Search Results (797)

Coal spontaneous combustion causes both human casualties and environmental pollution. Owing to special flow behaviors, foam materials used in fire-fighting technology can effectively bring water and solid non-combustible substances into the fire-fighting area, greatly preventing spontaneous combustion. This paper systematically elucidates three foam materials, three-phase foam, gel foam and curing foam, and analyzes their physical and chemical inhibition mechanisms on coal spontaneous combustion. In particular, the preparation, performance and latest chemical modification methods of the foam materials are summarized in detail. It is found that foam materials with environmental friendliness, economy and excellent anti-fire performance need to be consistently explored. The primary application areas for cement-based foamed materials remain the building materials and civil engineering industries, and their modification should be studied accordingly based on the specific application context. Furthermore, a new component of foam materials, coal gasification slag (a solid waste), is proposed. In addition, the seepage properties of fire-fighting foam in porous media should be fully studied to accurately grasp the dispersion of foam materials in mine goafs. This review provides new insights and guidance for the development of fire-fighting foam materials. Full article

Assessing seismic vulnerability is a critical step in evaluating the resilience of existing buildings, and fragility curves are widely used to quantify the probability of damage under varying levels of seismic intensity. However, traditional methods for generating these curves often rely on generalized assumptions that may not accurately capture the seismic behavior of diverse building types within a region. This limitation is particularly evident for non-engineered masonry buildings, which typically lack standardized designs. Their irregular and informal construction makes them difficult to assess using conventional approaches. Transformer-based models, a type of machine learning (ML) technique, offer a promising alternative. These models can identify patterns and relationships in available data, making them well suited for developing seismic fragility curves with improved efficiency and accuracy. While transformers are relatively new to civil engineering, their application to seismic fragility assessment has been largely unexplored. This study presents a pioneering effort to apply transformer models for deriving fragility curves for non-engineered masonry buildings. A comprehensive dataset of 646 masonry buildings observed in Malawi is used to train the models. The transformers are trained to predict the probability of four damage states: Light Damage, Severe Damage, Near Collapse, and Collapse based on Peak Ground Acceleration (PGA). The performance of the transformer-based approach is compared with other ML methods, demonstrating its strong potential for more efficient and accurate seismic fragility assessment. Future work could adopt the proposed methodology and extend the approach by incorporating larger datasets, additional regional contexts, and alternative ML techniques to further enhance predictive performance. Full article

Non-destructive testing methods are essential in civil engineering applications, such as evaluating the compressive strength of masonry. This paper presents a fuzzy signature model based on non-destructive in situ measurements and visual inspection, applying weighted geometric mean aggregation in the signature vertices determined by experts. The weights of the aggregation terms were optimized using the Monte Carlo method, genetic algorithm and particle swarm algorithm to ensure that the evaluation by the signature aligned with the results of destructive tests performed on existing masonry. The results of the methods were compared for single and multiple assembled masonry structures using the same objective function. All three methods provided relatively high confidence in finding the extreme values of the objective function on a generated dataset, which accounted for the correlations observed in actual measurements. Accordingly, validation based on real data yielded the expected results, thus demonstrating the model’s suitability for practical application. This study assessed the inherent, analyzing whether symmetric or asymmetric weight distributions affected evaluation consistency. While symmetric weighting simplified aggregation, asymmetry allowed local structural irregularities to be highlighted. In addition, the cost analysis of the optimization methods revealed a disparity in computational cost increments between the two approaches. The presented work outlines the advantages of the different methods and their applicability to structural assessment. Full article

As a key form of deep foundation in civil engineering, the concrete pouring quality of cast-in-place piles directly determines the integrity and long-term bearing performance of the pile body. Accurate monitoring of the pouring interface is critical to preventing defects such as mud inclusion and pile breakage. To address the limitations of existing monitoring methods for concrete pouring interfaces, this paper proposes a full-pulse acoustic monitoring method for the concrete pouring interface of cast-in-place piles. Firstly, by constructing a hardware system platform consisting of “multi-level in-borehole sound sources + interface acoustic wave sensors + orifice full-pulse receivers + ground processors”, differential capture of signals propagating at different depths is achieved through multi-frequency excitation. Subsequently, a waveform data processing method is proposed to realize denoising, enhancement, and frequency discrimination of different signals, and a target feature recognition model that integrates cross-correlation functions and signal similarity analysis is established. Finally, by leveraging the differential characteristics of measurement signals at different depths, a near-field measurement mode and a far-field measurement mode are developed, thereby establishing a calculation model for the elevation position of the pouring interface under different scenarios. Meanwhile, the feasibility of the proposed method is verified through practical engineering cases. The results indicate that the proposed full pulse acoustic monitoring method can achieve non-destructive, real-time, and high-precision monitoring of the pouring interface, providing an effective technical approach for quality control in pile foundation construction and exhibiting broad application prospects. Full article

