Search Results (8,652)

Accurate prediction of the peak impact force of reinforced concrete (RC) structures subjected to impact loads is critical for its impact-resistant design and structural performance assessment. Traditional approaches, such as experimental testing and refined finite element (FE) analysis, are generally time-consuming and resource-intensive. To address these limitations, this study develops an application-oriented machine learning (ML) framework for predicting the peak impact force of RC beams during the impact process. A comprehensive dataset comprising 144 samples is constructed from publicly available drop-hammer impact tests on rectangular simply supported RC beams based on the predefined selection criteria. Five ML models, i.e., Support Vector Regression (SVR), Random Forest Regression (RFR), Gaussian Process Regression (GPR), Extreme Gradient Boosting (XGBoost), and Artificial Neural Networks (ANN), are employed to predict peak impact force, and the corresponding model performances are evaluated by comparing with the experimental results. The SHapley Additive exPlanations (SHAP) algorithm and Local Interpretable Model-Agnostic Explanations (LIME) are utilized to assess the relative importance of input features. Based on feature importance, Monte Carlo simulations are further conducted to examine the influence of varying numbers of input features on prediction performance, and a feature reduction strategy is proposed and evaluated. The reliability and effectiveness of the proposed feature reduction strategy are evaluated using five independent experimental cases that are completely excluded from model development, and eleven numerical simulation cases are employed for further supplementary assessment. It is found that: (i) XGBoost achieves the best predictive performance for peak impact force, with a coefficient of determination (R²) of 0.976 on the testing set; (ii) impact velocity and yield strength of longitudinal reinforcement are the most influential features for predicting peak impact force; (iii) the feature reduction strategy with seven input features provides an optimal balance between the model complexity and predictive accuracy for rapid structural design and performance assessment. Full article

The escalating intensity and depth of coal mining operations have exacerbated underground strata pressure, resulting in fracture-induced air leakage channels that heighten risks of coal spontaneous combustion and gas explosions. These challenges necessitate enhanced flexibility and strength in cementitious materials used for roadway support. This study investigated the mechanical properties of fiber-reinforced polymer concrete (FRPC) for mining applications through response surface methodology (RSM) using Design Expert software. Three critical factors: styrene–acrylic emulsion content (5–15 wt.%), polypropylene fiber length (9–15 mm), and fiber content (0.7–1.1 kg/m³), were systematically investigated to establish factor-performance correlations via 3D response surfaces. This study experimentally investigated the effects of styrene–acrylic emulsion content, polypropylene fiber length, and fiber content on the mechanical properties of fiber-reinforced polymer concrete for mining roadway support. Response surface methodology was used as an empirical statistical tool to describe the response trends and factor interactions within the selected experimental range. The regression models developed in this study should therefore be interpreted as local empirical models rather than mechanics-based predictive equations. Using the flexural-to-compressive strength ratio as an index, the optimal formulation of FRPC was 10% emulsion, 12 mm fibers, and 1.1 kg/m³ fiber content. Microstructural characterization indicated that polypropylene fibers effectively inhibited crack propagation through bridging effects, while styrene–acrylic emulsion formed continuous film-like network structures on cement surfaces. Synergistically, both components enhanced matrix densification, achieving concurrent improvements in flexibility and strength. Field applications demonstrated that FRPC significantly reduced air leakage channels, decreased the risk of coal spontaneous combustion, and improved the safety of coal mine production. Full article

This study combines a systematic review with a case study to address the following question: can Sentinel-2 data yield accurate Surface Soil Moisture (SSM) estimates? Among the 1158 papers identified through the review, 66 met the eligibility criteria based on the exclusive use of Sentinel-2 data to estimate SSM and on reference measurements acquired simultaneously. Analysis of the eligible papers reveals six interconnected critical issues, the foremost being the insufficient spatial and temporal density of point-based reference measurements and their limited availability. The case study is aimed at addressing these issues. Specifically, Theia SSM products—freely available at the plot scale with an accuracy of approximately 5 vol%—are used as reference measurements, enabling a three-year multi-temporal comparison with Sentinel-2 bands. The comparison across different land and vegetation cover types, including a burned area, shows that SSM retrieval accuracy from Sentinel-2 can be strongly modulated by vegetation status and soil moisture magnitude. Specifically, the maximum R² (Sentinel 2-bands against Theia SSM) increases by 0.44 (from 0.01 to 0.45) where NDVI is less than 0.35, and SSM is less than 15%. Moreover, extending the analysis to a multi-year time series improves R² relative to single-date results (i.e., by up to 0.43). Across eligible papers, different methodologies (21 machine-learning algorithms, 18 optical trapezoidal models, and 7 statistical methodologies), different image processing products (4 algorithms, 77 indices, principal component, and albedo), and all bands were used to identify the best methodology and/or optimal image processing products and/or optimal bands. The median R² for these outputs against the SSM reference data ranged from 0.44 (statistical methods) to 0.63 (optical trapezoidal models), indicating weak to moderate overall accuracy. Unlike other approaches, the optical trapezoidal models rely primarily on SWIR bands. Results show that SWIR bands yield moderate fit (R² up to 0.65) during the dry season, but negligible fit during the wet season. These values align with those of the case study, suggesting that the use of the Sentinel-2 imagery for SSM estimation is critical and requires well planned approaches. Findings reported in this paper provide concrete guidance on image selection, reference measurement design, and time-series length for researchers seeking reliable SSM estimation from Sentinel-2 data. Full article

