The large-scale integration of renewable energy has compelled coal-fired power plants to operate under deep peak-shaving conditions, significantly increasing the failure risk of Circulating Fluidized Bed (CFB) boiler heating surfaces due to severe thermal and pressure fluctuations. To address the limitations of traditional static risk evaluations, this study proposes a novel dynamic risk assessment framework integrating a Spatial–Temporal Graph Convolutional Network (STGCN) and a Dynamic Bayesian Network (DBN). The STGCN, enhanced with an operation-adaptive dynamic cross-attention delay module, predicts spatiotemporal temperature and pressure variations across the high-temperature heating surfaces. The predicted variables are incorporated into the DBN as dynamic evidence, which utilizes Noisy-OR logic and an embedded Weibull physical degradation model to continuously quantify cumulative failure probabilities. Case study results demonstrate that the STGCN outperforms traditional LSTM and RNN baselines in prediction accuracy. Furthermore, the DBN effectively maps the distinct degradation characteristics of individual boiler components, accurately identifying the water wall and superheater as having the highest failure risks and the largest fluctuations in marginal failure probability during rapid load cycling. This integrated data-driven approach provides highly accurate, real-time risk predictions, offering essential decision-making support for the predictive maintenance and safe flexible operation of CFB boilers.
Particulate matter from small-scale combustion systems remains a concern, as fine and submicron particles are difficult to remove by inertial separation alone. Electrostatic precipitators (ESP) are a promising option for this application. However, the effective aerodynamic utilization of the available collecting area depends on the internal distribution of particle-laden flue gas. This study numerically investigates the gas-flow distribution and aerodynamic particle transport in a four-tube ESP, designed to increase the collecting surface area relative to a conventional tubular arrangement. Three geometrical configurations were evaluated using computational fluid dynamics: a basic four-tube model without flow guidance, a model with nine radial inserts, and a model with a screw-type guiding structure positioned in the T-junction region. The basic geometry showed strongly non-uniform flow distribution, with a maximum-to-minimum tube-average velocity ratio of 3.31 and a coefficient of variation of approximately 51%. Radial inserts reduced these values to 1.95 and 27%, respectively, while the screw-type structure provided the most uniform distribution, with corresponding values of 1.75 and 20%. Particle-velocity fields indicated that the guiding elements promoted a more even particle supply to the four tubes. The results demonstrate that inlet-flow conditioning is essential for the effective aerodynamic utilization of the enlarged collecting area in multi-tube ESPs.
Valve-controlled hydraulic servo systems have been broadly utilized in applications requiring fast response and high power, where input constraints represent one of the major limitations on control performance. To tackle this problem, this study proposes an adaptive asymptotic tracking control method considering input constraints. First, a nonlinear mathematical model of the valve-controlled hydraulic servo system is established and transformed into an integrator form to facilitate controller derivation. Second, a tracking controller is designed based on the robust integral of the sign of the error (RISE) method, and an adaptive law for the robust gain is developed. This method not only realizes asymptotic tracking but also guarantees that the control input magnitude remains within the prescribed bounds. The controller stability is rigorously analyzed via Lyapunov theory. Finally, the validity of the proposed control method is confirmed by comparative simulations. The results show that the amplitude of the control input can be effectively limited, and the tracking accuracy is further improved.
