanical properties suitable for construction applications. Moreover, these materials showcase sustainability advantages, including low embodied energy and potential carbon sequestration for hempcrete. The study contributes to the understanding of the feasibility and performance of 3D printable cobcrete and hempcrete, providing insights into their potential as eco-friendly alternatives in the construction industry. The results underscore the need for further research and development to optimize these materials for broader adoption and to address challenges such as standardization, regulatory compliance, and market acceptance. Ultimately, this study paves the way for utilizing 3D printable cobcrete and hempcrete in sustainable construction practices, contributing to the development of greener and more environmentally conscious building technologies.

Abstract: This study presents the development and comprehensive characterisation of a sustainable 3D-printable cementitious composition in which up to 40 wt.% of Portland cement was replaced by a ternary binder containing oil shale ash (OSA) and metakaolin (MK). Following laboratory optimisation, the developed formulations were successfully transferred to industrial production as pre-blended dry mixes at Sakret Latvia Ltd., demonstrating the feasibility of large-scale manufacturing of printable cementitious materials. Attention was devoted to the characterisation of the raw materials and dry mixtures using particle size distribution (PSD), scanning electron microscopy with energy-dispersive spectroscopy (SEM/EDS), and X-ray diffraction (XRD). Two compositions—a reference mixture (REF) and the ternary OSA mixture—were evaluated in terms of printability, mechanical performance, durability, and the anisotropic behaviour of 3D-printed elements. The ternary composition (due to the pozzolanic activity of MK and OSA) exhibited strength development resulting in compressive strength exceeding that of the reference mixture after 90 days of curing. The anisotropy study revealed a difference in the mechanical properties of printed samples compared with conventionally cast samples. Durability assessment, including capillary water absorption and surface freeze–thaw scaling tests performed using two standardised methods, demonstrated satisfactory frost resistance and confirmed the suitability of both mixtures for outdoor applications. The results further indicate that the layered manufacturing process governs moisture transport and mechanical anisotropy through interlayer interfaces. The developed pre-blended OSA–MK composite represents a promising low-carbon material for industrial 3D concrete printing, combining reduced cement consumption with reliable printability, mechanical performance, and durability.

Abstract: Concrete is the most widely used construction material in civil engineering, and its long-term performance and durability are directly related to structural safety and service life. To investigate the effects of early curing temperature and relative humidity on concrete performance, this study set different curing temperature conditions (20℃, 30℃, 40℃, 50℃, and 60℃) and relative humidity conditions (55%, 65%, 75%, 85%, and 95%). Compressive strength and impermeability tests were conducted on concrete specimens cured for 7 days, and comparative analysis was carried out using specimens of different ages under standard curing conditions. The results show that, under 55% RH, the compressive strength first increases and then slightly decreases with increasing temperature, with the most significant growth occurring between 40℃ and 50℃, followed by a decline at 60℃. The penetration height first decreases and then increases, indicating that moderate temperature elevation is beneficial to the development of concrete performance, whereas excessively high temperature may lead to performance deterioration. Under 20℃, with the increase in relative humidity, the compressive strength of concrete continuously increases and the penetration height decreases significantly, indicating that higher humidity is conducive to strength development and improvement of impermeability. Compared with 7-day curing, concrete under 28-day standard curing conditions exhibits higher strength and better impermeability, suggesting that prolonged curing age contributes to the continuous development of concrete performance. The research results can provide a reference for concrete curing control and durability improvement under complex environmental conditions.