A multi-faceted research framework integrating experimental tests, finite element (FE) simulations, and parametric analysis was adopted to investigate the compression–shear hysteretic performance of CFRP-confined concrete-filled steel tubes (CFRP-CFST). Nine circular specimens were designed, with the axial compression ratio and transverse CFRP confinement coefficient as key variables, to conduct material performance tests, laying a foundation for subsequent research. Displacement-controlled cyclic loading was applied in the tests to obtain hysteretic curves, observe the failure process, and calculate stiffness degradation and strength degradation. FE models were established using the constitutive relationships of corresponding materials and material parameters derived from tests (e.g., concrete plastic damage coefficients), and their reliability was verified by comparing simulation results with experimental data. Further stress analysis throughout the loading process was performed to reveal the stress distribution and evolution laws of concrete, steel tubes, and CFRP during loading. Finally, parametric analysis was carried out to explore the effects of material strength, transverse CFRP layers, and axial compression ratio on the hysteretic performance of the members. The results indicate that the specimens exhibit a stable four-stage mechanical response and typical failure modes, including steel tube shear fracture, CFRP tensile fracture, and concrete shear fracture. The established FE models can reliably predict the hysteretic characteristics and failure mechanisms of the specimens. While material strength, CFRP layers, and axial compression ratio significantly enhance the peak bearing capacity, they have little impact on the initial elastic stiffness or the overall trend of the skeleton curve. In addition, the steel tube and CFRP maintain effective synergy, improving the ductility and energy dissipation capacity of the members. Full article

The dry packing density $\phi$ of an aggregate mixture governs binder demand in concrete and the achievable density of unbound graded-aggregate bases. The Compressible Packing Model (CPM) of de Larrard gives $\phi$ only as the root of an implicit equation. Factoring out the analytical single-class solution and applying symbolic regression only to the multi-class correction gives an explicit, differentiable approximation for continuous Andreasen–Andersen gradations, assuming one specific packing density $\beta$ for all size classes; it vanishes as $\beta \to 0$ and $K\to 0$, though its asymptotic behavior differs from the CPM’s. On 1000 independent gradations, the four-variable formula reproduces the CPM with $R^2$ = 0.990 and root-mean-square error 0.0064 (0.0108 at the highest compaction index, at most 0.053 on the domain boundary). The surrogate can be smooth because the compaction relation sums over all candidates, filtering out the kink that the virtual packing density develops at each governing-class change. Its closed-form gradients are approximate and cannot locate interior optima in the distribution modulus. For TCVN 8859:2023 base gradations, the formula error stays below 0.020. The formula is validated against the CPM only; the CPM itself, checked separately against 64 glass-bead measurements, gives errors of 0.014–0.016. Crushed-aggregate validation remains future work. Full article

In prestressed concrete cable-stayed truss bridges, the prestress losses in the upper chords, tensile web members, lower chords, and closure segment of the mid-span region have a significant influence on the stress distribution and node displacements of the truss structure. To rapidly and accurately reveal the structural response laws induced by multi-source prestress loss coupling, an improved genetic algorithm (GA) and backpropagation (BP) neural network hybrid model, referred to as the improved GA-BP algorithm, was developed based on orthogonal experimental design. The model was validated by comparing its performance with those of BP, GA-BP, PSO-BP, and SABO-BP models under the same data partitioning. The improved GA-BP model achieved R² values of 0.9727, 0.9861, 0.9720, and 0.9524 for the maximum tensile stress, maximum compressive stress, maximum X-displacement, and maximum Z-displacement, respectively, with corresponding MAPE values of 2.28%, 0.86%, 0.43%, and 1.11%. Sensitivity analyses based on 800 sets of global stochastic predicted samples revealed that the prestress loss of the upper chord plays a dominant role in controlling node displacement and compressive stress, while that of the tensile web members serves as the core sensitive component inducing mid-span tensile stress exceedance. These findings provide practical guidance for structural health monitoring: the upper chord prestress loss should be prioritized for deformation control, while the tensile web members warrant close inspection to prevent tensile stress exceedance. The study demonstrates that the improved GA-BP neural network is suitable for predicting prestress loss effects and quantitatively evaluating structural responses for such complex bridges. Full article