Proppant transport and placement in rough fractures strongly influence hydraulic fracture conductivity, while the mechanisms by which fracture-wall heterogeneity affects particle migration and deposition remain insufficiently understood. In this study, a Eulerian–Eulerian two-fluid model coupled with fractal fracture reconstruction is developed to investigate proppant transport and placement in rough fractures. Rough fracture surfaces with different fractal dimensions are generated using the spectral synthesis method, and a modified cosine-weighted transition algorithm is proposed to improve geometric continuity and mesh stability between the inlet and rough fracture regions. The effects of fracture roughness, injection velocity, particle size, particle density, and sand concentration on sand-bank evolution are systematically investigated. The results reveal that fracture roughness has a non-monotonic influence on proppant deposition: moderate roughness enhances near-wall disturbances and particle resuspension, reducing sand-bank accumulation, whereas excessive roughness increases particle interception, collision, and local flow disturbance, resulting in localized deposition. Increasing injection velocity from 0.15 to 0.8 m/s decreases the equilibrium sand-bank height by approximately 44%. Increasing particle diameter from 0.25 to 0.85 mm increases the maximum sand-bank height from 2.23 to 25.64 cm, while increasing sand concentration from 1 to 10 increases the maximum sand-bank height from 3.97 to 15.01 cm. Although rough and smooth fractures have identical average apertures, roughness-induced contraction–expansion channels redistribute particle trajectories and promote deeper fracture placement. This study provides insights into proppant transport mechanisms in heterogeneous fractures.
Accurate AFM nanoindentation analysis requires models that account for the rounded apex of real pyramidal indenters. Although exact force-indentation equations for n-sided blunt pyramids exist, their numerical complexity limits routine use. In this work, a simple closed-form analytical approximation is developed that directly relates force to indentation depth for blunt pyramidal indenters. The method employs first-order Maclaurin series expansions of the geometric terms and the generic indentation differential equation, yielding a closed-form second-degree polynomial expression that is readily implemented in AFM data analysis. Comparison with the exact solutions showed that the approximation error decreases with indentation depth and is governed by the pyramid geometry rather than the tip radius. Simulated and experimental AFM data confirmed accurate Young’s modulus estimation above a geometry-dependent validity threshold. For a four-sided blunt pyramidal indenter, the proposed criterion predicts minimum indentation depths ranging from approximately 10.4 Rc for θ = 15° to 2.4 Rc for θ = 45° where Rc is the tip radius and θ is the pyramid’s semi-included angle. Application of the model to simulated AFM datasets yielded Young’s modulus values between 18.5 and 19.8 kPa for a true modulus of 20 kPa, corresponding to errors below 8% in all examined cases. Furthermore, the closed-form equation provided very good agreement with AFM nanoindentation data obtained from human prostate cancer cells. It is also shown that the generic derived equation includes the case of a spheroconical indenter as a limiting case. Young’s modulus is obtained directly from the quadratic coefficient, eliminating the need for tip-radius calibration. In addition, the formulation is applicable to heterogeneous materials, providing an effective local modulus through the weighted mean value theorem for integrals. The approach offers a practical and computationally efficient alternative for AFM data processing, improving the robustness of modulus estimation for soft biological materials.
Digital volume correlation (DVC) is widely used to extract internal displacement and strain fields from X-ray computed tomography (XCT) data, yet the limits imposed by the material’s own texture remain poorly quantified. This paper quantifies the individual and combined effects of particle volume fraction, particle geometry, and imaging noise on DVC accuracy. Sixteen specimens with prescribed volume fractions (0.25–10%) and particle shapes (spherical and angular) were generated using the discrete element method (DEM), loaded elastically in uniaxial compression, and converted into synthetic three-dimensional image datasets with and without additive Gaussian noise. Global DVC was performed in AVIZO and compared against the exact DEM ground truth. Under noise-free conditions, mean nodal displacement errors fall below 5% once the volume fraction exceeds 1%, whereas errors of 10–25% occur below this value; adding Gaussian noise with a variance of 0.001 raises the practical threshold to approximately 4%. Angular particles consistently outperform spherical particles at an equal volume fraction, a difference explained quantitatively by their 1.4-times-larger specific interfacial area and correspondingly higher image gradient. Median filtering favours spherical microstructures, whereas the Non-Local Means filter performs consistently across all microstructures. The results provide a priori guidelines for assessing DVC feasibility directly from microstructural descriptors.