This study explores the use of recycled polypropylene fibers as sustainable reinforcement materials for stabilizing expansive clayey soils. Laboratory testing revealed that the optimal fiber combinations enhanced ductility, post-peak behavior, strength, and swelling properties. A cradle-to-grave life cycle assessment (LCA) also showed the environmental advantages of fiber-reinforced soil over traditional methods. The results suggest that incorporating recycled fibers into soil stabilization techniques can improve performance and promote sustainability in civil engineering applications. Full article

This study focuses on the development, characterization, and numerical simulation of novel composite materials based on natural vegetable fibers for applications in civil engineering. Three different bio-based composites were formulated using Alfa plant fibers, wood waste, and an equal mixture of both (50% Alfa, 50% wood), with polyvinyl acetate (PVAc), a non-polluting polymer matrix, as the binder. The performance of these composites is strongly influenced by the fiber morphology, structural characteristics, and the nature of the matrix. Thus, understanding and optimizing these parameters is crucial for tailoring materials to meet specific design requirements. The experimental approach began with the morphological and structural characterization of the raw and treated fibers, followed by the evaluation of the thermal a properties of the resulting composites. Furthermore, thermal conductivity simulations were performed using COMSOL Multiphysics to validate the experimental results and gain deeper insights into heat transfer behavior within the composites. A comparative analysis with conventional synthetic insulation materials revealed that the developed bio-composites offer competitive thermal performance while being more environmentally sustainable. These findings highlight the potential of Alfa and wood waste fibers as effective, eco-friendly alternatives for thermal insulation in building applications. Full article

This study examines advanced cementitious composites incorporating Multi-Walled Carbon Nanotubes (MWCNTs), combining experimental investigations and analytical modeling for enhanced Structural Health Monitoring (SHM) applications. The experimental phase assessed the electrical properties of specimens with varying MWCNT contents, identifying a percolation zone between 0.05 wt% and 0.5 wt%. A dispersion protocol using ultrasonic agitation and a surfactant ensured the uniform distribution of CNTs. Furthermore, a novel micromechanical model, based on established polymer matrix approaches, was used to predict electrical conductivity behavior, accounting for nanotube geometry, concentration, waviness, and tunneling effects. Model predictions confirmed its effectiveness in analyzing structure–property relationships in CNT-based cementitious materials. Full article

Conventional characterization of ultrasonic testing (UT) transducers primarily focuses on determining centre frequency and usable bandwidth. However, the relative amplitude distribution across different frequency components—particularly in low-frequency transducers used for civil engineering applications—remains largely overlooked. This paper introduces a comprehensive methodology to assess the influence of transducer coupling and specimen geometry on ultrasonic pulse velocity signals. The novel approach combines high-frequency laser Doppler vibrometry, real-time photoelastic imaging, and computer simulations using commercial semi-analytical wave-propagation software. The methodology is applied to the characterization of a 250 kHz UT transducer, with particular emphasis on how coupling with a solid test medium alters its frequency response. A glass specimen with an acoustic impedance comparable to that of concrete is used to simulate practical testing conditions. Vibration patterns recorded at the distal end of the specimen are analysed through computer simulations and validated experimentally using a novel photoelastic system capable of capturing wave–specimen interactions at ultrasonic frequencies in real time. The findings offer valuable insights into frequency-dependent signal behaviour and transducer–medium interactions, providing practical guidance for the design and optimization of UT inspections in concrete and other highly attenuative materials commonly encountered in civil engineering. Full article