Automated quantity checking directly from two-dimensional bridge drawings remains challenging because the required information is distributed across structural views, detail drawings, and tables, while recognition errors may propagate into deterministic engineering calculations. This study proposes a knowledge-driven intelligent agent for automated quantity checking of concrete bridge structures. A task-specific dataset containing 1362 drawing images is constructed with region-level and parameter-level annotations. A two-stage YOLO method first locates functional regions and then detects parameter-related objects within cropped structural views. The agent coordinates detection, OCR, and table-parsing tools, associates recognized values with parameter types, spatial locations, and bridge components, and organizes them into a unified representation for deterministic rule execution. Human-in-the-loop verification is introduced before calculation to control error propagation. Compared with single-stage detection, the two-stage method increases mAP@0.5 from 0.769 to 0.908, mAP@0.5:0.95 from 0.471 to 0.656, and Recall from 0.664 to 0.792. After verification by bridge design professionals, parameter accuracy increases from 82.77% to 100%, and the overall mean concrete volume error decreases from 23.94% to 2.98%. The framework also produces lower concrete volume errors than three prompt-based large-model baselines across all six evaluated structural types. The methodological novelty lies in integrating region-to-parameter drawing perception, agent-orchestrated heterogeneous information organization, parameter-level human verification, and deterministic engineering rules into a controlled workflow for concrete quantity checking and reinforcement information consistency checking. Full article

Fire remains one of the most damaging exposures a concrete structure can face, yet how entrained air interacts with that damage is still not fully mapped out. This work looks at that gap directly, testing concretes with nominal total fresh-concrete air contents of approximately 2% (control), 4% (AE-4), and 6% (AE-6) after exposure to temperatures between 23 °C and 700 °C, followed by either air or water cooling. Six properties were measured: dry unit weight, thermal conductivity, compressive strength, flexural strength, ultrasonic pulse velocity (UPV), and dynamic modulus of elasticity (DEM), and each was then modeled with a dedicated feed-forward artificial neural network (ANN) using only AE content and temperature as inputs. The compressive strength of the control mix fell from 65.30 MPa at ambient temperature to 8.57 MPa at 700 °C, an 87% loss, with comparably steep reductions recorded for the other properties. At every temperature tested, water-cooled control specimens retained less strength and stiffness than their air-cooled counterparts. The ANN models, trained with the Levenberg–Marquardt algorithm, reproduced the experimental trends closely, returning coefficients of determination between 0.9364 and 0.9735. Sensitivity analysis placed temperature well ahead of AE content as the dominant driver of property change in every model (sensitivity ratio of 2.36–7.25 versus 1.14–1.87), although AE content was never negligible. The models provide accurate predictions within the investigated experimental ranges and may support preliminary assessment of comparable air-entrained concrete systems. Scanning electron microscopy tied these numbers to what was actually happening inside the material: the C–S–H structure held together reasonably well up to about 500 °C, then broke down visibly by 700 °C, with the AE’s air voids appearing to interrupt crack growth along the way. Together, the results offer both a practical dataset and a set of ready-to-use ANN tools for assessing the residual condition of air-entrained concrete after fire. Full article

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 contributing 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. It is crucial to clarify that this yields a 16-fold mass reduction specifically at the floor-screed layer level (dropping from 120 kg/m² to 7.5 kg/m²). Consequently, this localized weight saving translates to an approximately 16.6% reduction in the total seismic weight (W) of the entire building. This substitution also provides 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 performed per the Turkish Building Earthquake Code (TBEC, 2018). HD-PUR substitution reduced base shear by 10.5–20.0% and inter-story drift by 10.4–20.2% across all models. These trends were validated against an existing five-story RC building in Beyoğlu, Istanbul (site class ZC; PGA = 0.359 g; in situ concrete class C14), modeled 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 nonlinear 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 heating degree-day load associated with the floor-slab envelope interfaces (basement ceilings and roof slabs), 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 resilience, energy efficiency, and environmental performance in multi-story RC buildings. Full article

