CivilEng, Volume 7, Issue 3 (September 2026) – 11 articles

Coplanar capacitance detection exhibits remarkable advantages in the detection of road texture in asphalt layers, including high sensitivity and minimal environmental constraints. However, the inherent performance contradiction between signal strength and penetration depth of traditional interdigitated coplanar capacitance sensors (ICCSs) has restricted their widespread application in road texture detection. To address this issue, a hybrid approach combining response surface methodology (RSM) and non-dominated sorting genetic algorithm II (NSGA-II) is developed to optimize the structural parameters that influence the signal strength and penetration depth of a novel ICCS. Initially, a central-composite design (CCD) based on RSM is employed to establish statistical models for the two key sensing performances of ICCSs, namely signal strength and penetration depth. Subsequently, Analysis of Variance (ANOVA) and three-dimensional (3D) response surface plots are utilized to investigate the significant effects of various structural parameters (electrode length, width, and inter-finger gap) on the two sensing performances. Furthermore, NSGA-II is applied to search for global optimal solutions using the established statistical models, thereby achieving multi-performance optimization of the ICCS. Finally, the fabricated ICCS is used to detect the surface texture of asphalt mixture specimens with different gradations, and the results are compared with those obtained by laser point cloud detection. The results indicate that both statistical models are highly significant, with the coefficient of determination (R-squared) exceeding 0.95. All individual structural parameters have a significant impact on the two sensing performances. Based on the optimization by the RSM-NSGA-II hybrid method, the predicted optimal parameters are verified, showing a relative error of less than 5% from the simulation results. Additionally, the detection results of the ICCS are consistent with the laser point-cloud data, demonstrating its feasibility for pavement texture detection. Full article

The global construction industry is increasingly seeking sustainable alternatives to conventional structural materials to reduce environmental impacts and support circular economy goals. This review examines bio-based construction materials from a structural engineering perspective, focusing on the transition from intrinsic material properties to member-level behavior and system-scale applications. A combined bibliometric and “From Material to Member” framework is used to connect microstructural characteristics with structural performance across scales. The review covers microbial self-healing concretes, engineered bamboo, plant-aggregate concretes such as hempcrete and rice-husk composites, lignin-based polymers and resins, and mycelium composites, with emphasis on materials and systems relevant to structural and member-scale applications. Bio-based materials developed primarily for asphalt and pavement applications are outside the scope of this review. Mechanical, thermal, durability, and environmental performance are evaluated alongside emerging multi-scale modeling approaches and hybrid structural systems. The findings show that bio-concretes can provide autonomous crack repair, engineered bamboo offers high strength-to-weight efficiency, and lignin-based polymers enable renewable composite matrices with adaptable properties. However, challenges remain regarding connection design, moisture sensitivity, long-term durability, standardization, and the transfer of laboratory findings to structural-scale reliability. Life-cycle assessment studies indicate substantial embodied carbon reduction potential, although outcomes depend on processing methods, service-life assumptions, and end-of-life scenarios. Overall, performance-based design, durability assessment, standardized testing, and dynamic life-cycle approaches are essential for broader structural implementation. Full article

Climate change has been associated with an increasing occurrence of extreme wind phenomena, including hurricanes, tornadoes, and downbursts, with noticeable rises in both their frequency and severity. These events have heightened concerns regarding their devastating impacts on structures, infrastructure, and economies. To provide a comprehensive and reliable review, a large number of previous studies and scientific references were collected and carefully screened. The selection process focused primarily on studies directly related to structural engineering applications, wind-induced structural responses, tornado and hurricane loading mechanisms, and simulation techniques used in wind engineering research. References unrelated to structural behavior, engineering analysis, or wind-resistant design were excluded to maintain the technical relevance and consistency of the review. This review explores parameters influencing wind loads, focusing on tornado flow field characteristics such as swirl ratio, ground roughness, translation speed, and topography. It also examines structural properties such as geometry, material, orientation, and proximity to the tornado path that govern a building’s ability to withstand wind-induced forces. The review evaluates experimental techniques, including wind tunnel tests, tornado simulators, and numerical simulations using Computational Fluid Dynamics (CFD) to improve understanding and resilience. These approaches are compared for effectiveness in replicating real-world scenarios and enhancing predictive accuracy. Furthermore, key engineering standards, such as ASCE 7-22 and FEMA guidelines, are highlighted, showing their role in improving structural design, identifying gaps in research, and advocating for future studies. It emphasizes integrating emerging computational technologies, including machine learning, to enhance structural design efficiency and disaster response performance. This review aims to guide researchers and engineers toward developing resilient structures capable of mitigating the impacts of extreme wind events. Full article

