In civil engineering, carbon fibre-reinforced polymer (CFRP) composites have emerged as a promising alternative to conventional materials. The article provides a comprehensive overview of the application of CFRP composites in various building structural elements and their characteristics and properties, such as their fatigue and corrosion resistance, stiffness and high strength, and incorporation of temperature factors. The advantages and disadvantages of CFRP composites and the current trends and prospects for CFRP composites in the construction sector are discussed. In addition, the article compares various studies on CFRP composites to shed light on their performance and potential limitations. This paper aims to provide useful information to researchers and practitioners interested in using CFRP composites in civil engineering applications. In addition, the article discusses emerging materials in CFRP, such as nanostructured carbon fibres, hybrid fibre reinforcement, and self-sensing CFRP. Additionally, the paper outlines how CFRP composites promote sustainability by increasing structural durability and longevity.

This study investigated operational and structural health monitoring (SHM) as well as damage evaluations for building structures. The study involved damage detection and the assessment of buildings by placing sensors and by assuming weak areas, and considered situations of assessment and self-monitoring. From this perspective, advanced sensor technology and data acquisition techniques can systematically monitor a building in real time. Furthermore, the structure’s response and behavior were observed and recorded to predict the damage to the building. In this paper, we discuss the real-time monitoring and response of buildings, which includes both static and dynamic analyses along with numerical simulation studies such as finite element analysis (FEA), and recommendations for the future research and development of SHM are made.

Structural health monitoring (SHM) has evolved into an indispensable component for ensuring the safety, durability, and life-cycle efficiency of civil infrastructure. Over the past five years, significant technological advancements have been made in innovative sensing systems, facilitating real-time assessment of structural performance and the early detection of deterioration. This comprehensive review presents recent developments in smart sensor-based SHM, with particular emphasis on the convergence of the Internet of Things (IoT), artificial intelligence (AI), and digital twin (DT) frameworks. Our review critically examines advances in fiber-optic, piezoelectric, MEMS-based, vision-based, acoustic, and environmental sensors, as well as emerging multi-sensor fusion architectures. In addition, bibliometric insights highlight the significant rise in global research activity and influential thematic clusters in SHM between 2020 and 2025. The discussion underscores how AI-integrated data analytics, IoT-enabled wireless networks, and DT-driven virtual replicas enable intelligent, autonomous, and predictive monitoring of bridges, buildings, tunnels, and other large-scale civil infrastructure. Field deployments and case studies are analyzed to bridge the gap between laboratory-scale demonstrations and real-world implementation. Finally, key scientific and practical challenges—including the durability of embedded sensors, the interoperability of heterogeneous data, cybersecurity in connected systems, and the explainability of AI models—are outlined to guide future research. Overall, this review positions contemporary SHM as a transition from traditional damage detection to comprehensive life-cycle management of infrastructure through self-diagnosing, data-centric, and sustainability-driven monitoring ecosystems.

Remediated landfills require long-term monitoring due to ongoing processes such as settlement, water infiltration, leachate migration, and biogas emissions, which may lead to cover degradation and environmental risks. Traditional ground-based inspections are often time-consuming, costly, and limited in terms of spatial coverage. This study presents the application of Unmanned Aerial Vehicle (UAV)-based remote sensing methods for the structural assessment of a remediated landfill. A multi-sensor approach was employed, combining geometric data (Light Detection and Ranging (LiDAR) and photogrammetry), hydrological modeling (surface water accumulation and runoff), multispectral imaging, and thermal data. The results showed that subsidence-induced depressions modified surface drainage, leading to water accumulation, concentrated runoff, and vegetation stress. Multispectral imaging successfully identified zones of persistent instability, while UAV thermal imaging detected a distinct leachate-related anomaly that was not visible in red–green–blue (RGB) or multispectral data. By integrating geometric, hydrological, spectral, and thermal information, this paper demonstrates practical applications of remote sensing data in detecting cover degradation on remediated landfills. Compared to traditional methods, UAV-based monitoring is a low-cost and repeatable approach that can cover large areas with high spatial and temporal resolution. The proposed approach provides an effective tool for post-closure landfill management and can be applied to other engineered earth structures.

Boundary effect behavior understood as near-boundary suppression of boundary fluctuation loads is described in various ways depending on the mathematical representation of solutions and the type of the center. In the case of periodic composites, the homogenization method is decisive here. In the framework of the Tolerance Averaging Approach, developed by prof. Cz. Woźniak leading to an approximate model of phenomena related to periodic composites this effect is described by a homogeneous part of differential equation for fluctuation amplitudes and usually this approximate description of the boundary effect behavior is restricted to a single fluctuation. In this paper, contrary to the previous elaborations, the boundary effect is developed in the variant of the tolerance thermal conductivity model in which the temperature field is represented by the Fourier expansions composed by an average temperature with infinite number of Fourier terms imposed on the average temperature as tolerance fluctuation suppressed in the framework of the boundary effect.

The well-known parabolic Heat Transfer Equation is a simplest recognized description of phenomena related to the heat conductivity in solids with microstructure. However, it is a tool difficult to use due to the discontinuity of coefficients appearing here. The purpose of the paper is to reformulate this equation to the form that allows to represent solutions in the form of Fourier’s expansions. This equivalent re-formulation has the form of infinite number of equations with Fourier coefficients in expansion of the temperature field as the basic unknowns. The first term in Fourier representation, being an average temperature field, should satisfy the well-known parabolic heat conduction equation with Fourier coefficients as fields controlling average temperature behavior. The proposed description takes into account changes of the composite periodicity accompanying changes in the variable perpendicular to the surfaces separating components, concerning FGM - type materials and can be treated as the asymptotic version of Heat Transfer Equation obtained as a result of a certain limit passage where the cell size remains unchanged.