To address the challenge of efficiently blending low-mutual-solubility gas–liquid two-phase systems, a composite structure comprising a Venturi and a static mixer was designed, and its flow field characteristics were analyzed using computational fluid dynamics (CFD) simulations. The results indicate that positioning the static mixer at the exit of the Venturi diffusion section yields optimal performance. This configuration prevents disruption of the jet premix flow field and facilitates the uniform dispersion of gas–liquid mixtures throughout the entire domain via six sets of SK-type single-spiral static mixer (SK) units following the initial blending. The composite structure exhibits a three-tier synergistic mechanism characterized by “suction–premix–mixing intensification”: the negative pressure zone within the throat tube induces suction of the gas phase, the diffusion section converts pressure energy to enhance shearing and crushing, and the static mixing section disrupts the axial jet through cutting and swirling effects, thereby generating secondary vortices. This process ultimately achieves uniform dispersion of gas and liquid across the entire domain. The structure’s lack of moving parts addresses the issues of low efficiency and unstable flow fields associated with traditional devices. This design facilitates enhanced crude oil recovery and low-pressure reservoir gas injection drilling.
Lightweight metro carbodies may exhibit elastic modes within ride-comfort-relevant frequency bands, limiting semi-active suspension controllers tuned offline for nominal conditions. This study proposes a skyhook-based parameter self-tuning fuzzy control (PSTFC) strategy for lateral secondary suspension. Its rule base and membership functions remain fixed, whereas two input quantization factors and one output scaling factor are updated online from the carbody lateral velocity and carbody–bogie relative lateral velocity, enabling state-dependent adaptation without increasing fuzzy-inference complexity. A rigid–flexible coupled multibody model is developed using Craig–Bampton component-mode synthesis and validated against field vibration measurements from a Type-A metro lead car. The controller is evaluated using ADAMS/Rail–MATLAB co-simulation, with robustness examined through repeated stochastic simulations and variations in vehicle speed, passenger load, track-irregularity intensity, and suspension parameters. The flexible model reproduces the measured location-dependent spectral characteristics more accurately than the rigid-carbody model. Under nominal conditions, PSTFC reduces the rear-carbody lateral-acceleration RMS from 0.1341 to 0.1015 m/s2 and the Sperling ride comfort index from 1.5485 to 1.2864, corresponding to improvements of 24.3% and 16.9% over passive suspension. Relative to fixed-parameter fuzzy skyhook control, the two indicators are further reduced by 5.8% and 4.7%, respectively. The improvement persists across the investigated off-nominal conditions without controller retuning. These results demonstrate that state-dependent parameter scaling improves the adaptability of fuzzy skyhook control while retaining a compact inference structure, providing a computationally tractable approach to flexible-carbody vibration suppression.
Sustainable machining of composite materials requires optimizing conflicting responses influenced by limited experimental datasets, trade-offs, nonlinear process variables, and response variability. This study proposes a hybrid framework (Gaussian Process Regression—Method based on the Removal Effects of Criteria—Criteria Reliability—Momentum-Based Optimization Algorithm: GPR–MEREC-CR–MOA) to address these challenges in turning composite materials (PA66, PA66 + GF30, and PA66 + MoS2). The GPR model learns from small datasets to capture nonlinear relationships between machining variables (workpiece material, tool approach angle, tool nose radius, cutting speed, feed rate, depth of cut) and performance characteristics (surface roughness, cutting force, vibration, tool wear rate, temperature, sound pressure level, specific cutting energy, and material removal rate). The MEREC-CR method considers experimental dispersion and response variability to enhance the robustness of the multi-response aggregation model. The weighted responses determined by MEREC were optimized by exploring the operating ranges of machining variables using MOA. The GPR model accurately predicts eight performance characteristics (R2 ≥ 0.973). The GPR–MEREC-CR–MOA model identified optimal conditions for PA66 + MoS2 and composite material (tool angle = 93°, nose radius = 0.40 mm, cutting speed = 200 m/min, feed rate = 0.300 mm/rev, depth of cut = 1.08 mm), resulting in a composite performance index (CPI) of 0.9265 and a 30.2% improvement over the best experimental datasets from Taguchi L27 design. The tool wear rate, specific cutting energy, and vibration have a significant impact on overall machining performance. Feed rate has the strongest influence on CPI, as confirmed by Partial Rank Correlation Coefficients analysis. Monte Carlo-driven uncertainty analysis validates the optimal solution with a 95% confidence level for CPI between 0.8859 and 0.9451. External validation with nine independent cases confirmed the GPR model’s strong generalizability (R2 = 0.811–0.998). Benchmarking showed that MOA achieves solution quality comparable to GA, PSO, and GWO while reducing computational time by 66–86%, making it suitable for real-time optimization. The proposed hybrid framework provides an alternative data-driven decision support approach for evaluating sustainable machining parameters using limited experimental datasets of polymer composites.