Understanding the non-linear dynamic interaction between tracks and bridge structures is essential for maintaining the safety of continuous rigid-frame bridges. To accurately capture these beam–rail interactions, this study develops a detailed 3D finite element model based on the principle of stationary total potential energy. This framework fully integrates the track, main girders, and piers into a single system. Based on a 4 × 40 m continuous rigid-frame viaduct in an urban transit network, the numerical model accounts for the bilinear mechanical behavior of the rail fasteners. The study compares the transmission of longitudinal forces along the continuously welded rail (CWR) under two fastening layouts. The baseline case uses uniform constant-resistance fasteners across the entire bridge, while the optimized scheme places small-resistance fasteners at the final 20% of each structural segment. Analysis shows that placing low-resistance fasteners in the high-displacement areas near the girder ends creates an effective longitudinal “release zone.” This design effectively interrupts the buildup of longitudinal forces, resulting in a much smoother force distribution along the rails. Quantitative results indicate that this optimized fastener layout has only a minor effect on structural deflection and braking-induced rail stresses, keeping deviations below 11%. At the same time, it significantly reduces the peak expansion stress and broken-rail stress by 43.4% and 22.2%, respectively. By shifting the stress regulation philosophy from “rigid resistance” to dynamic “force channeling,” these findings demonstrate that local low-resistance fastener deployment improves the overall mechanical compatibility of the track–bridge infrastructure. Ultimately, this work offers a solid theoretical basis for the design and maintenance of CWR systems on long-span rigid-frame bridges. Full article

This article thoroughly documents the focus, long-term monitoring, and survey of the condition of a unique early Gothic wooden ceiling from the 13th century in Zvíkov Castle in the Czech Republic. Through long-term monitoring of climatic parameters, wood moisture, and the movement of ceiling beams, this paper described how wood constantly works, that is, swells and shrinks during a single annual cycle. At the interface of the ceiling beams and the masonry, the ambient temperature ranged from 2.75 °C to 31.75 °C, and the relative humidity of the air from 38 to 93%. The condition of the walled-in beam ends was assessed by resistance drilling and endoscopy. Out of a total of 16 beam ends, 7 ends are completely degraded by brown rot fungus. The condition of the ceiling structure is not good, even in the case of the boards covering the ceiling. Here, the sapwood parts are additionally damaged by the beetle Anobium punctatum which, in combination with rot, has broken down the wood into crumbling matter. High static load due to a massive embankment 35–50 cm thick, two layers of fired paving, and occasional loading causes the wood to deteriorate over time and requires urgent stabilization. The final section reflects on the approach to sustainably securing the uniquely preserved structure. Finding the optimal solution represents a challenging decision-making task between a conservation intervention of external support with minimal intervention in the existing structure and the structural restoration of individual elements, requiring disassembly and reassembly of the structure. Full article

