Search Results (1,352)

The growing demand for innovation and the use of diverse materials in cementitious composites necessitate predictive models that account for material variability. Numerical, code-based, and machine learning (ML) models have been developed to predict various concrete properties. However, their accuracy is significantly influenced by factors such as mix design, composition, intrinsic properties, and external conditions. Developing robust models that integrate these variables is essential for improving predictive accuracy and optimizing material performance. This paper presents a comprehensive review of numerical, code-based, and ML modelling techniques for predicting both fresh and long-term concrete properties. Since both numerical and ML models rely on experimental data—either to determine coefficients in numerical approaches or to train ML models—data gathering, preprocessing, and handling are crucial for model performance. Previous studies indicated that data variability significantly impacts accuracy, emphasizing the importance of effective preprocessing. While larger datasets generally improve reliability, some models achieve high accuracy even with very limited data. This review not only demonstrates the superior performance of ML models over traditional numerical approaches but also highlights the relative effectiveness of different ML algorithms based on reported accuracy metrics. ML-based approaches, including both ensemble and non-ensemble models, have exhibited strong predictive capabilities across a wide range of concrete property categories. In contrast, traditional numerical models often yield lower accuracy, although modified versions that incorporate additional parameters have shown improved performance. Furthermore, the integration of optimization algorithms and interpretability tools enhances both predictive reliability and model transparency—critical aspects that are often overlooked. Full article

Periodic visual inspections are currently conducted to maintain the condition of railway bridges. These inspections rely on direct visual assessments by human inspectors, often requiring specialized equipment such as aerial ladders. However, this method is not only time-consuming and costly but also involves significant safety risks. Therefore, there is a growing need for a more efficient and reliable alternative to traditional visual inspections of railway bridges. In this study, we evaluated and compared the performance of damage detection using U-Net-based deep learning models on images captured by unmanned aerial vehicles (UAVs). The target damage types include cracks, concrete spalling and delamination, water leakage, exposed reinforcement, and paint peeling. To enable multi-class segmentation, the U-Net model was trained using three different loss functions: Cross-Entropy Loss, Focal Loss, and Intersection over Union (IoU) Loss. We compared these methods to determine their ability to distinguish actual structural damage from environmental factors and surface contamination, particularly under real-world site conditions. The results showed that the U-Net model trained with IoU Loss outperformed the others in terms of detection accuracy. When applied to field inspection scenarios, this approach demonstrates strong potential for objective and precise damage detection. Furthermore, the use of UAVs in the inspection process is expected to significantly reduce both time and cost in railway infrastructure maintenance. Future research will focus on extending the detection capabilities to additional damage types such as efflorescence and corrosion, aiming to ultimately replace manual visual inspections of railway bridge surfaces with deep-learning-based methods. Full article

Accurately predicting the compressive strength of high-performance concrete (HPC) is critical for ensuring structural integrity and promoting sustainable construction practices. However, HPC exhibits highly complex, nonlinear, and multi-factorial interactions among its constituents (such as cement, aggregates, admixtures, and curing conditions), which pose significant challenges to conventional predictive models. Traditional approaches often fail to adequately capture these intricate relationships, resulting in limited prediction accuracy and poor generalization. Moreover, the high dimensionality and noisy nature of HPC mix data increase the risk of model overfitting and convergence to local optima during optimization. To address these challenges, this study proposes a novel bio-inspired hybrid optimization model, AP-IVYPSO-BP, which is specifically designed to handle the nonlinear and complex nature of HPC strength prediction. The model integrates the ivy algorithm (IVYA) with particle swarm optimization (PSO) and incorporates an adaptive probability strategy based on fitness improvement to dynamically balance global exploration and local exploitation. This design effectively mitigates common issues such as premature convergence, slow convergence speed, and weak robustness in traditional metaheuristic algorithms when applied to complex engineering data. The AP-IVYPSO is employed to optimize the weights and biases of a backpropagation neural network (BPNN), thereby enhancing its predictive accuracy and robustness. The model was trained and validated on a dataset comprising 1030 HPC mix samples. Experimental results show that AP-IVYPSO-BP significantly outperforms traditional BPNN, PSO-BP, GA-BP, and IVY-BP models across multiple evaluation metrics. Specifically, it achieved an R² of 0.9542, MAE of 3.0404, and RMSE of 3.7991 on the test set, demonstrating its high accuracy and reliability. These results confirm the potential of the proposed bio-inspired model in the prediction and optimization of concrete strength, offering practical value in civil engineering and materials design. Full article