Fire-induced domino effects in tank farms can be catastrophic, particularly under wind conditions. However, due to multiple evolutionary stages, Computational Fluid Dynamics (CFD)-based modelling of wind-influenced, fire-induced domino effects and tank farm Time to Failure (TTF) calculation remain computationally expensive. This study addresses this gap by using Fire Dynamics Simulator (FDS) to model fire-induced domino effects in a tank farm and perform detailed tank farm TTF calculations across multiple wind speeds and primary pool fire scenarios. The FDS results showed that increasing wind speed from 0 to 8 m/s altered domino escalation, increasing incident heat flux on the downwind in-line tank by more than sevenfold (a 35% reduction in tank farm TTF). A new perturbation-based analytical formulation was then proposed for rapid determination of tank farm TTF under wind effects, without requiring complete CFD simulations of pool fire escalation. The formulation updates tank farm TTF under the no-wind baseline solution with wind-influenced perturbative correction terms. The proposed formulation agreed with the detailed CFD modelling-based calculation, with a mean relative error of 2.8% across all primary fire scenarios and wind conditions. This formulation provides a practical basis for rapid assessment of domino effects due to pool fire under wind conditions. However, it is calibrated for one specific six-tank configuration and crosswind directions and is not yet general.
Assembly-oriented geometric models of aero-engine casings require the spatial distribution of deviations at mating interfaces. Conventional scalar descriptors, including flatness, axial runout, and radial runout, cannot retain this information. This study proposes a measurement-driven integrated modelling method based on measured point clouds. After boundary completion, gross-error removal, and Gaussian filtering, the interface morphology is reconstructed as a tensor-product cubic B-spline surface in unit-weight non-uniform rational B-spline (NURBS) form. The reconstructed surface is then integrated with the nominal computer-aided design (CAD) model. Validation was performed using two cuboidal specimens and three representative casing flange surfaces. The relative differences between the reconstructed and measured flatness values of the cuboidal specimens were −8.58% and −1.04%. At the withheld verification points of the casing flange surfaces, the mean absolute reconstruction errors were 0.0022 mm, 0.0017 mm, and 0.0049 mm. These results show that measured interface morphology can be transferred into a CAD-compatible component model while retaining its spatial characteristics. The present study provides a geometric basis for subsequent assembly analysis.
Non-uniform illumination in tunnel environments severely degrades image quality, posing substantial challenges to visual monitoring and intelligent transportation systems. While histogram equalization (HE) remains prevalent due to its computational simplicity, its non-linear pixel transformations frequently induce over-enhancement, artifacts, and structural distortions. This paper proposes Prior-Guided Histogram Equalization (PGHE), a lightweight enhancement framework that integrates conventional HE with Retinex-based illumination priors. Within the Retinex decomposition paradigm, PGHE constructs a contrast illumination map from the ratio between the HE-enhanced image and the original input. A Prior Correction Module (PCM) subsequently refines this map via relative total variation regularization, thereby restoring spatial coherence and alleviating local discontinuities introduced by HE. The corrected map is then applied to the original image to obtain the final enhanced result. Extensive evaluation on the LOL low-light benchmarks and a proprietary tunnel dataset comprising 247 real-world frames shows that PGHE offers favorable trade-offs among contrast enhancement, structural fidelity, and brightness preservation: it is particularly strong in brightness preservation and Entropy, while its PSNR/SSIM on LOL and its NIQE on the tunnel dataset are comparable to, but not always the best among, the compared methods. Furthermore, the proposed PCM functions as a plug-in module that improves existing HE variants with measurable gains in Structural Similarity and perceived naturalness at a modest cost in Absolute Mean Brightness Error.