Predicting the shear strength of polypropylene and macro-synthetic fiber-reinforced concrete (PFRC) beams remains challenging because many available formulations were originally developed for conventional reinforced concrete or steel-fiber-reinforced concrete. This study develops practical and explicit symbolic equations for estimating the shear strength of PFRC beams without transverse reinforcement. A dedicated database comprising 98 shear-critical PFRC beam specimens was assembled using clearly defined inclusion and exclusion criteria. Seven predictors representing beam geometry, concrete strength, longitudinal reinforcement, and fiber properties were employed. Gene Expression Programming (GEP) and Multi-Expression Programming (MEP) were used to derive explicit mathematical equations, which were evaluated using a hold-out testing subset and compared under consistent data conditions with six benchmark machine learning algorithms and twenty existing shear strength formulations. Unlike previous studies that generally focused on individual prediction approaches or specific classes of fiber-reinforced concrete, the present study combines a PFRC-specific database, two explicit symbolic regression methods, comprehensive benchmarking, and SHAP-based model interpretation within a unified framework. The proposed MEP equation achieved the best testing performance, with a coefficient of determination (R²) of 0.976 and a root-mean-square error (RMSE) of 15.75 kN. The corresponding mean absolute percentage error (MAPE) and coefficient of variation (COV) were 10.55% and 0.143, respectively. The GEP equation also demonstrated satisfactory predictive performance, with an R² of 0.965 and a MAPE of 18.16%. Among the benchmark machine learning algorithms, CatBoost achieved the best testing performance, with an R² of 0.968, but was outperformed by the MEP equation. The MEP equation also improved the highest R² among the twenty existing formulations from 0.883 to 0.976 and reduced the lowest MAPE among the existing formulations from 25.81% to 10.55%. Shapley additive explanations (SHAP) analysis indicated that beam width, shear span-to-effective depth ratio, and effective depth were the most influential variables governing the predictions of the MEP equation. Within the limits of the assembled database, the proposed MEP equation provides an accurate, transparent, and directly applicable approach for estimating the shear strength of PFRC beams without transverse reinforcement. Full article

The increasing demand for efficient manufacturing systems in the construction-materials industry creates a need for digital methods that enable logistics alternatives to be evaluated before physical implementation. This study applies discrete-event simulation (DES) to a real concrete paving block production system to evaluate two complementary dimensions of internal logistics improvement: logistics-system reconfiguration and coordinated resource scaling. An empirically parameterized case-study model was developed to represent mixing, vibro-pressing, curing, quality control, packaging, technological-pallet circulation, and internal transport. First, a baseline forklift-based configuration was compared with an improved configuration incorporating partial transport automation and reorganization of curing-zone material flow. In a single deterministic five-day run under identical technological assumptions, cumulative simulated output increased from 1825 to 3141 finished pallets, corresponding to a 72.1% higher output in the improved configuration. This result represents a deterministic case-scenario comparison rather than a replication-based statistical effect estimate. Second, a separate replicated resource-scaling experiment evaluated coordinated changes in forklifts, packing stations, and quality-control units. This experiment was not factorially crossed with the logistics-configuration comparison; therefore, no interaction effect between logistics configuration and resource level is inferred. The model is an offline DES decision-support representation rather than a fully integrated Digital Twin, as it does not include real-time synchronization, automatic state updating, or feedback-based control. The results demonstrate how DES can support scenario-based assessment of logistics redesign and resource-allocation decisions while making explicit the case-specific and methodological limits of the resulting performance estimates. Full article

Pre-existing damage in reinforced concrete (RC) beam–column joints may induce strain incompatibility between the original member and subsequently applied strengthening layers, limiting the applicability of conventional flexural-capacity models. This study experimentally and analytically investigates pre-damaged RC beam–column joints strengthened with high-strength steel strand wire mesh-reinforced engineered cementitious composite (HSSWM–ECC). Eight specimens were tested to evaluate the effects of pre-damage level, steel strand spacing, and axial compression ratio on beam-end flexural behavior. HSSWM–ECC effectively suppressed crack localization and concrete spalling, and all strengthened specimens ultimately exhibited beam-end flexural failure while the columns and joint cores remained largely intact. Compared with the unstrengthened specimen, the peak load increased by 39.24–63.88%. Reducing the strand spacing from 70 to 30 mm increased the peak load by 8.86%, whereas the higher pre-damage level caused an 11.28% reduction relative to the undamaged strengthened specimen. Within the investigated axial compression ratio range of 0.2–0.4, the difference in peak load remained below 3.64%, whereas increasing axial compression was associated with more pronounced post-peak strength degradation. Based on sectional force equilibrium and the plane-section assumption, an analytical model was developed by incorporating damage-induced strain lag and the contribution of the wrap-around side strengthening layers. The predicted capacities of the seven strengthened specimens agreed well with the experimental results, with deviations within 7%. The proposed model provides a rational approach for evaluating the beam-end flexural capacity of pre-damaged RC beam–column joints strengthened with HSSWM–ECC within the investigated parameter range and flexure-dominated failure mode. Full article