This study systematically investigated the corrosion behavior of P110 pipeline steel in simulated carbonated concrete environments through a combination of electrochemical testing and multiphysics simulation, with particular focus on revealing the evolution mechanisms of corrosion product deposition and ion concentration distribution under half crevice structures, providing new insights into localized corrosion in concealed areas. Experimental results showed that no significant corrosion occurred on the P110 steel surface in uncarbonated simulated pore solution. Conversely, the half crevice structure significantly promoted the development of localized corrosion in carbonated simulated pore solution, with the most severe corrosion and substantial accumulation of corrosion products observed at the crevice mouth region. COMSOL Multiphysics simulations demonstrated that this phenomenon was primarily attributed to local enrichment of Cl⁻ and H⁺ ions, leading to peak corrosion current density, and directional migration of Fe²⁺ ions toward the crevice mouth, causing preferential deposition of corrosion products at this location. This “electrochemical acceleration-corrosion product deposition” multiphysics coupling analysis of corrosion product deposition patterns within crevices represents a new perspective not captured by traditional crevice corrosion models. The established ion migration-corrosion product deposition model provides new theoretical foundations for understanding crevice corrosion mechanisms and predicting the service life of buried concrete pipelines. Full article

Aiming at the lightweight design of a bridge-shed integration structure, this paper presents a three-layered absorbing system in which a part of the sand cushion is replaced by expanded polystyrene (EPS) geofoam and the reinforced concrete (RC) protective slab is arranged above the sand cushion to enhance the composite system’s safety. A three-dimensional Smoothed Particle Hydrodynamics–Finite Element Method (SPH-FEM) coupled numerical model is developed in LS-DYNA (Livermore Software Technology Corporation, Livermore, CA, USA, version R13.1.1), with its validity rigorously verified. The dynamic response of rockfall impacts on the shed slab with composite cushions of various thicknesses is analyzed by varying the thickness of sand and EPS materials. To optimize the cushion design, a specific energy dissipation ratio (SEDR), defined as the energy dissipation rate per unit mass (η/M), is introduced as a key performance metric. Furthermore, the complicated interactional mechanism between the rockfall and the optimum-thickness composite system is rationally interpreted, and the energy dissipation mechanism of the composite cushion is revealed. Using logistic regression, the ultimate stress state of the reactive powder concrete (RPC) slab is methodically analyzed, accounting for the speed and mass of the rockfall. The results are indicative of the fact that the composite cushion not only has less dead weight but also exhibits superior impact resistance compared to the 90 cm sand cushions; the impact resistance performance index SEDR of the three-layered absorbing system reaches 2.5, showing a remarkable 55% enhancement compared to the sand cushion (SEDR = 1.61). Additionally, both the sand cushion and the RC protective slab effectively dissipate most of the impact energy, while the EPS material experiences relatively little internal energy build-up in comparison. This feature overcomes the traditional vulnerability of EPS subjected to impact loads. One of the highlights of the present investigation is the development of an identification model specifically designed to accurately assess the stress state of RPC slabs under various rockfall impact conditions. Full article

Rapid pavement repair demands materials that combine accelerated strength gains, dimensional stability, long-term durability, and sustainability. However, finding materials or formulations that offer these balances remains a critical challenge. This study systematically evaluates two polymer-modified belitic calcium sulfoaluminate (CSA) concretes—CSAP (powdered polymer) and CSA-LLP (liquid polymer admixture)—against a traditional Type III Portland cement (OPC) control under both laboratory and realistic outdoor conditions. Laboratory specimens were tested for fresh properties, early-age and later-age compressive, flexural, and splitting tensile strengths, as well as drying shrinkage according to ASTM standards. Outdoor 5 × 4 × 12-inch slabs mimicking typical jointed plain concrete panels (JPCPs), instrumented with vibrating wire strain gauges and thermocouples, recorded the strain and temperature at 5 min intervals over 16 weeks, with 24 h wet-burlap curing to replicate field practices. Laboratory findings show that CSA mixes exceeded 3200 psi of compressive strength at 4 h, but cold outdoor casting (~48 °F) delayed the early-age strength development. The CSA-LLP exhibited the lowest drying shrinkage (0.036% at 16 weeks), and outdoor CSA slabs captured the initial ettringite-driven expansion, resulting in a net expansion (+200 µε) rather than contraction. Approximately 80% of the total strain evolved within the first 48 h, driven by autogenous and plastic effects. CSA mixes generated lower peak internal temperatures and reduced thermal strain amplitudes compared to the OPC, improving dimensional stability and mitigating restraint-induced cracking. These results underscore the necessity of field validation for shrinkage compensation mechanisms and highlight the critical roles of the polymer type and curing protocol in optimizing CSA-based repairs for durable, low-carbon pavement rehabilitation. Full article