Paired comparison models are examined from the perspective of the placement of objects within specific comparison structures. For both pairwise comparison matrix-based models and stochastic models, previous studies have examined which comparison structures maximize the amount of information that can be recovered from incomplete comparisons. In this paper, we investigate how the amount of extracted information can be increased in stochastic paired comparison models—primarily the Bradley–Terry model—by way of exploiting prior information about the ranking of the objects, if such information is available. We examine several comparison structures to identify the optimal placement of objects within each structure with respect to information recovery and evaluability. The investigated structures are the star graph, the union of two star graphs, and the union of two edge-disjoint spanning trees. Parameters are estimated using the maximum likelihood method. The applied evaluation metrics are the Euclidean distance, Pearson, Spearman, and Kendall correlations, called similarity metrics. Moreover, the rate of evaluable datasets and an inconsistency index is also computed. We found that, in almost all cases, all four similarity metrics identified the same placement as optimal. Our results show that, for the star graph, placing an object of medium strength at the center and comparing all other object to it maximizes the amount of information recovered from the comparisons. For the union of two star graphs, placing objects that occupy middle positions in the ranking at the centers also outperforms the commonly used best–worst centered placement. However, the union of two edge-disjoint spanning trees provides, on average, even better information recovery based on all investigated metrics. We also examined the proportion of evaluable datasets and found it to be higher when medium-strength objects were placed at the centers. Finally, we compared the findings obtained from the stochastic models with those from pairwise comparison matrix-based models and observed strong agreement between the two approaches.
Roofing systems strongly influence the energy performance and environmental footprint of buildings, particularly in hot–arid climates such as Saudi Arabia, where cooling dominates electricity demand; however, the comparative lifecycle environmental performance of alternative roofing strategies remains underexplored in this specific climatic and market context. This study therefore aims to evaluate and compare the environmental performance of four sustainable roofing strategies against a conventional flat roof (FR) baseline in order to provide evidence-based guidance for climate-specific roofing selection in Saudi Arabia. This study conducts a comparative cradle-to-grave lifecycle assessment (LCA) of four sustainable roofing strategies considering the hot–arid climate of Saudi Arabia. Green roof (GR), cool roof (CR), solar photovoltaic roof (SPV), and roof canopy (RC) were assessed using the ReCiPe 2016 method in the SimaPro software. The environmental impacts of these strategies were assessed across product, construction, use, and end-of-life stages relative to conventional flat roofs (FRs). The results indicate that the production stage consistently contributes the highest environmental impacts, with increases ranging from 30 to 3000% for GR, CR, and RC and exceeding 10,000% for SPV. On the other hand, the use stage offers the greatest reductions ranging from 10 to 200%, particularly for SPV and CR, due to operational energy savings and electricity generation. Overall, CR demonstrates the most balanced environmental performance, combining high impact reductions with minimal trade-offs, while SPV provides significant climate and fossil resource benefits but increases mineral resource use. These findings highlight the importance of climate-specific and resource-conscious selection of roofing strategies in Saudi Arabia and provide a transferable comparative LCA framework that can inform sustainable roofing decisions in other hot–arid and hot–humid regions, in support of the Kingdom’s Vision 2030 objectives for sustainable urban development.