Concrete pier-supported double-layer reticulated shells are widely used in long-span industrial buildings, yet the influence of support equivalent stiffness on structural stability remains insufficiently understood. This study investigates the stability behavior of an elliptical cylindrical double-layer reticulated shell supported by concrete piers with steel bearings. A simplified calculation method for equivalent support stiffness is derived based on a series cantilever system model and validated numerically. Seven steel bearing web thicknesses (10–40 mm) commonly used in engineering practice are selected, yielding equivalent stiffness coefficients ranging from 0.017 to 0.065. Eigenvalue buckling analysis is performed, and three quantitative indicators—critical load factor, sensitivity, and saturation degree—are introduced to systematically evaluate the effect of equivalent stiffness on stability capacity. Results show that the equivalent stiffness coefficient is typically less than 0.1, and using rigid links (infinite stiffness) overestimates the stability capacity by approximately 7% compared with the lowest stiffness case. The sensitivity of stability capacity to stiffness decreases monotonically as equivalent stiffness increases, with a threshold identified at ξ = 0.05. When the saturation degree of the critical load factor exceeds 72%, further increasing equivalent stiffness yields diminishing returns. Buckling modes transition from local end uplift to overall lateral tilting as equivalent stiffness increases. These findings provide a theoretical basis for selecting rational support stiffness values in the design of concrete-pier-supported reticulated shell structures. Full article

To address the shortcomings of rapid stiffness degradation and single energy dissipation mechanisms in conventional post-tensioned unbonded prestressed concrete beam–column joints, this paper proposes a beam–column joint configuration that integrates a “friction-bending” two-stage energy dissipation mechanism. By adopting a low-prestress strategy, the joint enhances the energy dissipation ratio. Furthermore, energy dissipation bars with a secondary activation function in the energy dissipater form a stable third stiffness, thereby improving the hysteretic performance under large structural deformations. To clarify the influence of key design parameters on the hysteretic performance of the joint, this study established a refined finite element model using OpenSees3.3.0. A systematic parametric analysis was subsequently conducted, covering the number of prestressing tendons, initial prestress force, friction force, diameter of energy dissipation bars, and activation displacement ratio. The results indicate that the number of prestressing tendons only regulates the second stiffness and bearing capacity of the joint, with no significant effect on the energy dissipation capacity. The initial prestress force has limited influence on the joint stiffness and absolute energy dissipation; reducing the prestress can increase the equivalent viscous damping ratio by approximately 32%. The friction force is linearly and positively correlated with the activation force, enabling independent control of the joint’s energy dissipation capacity. Increasing the friction force can enhance the energy dissipation per cycle by 21.7%, without affecting the stiffness at each stage. The third stiffness is dominated by the compression-bearing mechanism of the energy dissipation bars. Enhancing the third stiffness can increase the peak loading capacity of the joint by 28.7%, while slightly improving the ultimate energy dissipation capacity. The research finding can provide a theoretical basis for the collaborative optimization design that achieves “low prestress for efficiency enhancement, friction dissipation for guaranteed energy absorption, and third stiffness for safety assurance.” Full article

Reordering operations in a declared finite, straight-line MLIR Transform IR core must preserve request availability, handle phases, effects, and completed outcomes. We present a model-relative certificate framework for this core, restricted to operation handles and matcher-generated carriers. Polynomial interfaces describe request menus, completed coalgebras represent success and failure, and exact branch descriptors compile to ordinary fixed-unit Petri nets. For total-success paths, our reordering theorem constructs the swapped path from residual guards and derives Petri interchange from disjoint compiled supports. With exact atomic source summaries, concrete-to-rich refinement, and an exact target quotient as premises, the two paths have equivalent concrete outcomes. Two worked instances cover unit annotations and guarded handle generation. An independent finite-model audit validates 29 complete-marking transitions and 12 local exchange squares, and rejects seven targeted semantic mutations. Checks of 39 archived MLIR cases in each of two runs confirm the expected payload outputs and diagnostics. A repeated evaluation across 522 files in a fixed corpus available during development constructs 617 V3 kernel records, compared with 292 for V2, and records five native successes among six candidates. A mirror benchmark over 39 conditions confirms the structural count formulas. These results validate the finite model-to-Petri construction and show that the frozen pipeline operates reproducibly on the declared corpus. Full article