Processed sea sand has emerged as a viable alternative to traditional fine aggregates in the Sri Lankan construction industry. Despite its economic and environmental advantages, concerns over residual seashell content have limited its widespread adoption by local contractors. Residual seashell content, typically ranging from 1% to 3% after processing, has raised concerns about its impact on the performance of concrete. This study systematically investigates the influence of seashell fragments, with a content of up to 5%, on the fresh, mechanical, and durability properties of sea sand concrete and mortar. Experimental results indicate that workability remains stable, with minor variations across the tested range of shell content. Compressive strength remains relatively consistent from 0% to 5% seashells, indicating that seashell content does not significantly impact the strength within this range. Durability tests reveal minimal effects of shell content on concrete performance within the tested shell range, as indicated by results for water absorption, rapid chloride penetration, and acid exposure testing. Accelerated corrosion indicates that the typical shell content does not increase corrosion risk; however, high shell content (>3%) can compromise corrosion durability. Overall, these findings demonstrate that the mechanical and durability performance of sea sand concrete remains uncompromised at typical seashell content levels (1–3%), supporting the use of processed sea sand as a sustainable and viable alternative to traditional fine aggregates in Sri Lankan construction. Full article

This research aimed to evaluate the impact of using brick waste powder (BWP) and varying lengths of polyester fibers (PFs) on the performance properties of asphalt concrete (AC) mixtures. BWP was utilized as a replacement for traditional limestone powder (LS) filler, while PFs of three lengths (3 mm, 8 mm, and 15 mm) were introduced. The study employed the response surface methodology (RSM) for experimental design and analysis of variance (ANOVA) to identify the influence of BWP and PF on the selected performance indicators. These indicators included bulk density, air voids, voids in the mineral aggregate, voids filled with asphalt, Marshall stability, Marshall flow, Marshall quotient, indirect tensile strength, wet tensile strength, and the tensile strength ratio. The findings demonstrated that BWP improved moisture resistance and the mechanical performance of AC mixes. Moreover, incorporating PF alongside BWP further enhanced these properties, resulting in superior overall performance. Using multi-objective optimization through RSM-based empirical models, the study identified the optimal PF length of 5 mm in combination with BWP for achieving the best AC properties. Validation experiments confirmed the accuracy of the predicted results, with an error margin of less than 8%. The study emphasizes the intriguing prospect of BWP and PF as sustainable alternatives for improving the durability, mechanical characteristics, and cost-efficiency of asphalt pavements. Full article

Liquid calcium aluminate cement (LCAC) is an innovative material technology with significant potential for varied applications in civil engineering. However, despite its promising results, a significant gap remains in the direct application of LCAC as a concrete binder. The primary catalysts for LCAC are sodium hydroxide (NaOH) and potassium hydroxide (KOH). Therefore, it is crucial to investigate the effects of sodium and potassium ions on alkali–aggregate reactions in concrete structures. This study evaluated the durability of liquid calcium aluminate cement concrete catalyzed using four different concentrations of NaOH (0.5%, 1.0%, 1.5%, and 2.0%) as experimental variables, incorporating a control group of traditional concrete with a water–cement ratio of 0.64. The findings indicate that NaOH catalysis in the concrete significantly trigger alkali–aggregate reactions, leading to volume expansion. Furthermore, it increased chloride ion penetration and porosity in the concrete. These effects were more notable with the increase in NaOH concentration. The results suggested that NaOH catalysis can enhance certain chemical reactions within the concrete matrix; however, its concentration must be carefully controlled to mitigate adverse effects. The NaOH dosage should be limited to 0.5% to ensure optimal durability of the concrete. This study emphasizes the crucial importance of precisely balancing catalyst concentration to maintain the long-term durability and performance of liquid calcium aluminate cement concrete in structural applications. Full article