Abstract: This study develops and experimentally verifies nonlinear finite element models for carbon-fiber-reinforced (CFRP) confined concrete-filled steel tube (CFST) columns under axial compression. ABAQUS- based 3D models represent the steel tube, concrete core and CFRP wrap using C3D8R solid elements and S4R shell elements, respectively, with contact interactions to capture interface behavior. Concrete is modeled with the concrete damage velocity (CDP) model steel and (CFRP) adopt bilinear and orthotropic elastic – plastic/elastic constitutive laws. Validation uses an experimental database of 72 columns (18 CFST,54 CFRP- confined) varying steel thickness (1.8-3.8mm) concrete strength (20-40 MPa), and CFRP layers (0-3). Numerical load- shortening curves, ultimate loads and failure modes closely match tests (Nu, exp/Nu,FEM=0.83-1.08) parametric studies show that increasing CFRP layers raises peak load and ductility and smooths post-peak softening, but concrete strength and steel thickness exert equal or greater influence on axial capacity. Modeled stress field reveal that CFRP delay outward steel deformation, promotes uniform stress distribution and mitigates local buckling. The validated models quantify confinements effects and provide insight in to interaction mechanics among concrete, steel, and CFRP. Results support the use of the FE framework for design-oriented parametric studies and for developing practical predication tools for CFRP- strengthened CFST columns.

Abstract: Discontinuity layout optimization (DLO) obtains upper bounds from networks of velocity discontinuities. An analogous lower-bound formulation cannot rely on line forces alone, since an integrated strength condition may admit local yield violations. We formulate a lower-bound DLO for plane Mohr-Coulomb plasticity using element-local nodal stresses. Equilibrium, traction continuity, prescribed tractions, and an inward polygonal yield surface are imposed on T3 stress elements; a VDLO line integral projects the admissible stress field onto the intersecting DLO network. Linear and quadratic Bernstein stress spaces both give sparse linear programs. Yield-constraint duals drive conforming mesh refinement, while active DLO points guide an independent upper-bound refinement. Prandtl footing, frictional-slope, and plane-strain extrusion calculations produce increasing lower bounds and decreasing upper bounds, together with similar critical regions. The resulting scheme places stress-based lower bounds and velocity-discontinuity upper bounds in a common adaptive setting without merging their admissibility conditions.

Abstract: The Large Zarima-Mayday dam Reservoir Project, situated in the Tekeze River Basin in Northern Ethiopia, represents a critical water infrastructure initiative designed to support large-scale agricultural expansion specifically sugarcane cultivation and supply downstream environmental flows. Despite its total impoundment capacity of 3.6 BCM, steep valley morphology limits active usable storage to 785 MCM. However, a major strategic flaw in the project’s execution is the failure to implement standard design protocols that integrate reservoir filling in parallel with phased dam construction. Consequently, civil works reached full completion without initiating impoundment, transforming a multi-million-dollar asset into an idle structure facing a four-year filling bottleneck and severe capital lockup. This study evaluates post-construction reservoir operational policies and water supply reliability using the HEC-ResSim model across a 51-years historical hydrologic dataset (1968–2018). Simulation results reveal that under full land development (40,000 ha), the reservoir suffers severe irrigation water deficits in 51% of simulated months. Scaled development scenarios of 30,000 ha and 25,000 ha reduce monthly deficit frequencies to 15% and 11%, respectively. To mitigate such delays in future megaprojects and ensure operational sustainability, integrating dynamic rule curves, phasing impoundment concurrently with civil construction, and adopting high-efficiency micro-irrigation systems are strongly recommended.

Abstract: This study quantitatively characterized corrosion morphology evolution of Q355B steel under magnetic fields (MFs) using non-contact 3D scanning. MFs exert a threshold-dependent effect on the corrosion morphology and spatial distribution of Q355B steel, while the macroscopic mass loss rate remains largely stable across different MF intensities, the maximum local pit depth peaks at 60 mT, increasing by 42.9%. Spatial autocorrelation shifts from longitudinal long-range to enhanced transverse continuity. Depth distributions follow lognormal distributions. Under uplift, corroded helical anchor bearing capacity varies nonlinearly with MF intensity, reaching a maximum at 30 mT due to the optimal synergy between enhanced surface roughness-induced interface friction and localized cross-sectional reduction. These findings support corrosion assessment and mechanical prediction for Q355B components in MF-coupled environments.