Photogrammetry offers a non-contact and efficient alternative for monitoring structural deformation and is particularly suited to large or complex surfaces such as masonry walls. This study proposes a spatio-temporal photogrammetric refinement framework that enhances the accuracy of three-dimensional (3D) deformation and strain analysis by integrating advanced filtering techniques into markerless image-based measurement workflows. A hybrid methodology was developed using natural image features extracted using the Speeded-Up Robust Features algorithm and refined through a three-stage filtering process: median absolute deviation filtering, Gaussian smoothing, and representative point selection. These techniques significantly mitigated the influence of noise and outliers on deformation and strain analysis. Comparative experiments using both manually placed targets and automatically extracted feature points on a full-scale masonry wall under destructive loading demonstrated that the proposed spatio-temporal filtering effectively improves the consistency of displacement and strain fields, achieving results comparable to traditional marker-based methods. Validation against laser rangefinder measurements confirmed sub-millimeter accuracy in displacement estimates. Additionally, strain analysis based on filtered data captured crack evolution patterns and spatial deformation behavior. Therefore, integrating photogrammetric 3D point tracking with spatio-temporal refinement provides a practical, accurate, and scalable approach to monitor structural deformation in civil engineering applications. Full article

Wood is a renewable, carbon-sequestering, and structurally versatile material that has supported human civilization for millennia and continues to play a central role in advancing sustainable development. Although its low density, high specific strength, and esthetic appeal make it highly attractive, its intrinsic flammability presents significant challenges for safety-critical uses. This review offers a comprehensive analysis that uniquely integrates three key domains, covering advanced processing technologies, flame-retardant functionalization strategies, and multifunctional applications. Clear connections are drawn between processing approaches such as delignification, densification, and nanocellulose extraction and their substantial influence on improving flame-retardant performance. The review systematically explores how these engineered wood substrates enable more effective fire-resistant systems, including eco-friendly impregnation methods, surface engineering techniques, and bio-based hybrid systems. It further illustrates how combining processing and functionalization strategies allows for multifunctional applications in architecture, transportation, electronics, and energy devices where safety, durability, and sustainability are essential. Future research directions are identified with a focus on creating scalable, cost-effective, and environmentally compatible wood-based materials, positioning engineered wood as a next-generation high-performance material that successfully balances structural functionality, fire safety, and multifunctionality. Full article

Wet–dry cycles are a key factor aggravating the durability degradation of foam concrete. To address this issue, this study prepared bentonite slurry–steel slag foam concrete (with steel slag and cement as main raw materials, and bentonite slurry as admixture) using the physical foaming method. Based on 7-day unconfined compressive strength tests with different mix proportions, the optimal mix proportion was determined as follows: mass ratio of bentonite to water 1:15, steel slag content 10%, and mass fraction of bentonite slurry 5%. Based on this optimal mix proportion, dry–wet cycle tests were carried out in both water and salt solution environments to systematically analyze the improvement effect of steel slag and bentonite slurry on the durability of foam concrete. The results show the following: steel slag can act as fine aggregate to play a skeleton role; after fully mixing with cement paste, it wraps the outer wall of foam, which not only reduces foam breakage but also inhibits the formation of large pores inside the specimen; bentonite slurry can densify the interface transition zone, improve the toughness of foam concrete, and inhibit the initiation and propagation of matrix cracks during the dry–wet cycle process; the composite addition of the two can significantly enhance the water erosion resistance and salt solution erosion resistance of foam concrete. The dry–wet cycle in the salt solution environment causes more severe erosion damage to foam concrete. The main reason is that, after chloride ions invade the cement matrix, they erode hydration products and generate expansive substances, thereby aggravating the matrix damage. Scanning Electron Microscopy (SEM) analysis shows that, whether in water environment or salt solution environment, the fractal dimension of foam concrete decreased slightly with an increasing number of wet–dry cycle times. Based on fractal theory, this study established a compressive strength–porosity prediction model and a dense concrete compressive strength–dry–wet cycle times prediction model, and both models were validated against experimental data from other researchers. The research results can provide technical support for the development of durable foam concrete in harsh environments and the high-value utilization of steel slag solid waste, and are applicable to civil engineering lightweight porous material application scenarios requiring resistance to dry–wet cycle erosion, such as wall bodies and subgrade filling. Full article