In the wet-process stage of lithium-ion battery manufacturing, double-sided coating combined with air-flotation drying can reduce repeated drying operations, thereby helping improve production throughput. However, the requirements of air-flotation drying for equipment stability, together with the coupling among process parameters such as temperature, air velocity, and tension, substantially increase the difficulty of process-parameter calibration. As critical components degrade over time, deviations arise between nominal process parameters and actual operating conditions, introducing non-negligible uncertainty and further complicating parameter recalibration. This paper proposes a collaborative robust multi-objective optimization algorithm to obtain stable and reliable process-parameter combinations under limited computational resources. Specifically, multi-objective optimization models are first established. Then, the operating condition of new equipment is approximately formulated as an undisturbed auxiliary optimization problem, whereas the operating condition of aged equipment with parameter perturbations is formulated as a robust optimization problem; surrogate models are constructed for both problems. Finally, search information from the auxiliary problem is used to guide the evolution of the robust optimization problem, thereby improving its optimization efficiency. Experimental results demonstrate that the proposed algorithm can obtain robust Pareto solutions with favorable convergence and diversity while consuming fewer resources, providing engineers with reliable references for selecting suitable process parameters.
Mechanical metamaterials (MMMs) are periodic architectures engineered to achieve extraordinary macroscopic mechanical properties. A primary objective in their design is wave propagation isolation, achieved through the generation of phononic bandgaps. These bandgaps are highly sensitive to the geometric features of the underlying unit cell, which is frequently based on a lattice topology. While additive manufacturing has become the predominant approach for fabricating these MMMs, a persistent challenge remains: standard finite element (FE) models based on nominal designs may differ from both the manufactured geometry and its numerical representation. In this context, manufacturing-induced geometric deviations and FE modelling discrepancies can both lead to dispersion characteristics that diverge from the intended behaviour. The present work focuses on the latter through a controlled numerical sensitivity study. This work assesses the impact of these FE modelling errors on the dynamic response of lattice metastructures by simulating structural deviations through conditional node addition and relocation. Specifically, we investigate the influence of two distinct scenarios that lead to significantly different outcomes: nodes subjected to Floquet–Bloch periodic boundary conditions, and interior nodes unaffected by these boundary constraints. Finally, a quantitative threshold for the maximum permissible modeling error is established for each case.
Accurate probabilistic analysis of wind-induced structural vibration is essential for accurately analyzing structural safety and serviceability. Though the FPK equation offers a tool for analysis, its application is challenged by the noise characteristics of wind spectra, such as the Davenport and Kaimal spectra. Using the conventional second-order linear filter model to fit Davenport and Kaimal spectra tends to underestimate their spectral energy in the mid-to-high-frequency range. To address this limitation, this paper proposes an improved second-order filter model that enhances fidelity without increasing filter dimensionality. This model is complemented by an optimization strategy based on the idea that the frequency range is partitioned, which generates three models specifically for low-, mid-, and high-frequency ranges. These models can better fit Davenport and Kaimal spectra in a much larger frequency range compared to the conventional model. The effectiveness of the proposed models is validated through numerically analyzing a linear SDOF stochastic oscillator and a nonlinear stochastic SDOF oscillator in various cases. The results demonstrate that the proposed models maintain exceptional accuracy across a wide range of structural natural frequencies.
Traffic states evolve on irregular sensor graphs and vary with calendar context, yet the original ASTGCN does not explicitly model how the contribution of different graph receptive fields changes across traffic periods. This paper proposes CD-MRFG, a context-gated extension of ASTGCN that encodes hour-of-day, day-of-week and weekend information and uses the resulting representation to weight Chebyshev graph-convolution orders in each spatio-temporal block. Under a common 12-step forecasting protocol, CD-MRFG reduced the overall MAE and RMSE of the reproduced ASTGCN baseline from 18.66 and 31.05 to 16.98 and 28.59 on PEMS03, from 22.79 and 35.02 to 20.82 and 32.77 on PEMS04, and from 18.88 and 28.83 to 17.24 and 26.84 on PEMS08. Three-seed experiments confirmed lower mean MAEs on PEMS04 (p = 0.028) and PEMS08 (p = 0.042), although the corresponding RMSE differences did not reach the 0.05 significance threshold. Ablation, gate-weight, sensitivity, complexity and convergence analyses showed that temporal context was the main source of the improvement and that the gate provided a model-internal view of order selection with moderate overhead. CD-MRFG remains less accurate than several stronger recent baselines, so its value is a bounded and interpretable extension of ASTGCN rather than a universal state-of-the-art replacement.