Concrete mix design plays a pivotal role in ensuring the mechanical performance, durability, and sustainability of construction projects. However, the nonlinear interactions among the mix components challenge traditional approaches in predicting compressive strength and optimizing proportions. This study presents a two-stage hybrid framework that integrates deep learning with reinforcement learning to overcome these limitations. First, a Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) model was developed to capture spatial–temporal patterns from a dataset of 1030 historical concrete samples. The extracted features were enhanced using an eXtreme Gradient Boosting (XGBoost) meta-model to improve generalizability and noise resistance. Then, a Dueling Double Deep Q-Network (Dueling DDQN) agent was used to iteratively identify optimal mix ratios that maximize the predicted compressive strength. The proposed framework outperformed ten benchmark models, achieving an MAE of 2.97, RMSE of 4.08, and R² of 0.94. Feature attribution methods—including SHapley Additive exPlanations (SHAP), Elasticity-Based Feature Importance (EFI), and Permutation Feature Importance (PFI)—highlighted the dominant influence of cement content and curing age, as well as revealing non-intuitive effects such as the compensatory role of superplasticizers in low-water mixtures. These findings demonstrate the potential of the proposed approach to support intelligent concrete mix design and real-time optimization in smart construction environments. Full article

pH is a critical parameter requiring precise monitoring across scientific, industrial, and biological domains. Fluorescent pH probes offer a powerful alternative to traditional methods (e.g., electrodes, indicators), overcoming limitations in miniaturization, long-term stability, and electromagnetic interference. By utilizing photophysical mechanisms—including intramolecular charge transfer (ICT), photoinduced electron transfer (PET), and fluorescence resonance energy transfer (FRET)—these probes enable high-sensitivity, reusable, and biocompatible sensing. This review systematically details recent advances, categorizing probes by operational pH range: strongly acidic (0–3), weakly acidic (3–7), strongly alkaline (>12), weakly alkaline (7–11), near-neutral (6–8), and wide-dynamic range. Innovations such as ratiometric detection, organelle-specific targeting (lysosomes, mitochondria), smartphone colorimetry, and dual-analyte response (e.g., pH + Al³⁺/CN⁻) are highlighted. Applications span real-time cellular imaging (HeLa cells, zebrafish, mice), food quality assessment, environmental monitoring, and industrial diagnostics (e.g., concrete pH). Persistent challenges include extreme-pH sensing (notably alkalinity), photobleaching, dye leakage, and environmental resilience. Future research should prioritize broadening functional pH ranges, enhancing probe stability, and developing wide-range sensing strategies to advance deployment in commercial and industrial online monitoring platforms. Full article

To address the inefficiency of traditional concrete crack detection methods and the heavy reliance of supervised learning on extensive labeled data, in this study, an intelligent assessment method of concrete damage based on pseudo-label semi-supervised learning and fractal geometry theory is proposed to solve two core tasks: one is binary classification of pixel-level cracks, and the other is multi-category assessment of damage state based on crack morphology. Using three-channel RGB images as input, a dual-path collaborative training framework based on U-Net encoder–decoder architecture is constructed, and a binary segmentation mask of the same size is output to achieve the accurate segmentation of cracks at the pixel level. By constructing a dual-path collaborative training framework and employing a dynamic pseudo-label refinement mechanism, the model achieves an F1-score of 0.883 using only 50% labeled data—a mere 1.3% decrease compared to the fully supervised benchmark DeepCrack (F1 = 0.896)—while reducing manual annotation costs by over 60%. Furthermore, a quantitative correlation model between crack fractal characteristics and structural damage severity is established by combining a U-Net segmentation network with the differential box-counting algorithm. The experimental results demonstrate that under a cyclic loading of 147.6–221.4 kN, the fractal dimension monotonically increases from 1.073 (moderate damage) to 1.189 (failure), with 100% accuracy in damage state identification, closely aligning with the degradation trend of macroscopic mechanical properties. In complex crack scenarios, the model attains a recall rate (Re = 0.882), surpassing U-Net by 13.9%, with significantly enhanced edge reconstruction precision. Compared with the mainstream models, this method effectively alleviates the problem of data annotation dependence through a semi-supervised strategy while maintaining high accuracy. It provides an efficient structural health monitoring solution for engineering practice, which is of great value to promote the application of intelligent detection technology in infrastructure operation and maintenance. Full article