Abstract: Railway sleeper degradation is a major problem for track safety and maintenance planning, especially in real-world scenarios where ballast occlusion, variable lighting, and weather-induced surface changes make visual inspection hard. Surface-only image analysis often fails in complex field contexts, whereas traditional manual inspection techniques are labour-intensive and subjective. To overcome these issues, this study presents a hybrid deep learning based inspection system that combines image-level classification and object-level detection to identify cracks in prestressed concrete sleepers accurately. A dataset of 289 annotated field photos was collected from Arezzo LFI, Italy, including rejected and discarded images from failed and in-service sleepers. The dataset preserves natural visual entropy such as ballast interference, illumination gradients, and surface discoloration. Three pre-trained architectures VGG16, VGG19, and ResNet50, were assessed for classification. Three VGG19 variants were evaluated: baseline, Block-5, and GAP+BN. Baseline VGG19 achieved the highest accuracy (98.2%), outperforming VGG16 (95.0%) and ResNet50 (88%), confirming the effectiveness of texture-sensitive CNNs for crack-prone imagery. For object detection, YOLOv11 and RT-DETR were trained to localize sleepers and cracks under operational conditions. YOLOv11 achieved real-time sleeper localization (mAP@0.5 = 0.65), suitable for continuous monitoring, while RT-DETR improved robustness for crack detection (mAP@0.5 = 0.56) under shadows, occlusions, and low-contrast scenarios, reflecting the advantage of transformer-based attention for context-aware reasoning. Overall, baseline VGG19 and YOLOv11 demonstrated the highest performance in classification and detection. The framework is developed within the design-and-testing context of EN 13230 and offers a scalable pathway for intelligent sleeper inspection and railway asset management.

Abstract: The intensive development of transport infrastructure globally and in Poland has led to a rapid increase in the number of bridge structures. Prestressed concrete is currently the most widely utilized structural material, accounting for 43.4% of these structures. A primary advantage of prestressed concrete is its capability to achieve considerable span lengths; consequently, its percentage share in terms of total bridge surface area is even higher, reaching 58.2% by the end of 2017. Although visual inspections are feasible for exposed tendon components, evaluating the residual prestressing force and diagnosing internal cable degradation—such as corrosion, grout deterioration, and voids—in post-tensioned structures presents a significant technical and scientific challenge. This paper introduces a structural health monitoring (SHM) approach, utilizing either periodic inspections or continuous electronic monitoring, to evaluate anchorage condition. The proposed methodology employs a novel measurement system that identifies structural anomalies by utilizing pattern recognition algorithms applied to acoustic emission (AE) signals. Furthermore, the identified pattern classes have been correlated with crack opening widths. This correlation enables the tracking of crack propagation effects on structural stiffness, while simultaneously monitoring other degradative processes, including active corrosion and anchorage slippage.

Abstract: This study investigates upcycled high-density polyurethane (HD-PUR) as a substitute for conventional cement-based screed in multi-story reinforced concrete (RC) buildings. Conventional screed (≈2400 kg/m³) adds substantial seismic dead mass without con-tributing to lateral stiffness, amplifying base shear, inter-story drift, and overturning moments. HD-PUR, produced from industrial waste via mechanical re-pressing, has a density of ≈150 kg/m³ and thermal conductivity of 0.025 W/m·K, yielding a 16-fold mass reduction and near-negligible inter-story heat transfer. Three-dimensional finite element models of 5-, 10-, and 15-story moment-resisting RC frames were developed in SAP2000, with modal and response spectrum analyses per-formed per the Turkish Building Earthquake Code (TBEC, 2018). HD-PUR substitution reduced base shear by 11.2–16.8% and inter-story drift by 10–18% across all models. These trends were validated against an existing five-story RC building in Beyoğlu, Is-tanbul (site class ZC; PGA = 0.359 g; in-situ concrete class C14), modelled in SAP2000 and STA. The fundamental period shortened from 0.888 s to 0.793 s, global base shear (FX) decreased by 10.5%, vertical base reaction (FZ) decreased by 16.6%, and the non-linear pushover-based performance level improved from Collapse Prevention to Life Safety without any intervention on load-bearing members. Thermal calculations per TS 825 indicate an 18% reduction in the building envelope's heating degree-day load, while life-cycle assessment data reported in the literature point to appreciably lower embodied carbon, supporting circular economy objectives. In short, HD-PUR floor fillers offer a low-cost strategy that jointly improves seismic re-silience, energy efficiency, and environmental performance in multi-story RC buildings.