The roller–raceway contact response is a key factor affecting stress concentration, fatigue initiation, and raceway spalling in spherical roller thrust bearings. Uncertainty analysis of this response is therefore important for revealing how practical parameter fluctuations affect bearing contact behavior and for supporting robust bearing design and operating-condition optimization. In this paper, a multibody dynamic model of a spherical roller thrust bearing is established by explicitly considering the main internal contact pairs, including roller–raceway, roller–flange, roller–cage, and cage–guide interactions. The model is used to obtain the transient roller–raceway contact loads under coupled axial loading and rotational motion. The resulting contact loads are introduced into a finite element contact model to evaluate the dynamic contact stress response of the inner raceway. To assess the effects of random uncertainties on this response, an improved deep neural network (DNN) surrogate model is developed. An attention mechanism deep neural network (AM-DNN) is improved by incorporating feature importance information from random forest (RF) into its attention mechanism, and the resulting model is denoted by RF-AM-DNN. Validation on the generated dataset demonstrates that the proposed RF-AM-DNN outperforms conventional surrogate models in prediction accuracy. Finally, the RF-AM-DNN is used to investigate the uncertainty characteristics of dynamic contact stress in spherical roller thrust bearings under multiple uncertainty factors.
The integration of bio-based constituents into cementitious materials requires robust predictive models capable of describing mechanical degradation while supporting sustainability-driven material design. This study presents a macro-constitutive damage modelling framework for biomass-modified cement mortars subjected to monotonic compressive loading, combining experimental characterisation, continuum damage mechanics (CDM), and life-cycle assessment (LCA). The calibrated parameters are interpreted in terms of meso-scale mechanisms, but the study does not constitute a direct imaging-based multiscale characterisation. Mortars containing 0–10% dried microalgal biomass as a partial replacement for binder mass were investigated through their complete compressive stress–strain response. A scalar damage variable was employed to model stiffness degradation and progressive microcrack evolution, enabling the identification of elastic-modulus reduction, damage-initiation thresholds, softening behaviour, and residual load-bearing capacity. A thermodynamically consistent Mazars-type damage model was calibrated against the measured envelopes and internally verified by reproducing the same pre-peak and post-peak responses, with coefficients of determination ranging from 0.979 to 0.996. Increasing biomass content reduced the 28-day compressive strength from 47.8 to 23.7 MPa and the elastic modulus from 27.5 to 14.9 GPa, while increasing the damage level at peak load from 0.26 to 0.46 and promoting a more gradual post-peak softening response. The calibrated law provides a compact constitutive representation within the tested replacement range; independent external validation is still required before extrapolation to other biomass types, mixture proportions, or curing regimes. In parallel, a cradle-to-gate LCA quantified global warming, acidification, eutrophication, ozone depletion, and abiotic depletion potentials. An integrated carbon-efficiency index was used to relate mechanical performance to environmental impact. Biomass replacement reduced global warming potential by up to 7.7% but increased eutrophication potential, highlighting a clear performance–environment trade-off. Despite the reduction in mechanical properties, all mixtures satisfied masonry-unit strength requirements, supporting the application of biomass-modified mortars in low-carbon concrete masonry units. The proposed framework demonstrates how experimentally calibrated damage models can support the structural assessment and sustainable development of emerging bio-based cementitious materials.