Timely and accurate identification of surface damage in hydraulic structures is essential for maintaining structural integrity and ensuring operational safety. Traditional manual inspections are time-consuming, labor-intensive, and prone to subjectivity, especially for large-scale or inaccessible infrastructure. Leveraging advancements in aerial imaging, unmanned aerial vehicles (UAVs) enable efficient acquisition of high-resolution visual data across expansive hydraulic environments. However, existing deep learning (DL) models often lack architectural adaptations for the visual complexities of UAV imagery, including low-texture contrast, noise interference, and irregular crack patterns. To address these challenges, this study proposes a lightweight, robust, and high-precision segmentation framework, called LFPA-EAM-Fast-SCNN, specifically designed for pixel-level damage detection in UAV-captured images of hydraulic concrete surfaces. The developed DL-based model integrates an enhanced Fast-SCNN backbone for efficient feature extraction, a Lightweight Feature Pyramid Attention (LFPA) module for multi-scale context enhancement, and an Edge Attention Module (EAM) for refined boundary localization. The experimental results on a custom UAV-based dataset show that the proposed damage detection method achieves superior performance, with a precision of 0.949, a recall of 0.892, an F1 score of 0.906, and an IoU of 87.92%, outperforming U-Net, Attention U-Net, SegNet, DeepLab v3+, I-ST-UNet, and SegFormer. Additionally, it reaches a real-time inference speed of 56.31 FPS, significantly surpassing other models. The experimental results demonstrate the proposed framework’s strong generalization capability and robustness under varying noise levels and damage scenarios, underscoring its suitability for scalable, automated surface damage assessment in UAV-based remote sensing of civil infrastructure. Full article

The utilization of recycled concrete aggregate presents an effective solution for construction waste mitigation. However, concrete service in sulfate environments leads to sulfate ion retention in recycled aggregates, substantially impairing their quality and requiring modification approaches. A critical question remains whether traditional recycled aggregate modification techniques can effectively enhance the performance of these sulfate-containing recycled aggregates (SRA). Cement paste wrapping in various proportions was used in this investigation to enhance SRA. The performance of both SRA and modified aggregates was systematically assessed through measurements of apparent density, water absorption, crushing value, and microhardness. Microstructural analysis of the cement wrapping modification mechanism was conducted by scanning electron microscopy coupled with mercury intrusion porosimetry. Results revealed that internal sulfate addition decreased the crushing value and increased the water absorption of recycled aggregates, primarily due to micro-cracks formed by expansion. Additionally, the pores were occupied by erosion products, leading to a slight increase in the apparent density of aggregates. The performance of SRA was effectively enhanced by cement paste wrapping at a 0.6 water–binder ratio, whereas it was negatively impacted by a ratio of 1.0. The modifying effect became even more effective when 15% fly ash was added to the wrapping paste. Scanning electron microscopy observations revealed that the interface of SRA was predominantly composed of gypsum crystals. Cement paste wrapping greatly enhanced the original interface structure, despite a new dense interface formed in the modified aggregates. Full article

In the context of the growing need to reduce greenhouse gas emissions and to develop sustainable solutions for the construction industry, foamed geopolymers represent a promising alternative to traditional binders and insulation materials. This study investigates the thermal properties of novel low-emission, insulating geopolymer binders made from fly ash with diatomite, chalcedonite, and wood wool aiming to assess their potential for use in thermal insulation systems in energy-efficient buildings. The stability of the foamed geopolymer structure is also assessed. Measurements of thermal conductivity, specific heat, microstructure, density, and compressive strength are presented. The findings indicate that the selected geopolymer formulations exhibit low thermal conductivity, high heat capacity and low density, making them competitive with conventional insulation materials—mainly load-bearing ones such as aerated concrete and wood wool insulation boards. Additionally, incorporating waste-derived materials reduces the production carbon footprint. The best results are represented by the composite incorporating all three additives (diatomite, chalcedonite, and wood wool), which achieved the lowest thermal conductivity (0.10154 W/m·K), relatively low density (415 kg/m³), and high specific heat (1.529 kJ/kg·K). Full article