The tuned mass damper (TMD) exhibits good performance in suppressing wind-induced vibrations of high-rise structures. However, a single TMD has a limited control bandwidth and poor robustness. The multiple-pendulum tuned mass damper (MPTMD) offers advantages, such as a wider control bandwidth, stronger robustness, and a simple structural configuration, while its working frequency can be easily adjusted by varying the pendulum lengths. With two optimization objectives, namely displacement and acceleration, this study derives the displacement and acceleration dynamic amplification factors of the primary structure equipped with the MPTMD under external excitation and examines the interrelationships among the optimal parameters and their underlying mechanisms. The accuracy of the proposed optimization method and the effectiveness of the MPTMD are validated by fitting the theoretically derived optimal parameter curves with results from numerical simulations. Finally, the control performance of MPTMD and TMD is compared through a numerical example subjected to realistic wind load excitations, verifying the control effectiveness of MPTMD. Nevertheless, several limitations should be acknowledged. The present optimization is based on a single-degree-of-freedom (SDOF) primary structure and targets only the first translational mode; the effects of higher modes and multi-degree-of-freedom (MDOF) coupling are not considered. Additionally, the wind load is represented by a synthetic time history with a fixed return period, and uncertainties in real wind fields are not fully addressed. Future work should extend the proposed method to multi-modal control, nonlinear behavior, and experimental validation. Full article

The aging of social infrastructure, intensively constructed during periods of rapid economic growth, is a pressing challenge facing modern society. Conventional infrastructure asset management has disproportionately emphasized a “managerial financial perspective,” aiming to maintain physical functions within limited budgets. However, the malfunction of road appurtenances such as tunnel lighting facilities induces severe traffic accidents and chronic congestion, resulting in public health risks for users (physical trauma, psychological stress, and the deterioration of Disability-Adjusted Life Years: DALYs) as well as massive socio-economic losses. The primary novelty of this study lies in bridging the gap between stochastic engineering deterioration models—specifically, discrete-time Markov chain models predicting physical degradation—and socio-economic stakeholder value chains. This study constructs a “Social Life Cycle Cost (LCC) Optimization Model” that directly incorporates these social losses and stakeholder risk disparities into the evaluation function, addressing the limitations of conventional financial-centric LCC models. By conducting robust uncertainty and global sensitivity analyses via large-scale Markov Chain Monte Carlo simulations (number of trials $N={10}^5$), we reveal that a corrective maintenance strategy inheres a critical “fat-tail risk” of stochastically incurring catastrophic social losses. Conversely, preventive intervention at State C minimizes the expected total cost with statistical significance ($p<0.001$) and drastically decouples engineering costs from social risks. This research provides quantitative evidence that early infrastructure intervention functions as an indispensable “social investment” for mitigating public health risks under the specific parameters of the proposed model. Full article

Outrigger systems incorporating buckling-restrained braces (BRBs) can improve the seismic performance and resilience of tall buildings by combining lateral stiffness enhancement with supplemental energy dissipation. However, determining the effective number, elevation, and stiffness distribution of outrigger–BRBs remains computationally demanding when many possible configurations are considered. This study proposes a computationally efficient power-based sequential optimization approach for identifying effective outrigger–BRB placement and stiffness allocation in tall building systems. A nine-zone finite element benchmark model, developed in MATLAB based on a previously tested structural configuration, is used to examine the proposed method through nonlinear time-history analysis under the 1940 El Centro ground motion. The optimization procedure incrementally allocates BRB stiffness to candidate outrigger locations and selects the configuration that minimizes the maximum inter-story drift ratio at each step. The results are compared with a complete combinational reference search within the selected candidate space to assess whether the proposed procedure can identify optimal or near-optimal configurations with fewer nonlinear analyses. The findings show that the proposed method can reproduce the main effective outrigger–BRB placement patterns while reducing the number of required analyses within the investigated benchmark problem. The results also indicate that BRB stiffness limits influence the distribution of stiffness along the building height and promote more gradual drift reduction. Although the numerical investigation is limited to a benchmark model and a single seismic input, the proposed framework provides a practical basis for preliminary design, rapid parametric assessment, and future extension to multi-record and multi-objective optimization of outrigger–BRB systems. Full article

