CivilEng, Volume 7, Issue 1 (March 2026) – 19 articles

Active support systems are being increasingly applied in the control of large deformation in soft rock tunnels, and exploring the bearing characteristics of prestressed anchor bolts is of great engineering value for improving the long-term stability of tunnel structures. To address the problems of insufficient quantitative characterization of the bearing performance of prestressed anchor bolt support in soft rock tunnels and the difficulty of small-scale model tests in revealing the synergistic bearing law of support and surrounding rock, this study took a 350 km/h double-line high-speed railway tunnel as the prototype and established a large-scale tunnel structure model test system to conduct comparative tests under three working conditions: unsupported, ordinary bolt support, and prestressed anchor bolt support. By monitoring the tunnel failure process and mechanical response of the support structure throughout the test, the failure modes, bearing capacity, deformation characteristics, and axial force distribution of anchor bolts of tunnels under different support forms were systematically analyzed to quantitatively reveal the active support mechanism and bearing strengthening effect of prestressed anchor bolts. The results show that the design bearing capacity of the tunnel model with prestressed anchor bolt support is increased by 127.3% and 31.6% compared with that of the unsupported and ordinary bolt support models, and the ultimate bearing capacity is increased by 120.0% and 43.5%, respectively. Its secant stiffness in the initial loading stage reaches 80.0 kPa/mm, which is five times that of the ordinary bolt support and can effectively restrain the early plastic deformation of the surrounding rock. When the design bearing capacity is reached, the tensile stress of prestressed anchor bolts accounts for 40.2~69.8% of the ultimate tensile strength, with a more uniform axial force distribution and a much higher utilization rate of material mechanical properties than ordinary anchor bolts, which can fully mobilize the bearing potential of deep rock mass and realize the synergistic bearing of support and surrounding rock. This study accurately quantifies the bearing strengthening law of prestressed anchor bolts on tunnel support systems and clarifies the core mechanism of their active support. The research results provide important experimental basis and theoretical reference for the optimal design and engineering application of prestressed anchor bolts in soft rock tunnel engineering. Full article

The UK faces an urgent challenge to simultaneously accelerate housing delivery and reduce whole-life carbon emissions, yet robust empirical evidence on the carbon performance of modular steel housing remains limited. This study aims to quantify the carbon impacts of a modular light-gauge steel frame social housing dwelling in the UK and to benchmark its performance against contemporary low-carbon construction typologies. A cradle-to-grave life cycle assessment was conducted using primary project data from a real modular housing development, with embodied carbon modelled in One Click LCA and operational energy assessed through SAP 10.2-verified datasets. The results indicate a total whole-life carbon footprint of 91.3 tCO₂e over a 50-year period, with embodied emissions (A1–A3) accounting for 38.2% and operational energy and water use contributing 48.1%. The normalised embodied carbon intensity of 366 kgCO₂e/m² (A1–A5) is comparable to recent high-performing cross-laminated timber buildings, demonstrating that optimised modular steel systems can allow for low-carbon outcomes typically associated with bio-based construction. Sensitivity analysis shows that low-carbon foundation concrete, bio-based insulation, and steel optimisation can reduce upfront emissions by approximately 8–10%. Dynamic energy simulations were also used to assess how different design choices influence operational carbon emissions. This study provides transparent, real-project evidence of the whole-life carbon performance of UK modular light-gauge steel frame housing and identifies practical design strategies for further decarbonisation. The findings support informed decision-making for policymakers, designers, and housing providers seeking scalable, low-carbon residential solutions. Full article

To effectively enhance the seismic performance of traditional Chinese timber structures, this study proposes a reinforcement method utilizing friction dampers. Based on the working mechanism of friction dampers and the extended discrete element theory, an analytical model for timber structures equipped with these dampers was developed and validated through shake table tests. Subsequently, dynamic analyses were conducted to systematically evaluate the enhanced seismic energy dissipation capacity of the ancient timber structures by the reinforcement of friction dampers. The friction coefficient (μ), bolt pre-tension strain (ε), and action distance (l) were selected as key parameters. A multi-objective optimization function was constructed using the weighted sum method, enabling a multi-objective parameter optimization analysis for the friction dampers to identify the optimal parameter combination under specific conditions. The results indicate that the established extended discrete element model effectively simulates the dynamic characteristics of the structure. The installation of friction dampers significantly enhanced the structure’s energy dissipation capacity and substantially reduced the peak displacement. However, due to the initial stiffness introduced by the dampers, the lateral stiffness of the column frame increased markedly, leading to a significant amplification of the acceleration response, with a maximum increase in peak acceleration reaching 77%. The multi-objective optimization analysis revealed that with weighting coefficients λ_a = λ_b = 0.5, the optimal damper parameter combination is μ = 0.36, ε = 102 με, and l = 268 mm. Under these conditions, the structural displacement response decreased by 38.5%, while the acceleration response increased by 93.7%. It is noted that the derived optimal design solutions are pertinent to the specific structural typology and ground motions considered. Full article