The integration of artificial intelligence (AI) and machine learning (ML) has revolutionised civil engineering, enhancing predictive accuracy, decision-making, and sustainability across domains such as structural health monitoring, geotechnical analysis, transportation systems, water management, and sustainable construction. This paper presents a detailed review of peer-reviewed publications from the past decade, employing bibliometric mapping and critical evaluation to analyse methodological advances, practical applications, and limitations. A novel taxonomy is introduced, classifying AI/ML approaches by civil engineering domain, learning paradigm, and adoption maturity to guide future development. Key applications include pavement condition assessment, slope stability prediction, traffic flow forecasting, smart water management, and flood forecasting, leveraging techniques such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Support Vector Machines (SVMs), and hybrid physics-informed neural networks (PINNs). The review highlights challenges, including limited high-quality datasets, absence of AI provisions in design codes, integration barriers with IoT-based infrastructure, and computational complexity. While explainable AI tools like SHAP and LIME improve interpretability, their practical feasibility in safety-critical contexts remains constrained. Ethical considerations, including bias in training datasets and regulatory compliance, are also addressed. Promising directions include federated learning for data privacy, transfer learning for data-scarce regions, digital twins, and adherence to FAIR data principles. This study underscores AI as a complementary tool, not a replacement, for traditional methods, fostering a data-driven, resilient, and sustainable built environment through interdisciplinary collaboration and transparent, explainable systems. Full article

Thermal imaging is increasingly applied in remote sensing to identify material degradation, monitor structural integrity, and support energy diagnostics. However, its adoption is limited by the low spatial resolution of thermal sensors compared to RGB cameras. This study proposes a modular pipeline to generate thermally enriched 3D point clouds by fusing RGB and thermal imagery acquired simultaneously with a dual-sensor unmanned aerial vehicle system. The methodology includes geometric calibration of both cameras, image undistortion, cross-spectral feature matching, and projection of radiometric data onto the photogrammetric model through a computed homography. Thermal values are extracted using a custom parser and assigned to 3D points based on visibility masks and interpolation strategies. Calibration achieved 81.8% chessboard detection, yielding subpixel reprojection errors. Among twelve evaluated algorithms, LightGlue retained 99% of its matches and delivered a reprojection accuracy of 18.2% at 1 px, 65.1% at 3 px and 79% at 5 px. A case study on photovoltaic panels demonstrates the method’s capability to map thermal patterns with low temperature deviation from ground-truth data. Developed entirely in Python, the workflow integrates into Agisoft Metashape or other software. The proposed approach enables cost-effective, high-resolution thermal mapping with applications in civil engineering, cultural heritage conservation, and environmental monitoring applications. Full article

The growing demand for lightweight and reliable structures across aerospace, automotive, marine, and civil engineering has driven significant advances in polymer adhesive technology. These materials serve dual roles, functioning as matrices in composites and as structural bonding agents, where they must balance strength, toughness, durability, and sometimes sustainability. Recent review efforts have greatly enriched understanding, yet most approach the topic from specialized angles—whether emphasizing nanoscale toughening, multifunctional formulations, sustainable alternatives, or microscopic failure processes in bonded joints. While such perspectives provide valuable insights, they often remain fragmented, leaving open questions about how nanoscale mechanisms translate into macroscopic reliability, how durability evolves under realistic service conditions, and how mechanical responses interact across different loading modes. To address this, the present review consolidates knowledge on the performance of polymer adhesives under tension, shear, fracture, fatigue, creep, and impact. By integrating experimental findings with computational modeling and emerging data-driven approaches, it situates localized mechanisms within a broader structure–performance framework. This unified perspective not only highlights persistent gaps—such as predictive modeling of complex failure, scalability of nanomodified systems, and long-term durability under coupled environments—but also outlines strategies for developing next-generation adhesives capable of delivering reliable, high-performance bonding solutions for demanding applications. Full article

Incorporating Nitinol (NiTi) shape memory alloy (SMA) into concrete structures has gained significant attention in recent years due to its ability to enhance the properties of concrete. This review paper illustrates the history of NiTi SMA and its use in various civil engineering structural applications. A detailed analysis of the existing literature and case studies offers perspectives on the possible applications, benefits, and prospects of utilizing NiTi SMA to reinforce and strengthen elements in concrete structures. The study examined publications on the internal usage of NiTi SMA in concrete and cement-based matrices as an embedded element, including fibers, bars, cables, wires, powder, and strands. In addition, superelastic and shape memory forms of NiTi were considered. It was concluded that the superelasticity of NiTi aided in energy dissipation from impact or seismic events. It also improved the re-centering performance and deformation capacity and reduced residual stresses, strains, and cracks. Conversely, the SMA effect of NiTi helped bridge cracks, recover the original shape, and induced prestressing forces under thermal activation. Full article