Microencapsulated phase change material (MPCM) can be used in place of sand in mortar to obtain phase change construction materials; however, this will degrade the mortar’s mechanical qualities. To address this challenge, a novel synergistic approach was proposed: phase change materials were used in mortar to enhance its thermal properties, while polyvinyl alcohol (PVA) fibers were uniquely incorporated to counteract the mechanical degradation caused by MPCM. Twenty different types of mortar were created and produced. Tests were conducted on the mortar’s micro properties, consistency, compressive strength, thermal conductivity, and specific heat capacity. The findings indicate that adding 0.4% PVA to the mortar optimally strengthened it, compensating for mechanical loss, while replacing sand with MPCM had a negative impact on consistency. The thermal conductivity of the PVA-MPCM mortar ranged from 0.75 to 1.2 W·m⁻¹·K⁻¹, decreasing by up to 34.45% when the MPCM substitution rate reached 4%. Furthermore, as the MPCM substitution rate rises to 4%, the peak value of specific heat capacity increased by 195.28% during the heating process, and replacing sand with MPCM had a negative impact on consistency. The thermal conductivity of the PVA-MPCM mortar ranged from 0.75 to 1.2 W·m⁻¹·K⁻¹, indicating that adding MPCM to the mortar had a significant impact on thermal conductivity. As the MPCM substitution rate rises, so does the peak value of specific heat capacity. Full article

The Federal Institute of Material Research and Testing has performed many impact tests, from very small laboratory tests to very big “free-field” tests with heavy containers on stiff foundations. The first measurements have been done on a big foundation where it should be guaranteed that the foundation is rigid and the container is tested properly. Later, a smaller drop-test facility has been built on the ground inside an existing building. It had to be controlled by prediction and measurements to ensure that the drop test will not damage the building. Tests from different heights on soft, medium, and stiff targets have been done to find out rules which allow to identify acceptable and unacceptable drop tests. Later, the biggest drop test facility has been built for masses up to 200 t. It was necessary for the design of the foundation to estimate the forces which occur during the drop tests. In addition, the acceptable tests should be selected and controlled by measurements where the impact duration is important. Different sensors, accelerometers, accelerometers with mechanical filters, geophones (velocity transducers), strain gauges, and pressure cells have been applied for these tasks. Signal transformations and model calculations have been used to check and understand the dynamic measurements. The simplest law is the conservation of the momentum which is a good approximation if the impact is short. If the soil under the foundation has an influence on the deceleration of the container, the maximum foundation velocity is lower than the simple estimation. Full article

The present study investigates the application of conventional and ensemble machine learning models for slope stability prediction, which is essential for landslide risk reduction and sustainable infrastructure management. A database containing 627 slope cases was used, including six input variables: unit weight, cohesion, friction angle, slope angle, slope height, and pore pressure ratio. Six machine learning models, namely Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), Classification and Regression Tree (CART), and Boosted Tree, were developed and evaluated. The models were assessed using ROC analysis, confusion-matrix-derived metrics, precision–recall analysis, feature importance assessment, and unseen testing cases. The results showed that ensemble-based models provided superior predictive performance compared with conventional machine learning models. Based on ROC analysis, RF achieved the highest ROC-AUC value of 0.93, followed by Boosted Tree and XGBoost with ROC-AUC values of 0.92 and 0.90, respectively. Based on confusion-matrix-derived metrics, Boosted Tree achieved the highest accuracy of 0.862 and F1-score of 0.874, while RF showed comparable performance with an accuracy of 0.857 and F1-score of 0.868. Feature importance analysis indicated that cohesion and unit weight were among the most influential variables affecting slope stability prediction. In addition, the unseen testing cases confirmed the practical generalization capability of the ensemble models, with Boosted Tree and RF achieving accuracies of 0.920 and 0.880, respectively. Overall, the findings demonstrate that ensemble learning models, particularly Boosted Tree and RF, can provide reliable and interpretable decision-support tools for preliminary slope stability assessment and landslide hazard management. Full article