Abstract: The critical-state branch of stress-fractional plasticity owes its analytical elegance to the Modified Cam-Clay surface, whose polynomial form admits closed-form Caputo gradients. We extend that elegance to the teardrop bounding surface of Chatwong et al., which is not polynomial in stress, by defining the operator as a Caputo derivative in the logarithm of normalized pressure, an orientation-corrected construction for that decreasing map (Section 2.3). On this axis the surface takes a power–exponential form, its critical-state terminal falls exactly at t* = 1/Ψ independently of Ω, and the fractional gradient reduces to incomplete-Beta–Kummer and Humbert-Φ₁ closed forms, verified against singularity-aware quadrature over 1,240 cases to relative errors below 10⁻¹¹. The resulting flow rule recovers associated flow (in the log-stress conjugate representation) as α → 1, and its flow vector coincides with the associated vector exactly at the critical state. Under the present endpoint-dependent hardening law and critical-state-dominated loading budget, substituting this operator for the fixed-window Grünwald–Letnikov flow of a companion factorial study collapses the dominant flow main effect from −60% to below 1% for both clays of the factorial study at every overconsolidation ratio tested, robustly across drained and approximately undrained proportional strain paths and conjugacy conventions — below 1% under the calibrated α(OCR) law, and within 2.5% across a constant-α sensitivity grid; the residual is a critical-state dwell transient, and the window semantics decide where fractional flow influence resides in a factorial design.

Abstract: While predefined cambering is widely implemented in prestressed concrete girder bridges to counterbalance dead load deflections, literature addressing its direct influence on structural behavior remains limited. Existing research focuses primarily on provided the required camber during fabrication and accidental deflections, with sparse attention dedicated to the interactive effects of diverse predefined initial profiles on structural performance. In this paper, an experimental investigation on the effect of predefined camber and accidental deflection on the structural behavior of both reinforced and prestressed rectangular concrete girders under four-point bending loading is presented. Fourteen full-size girders were tested, including eleven prestressed and three conventional reinforced concrete girders having concrete compressive strengths of 30, 40, and 80 MPa with predefined initial profiles ranging from −30 mm to +30 mm on key structural parameters, including cracking propagation, stiffness degradation, structural efficiency, displacement ductility, and total energy absorption. Test results have shown that all girders failed in a ductile manner via progressive cracking. It has been observed that positive predefined cambers could positively affect the performance of prestressed girders to a large extent, as the +30 mm predefined camber improved the ultimate load capacity by 19.3%.

Abstract: There have been considerable amounts of waste fibres produced as a result of the rapid expansion of the textile industry, which has led to major concerns regarding the environment and brought to light the necessity of developing recycling systems that are sustainable. To provide lightweight, thermally efficient, and environmentally friendly construction materials, this work valorizes Linters textile waste fibres (LTWF) as reinforcement in cement-based mortars. Thermal conductivity, diffusivity, effusivity, and volumetric heat capacity were determined using the flash method coupled with an inverse identification approach based on a genetic algorithm. The addition of LTWF greatly improved the thermal insulating properties of the mortars. At 7 wt.% LTWF, thermal diffusivity decreased by about 60% and thermal conductivity declined from 0.592 to 0.17 W·m⁻¹·K⁻¹, representing a 71% reduction. The composites also showed increased porosity and reduced density, demonstrating their lightweight insulating properties and ability to minimize building energy use. Mechanical characterization identified 3 wt.% LTWF as the optimal fibre content, producing gains of around 97% in flexural strength and 53% in compressive strength when compared with the reference mortar. From an environmental perspective, The application of LTWF significantly reduced the carbon footprint of the composites, resulting in a maximum 31.7% reduction in CO₂ emissions through partial substitution of cementitious materials and lower clinker consumption. Overall, the developed LTWF-reinforced mortars showed an excellent mechanical performance, outstanding thermal insulation, lightweight behavior, and enhanced environmental sustainability, confirming the strong potential of recycled LTWF as a cost-effective reinforcement for next-generation sustainable composites and circular economy applications.