Alkali-activated materials can significantly reduce carbon dioxide emissions compared with cement. However, their durability remains insufficiently understood. This study investigated the effects of calcium hydroxide (Ca(OH)₂, CH), an expansion agent (calcium sulfoaluminate, CSA), and a shrinkage-reducing agent (SRA) on the compressive strength and length change and determined the optimal content levels for each agent. Experiments were conducted to evaluate the compressive strength and length change of 17 mortar mixtures containing CH, CSA, and SRA. The substitution ratios of CH, CSA, and SRA were fixed at three predefined levels for each factor. The microstructural changes induced by the use of each agent were analyzed using pH measurements, porosity analysis, and X-ray diffraction. In addition, the water desorption behaviors associated with CSA and SRA were assessed. Experimental and statistical analyses demonstrated that the optimal contents of CH, CSA, and SRA for simultaneously improving the compressive strength and length change were 8.54, 10.0, and 0.76 wt.%, respectively. The use of CSA significantly enhanced the compressive strength development and dimensional stability of the mortar. This improvement was associated with a reduction in the porosity, which was attributed to ettringite formation. Furthermore, while the SRA slightly reduced the compressive strength, it significantly improved the dimensional stability. Full article

The long-term behavior of reinforced concrete (RC) structures under sustained loading is strongly affected by creep and cracking, particularly under service conditions where tension stiffening and curvature changes are significant. This study investigates the flexural response of cracked RC beams through combined numerical and experimental analyses. A new 1D finite element model is proposed, integrating nonlinear material behavior, damage mechanics, and time-dependent effects, including creep in both compression and tension. The model relies on a layered fiber section approach and uses a Newton–Raphson iterative procedure to solve equilibrium, allowing accurate prediction of strain, curvature, and internal force evolution over time. The model shows excellent agreement with experimental observations and ABAQUS simulations, accurately capturing deflection trends and crack development. Its performance is further validated using a database of 55 RC beams, including specimens with recycled aggregates and fiber reinforcement. Across this dataset, 84.5% of predicted deflections fall within ±1 mm of measured values, with an R² of 0.960, demonstrating strong reliability. A Sobol-based sensitivity analysis identifies load ratio as the most influential parameter on long-term deflection, followed by concrete strength and humidity. Overall, the model offers an efficient and robust tool for long-term deflection prediction, bridging simplified design rules and complex 3D simulations. Full article

Nonlinear seismic analysis procedures can accurately estimate structural responses but are computationally intensive, making them impractical for engineering design. This study provides the first comprehensive evaluation of N2 and modal pushover analysis for eccentrically braced frames (EBFs), revealing their strengths and limitations in predicting link rotations, shear demands, and drift distribution under Canadian seismic hazards. Analyzed were four-, eight-, and 14-storey chevron EBFs under real and artificial ground motions compatible with the response spectrum of Vancouver, Canada. The findings indicate that inelastic link rotations for all EBFs remain below the design limit of 0.08 rad, except for the upper two floors of the 14-storey EBFs. Seismic analysis reveals that maximum inelastic link shear forces often exceed design recommendations. It is also observed that both the N2 method and MPA procedure could reasonably predict the peak roof displacements for low-rise EBF buildings. In addition, while the MPA procedure provides better predictions of maximum inter-storey drifts over all storeys for medium-to-taller EBFs, inter-storey drifts are not predicted well in the N2 method. Additionally, the current code formula for estimating the fundamental period of EBFs predicts shorter periods than those obtained from analysis. An improved formula for estimating EBF periods is proposed. Full article

Concrete, as a widely used construction material, suffers from performance degradation due to chloride penetration and sulfate attack in harsh environments. Conventional performance-enhancing methods are costly and emit high levels of carbon dioxide. This study modified graphene oxide (GO) with polycarboxylate superplasticizer (PCE) alone or PCE synergized with a rubber viscosity reducer, optimized dispersion (50 °C water bath for 1 h), and prepared C50 modified concrete (500 kg/m³ cementitious materials, w/b = 0.33). GO contents were 0%, 0.001%, 0.003%, 0.005%; a group with 8% reduced cementitious materials (460 kg/m³) was also tested. Results showed PCE-viscosity reducer synergy better dispersed GO, improving concrete workability. GO accelerated cement hydration via nucleation, refining C-S-H gel and reducing porosity. At 0.005% GO, 56 d drying shrinkage dropped by 29.3% vs. the blank, and 56 d chloride penetration electric flux was 586 C, meeting 100-year service life. Sulfate resistance also improved with higher GO content. Even with 8% less cementitious materials, modified concrete outperformed the blank. This provides support for GO’s application in cement-based materials. Full article

Avoiding highway infrastructure construction cost overruns and reducing associated claims and disputes continues to be a challenge in many countries. Research is needed in identifying notable project planning and management deficiencies that are likely to cause cost overruns. The literature suggests numerous potential causes of cost overrun but the clustering of cause variables and relative importance of clusters has not been researched. The research reported here addresses this knowledge gap using predictive models developed with data contributed by several agencies in participating countries and suggests mitigation measures. Following a review of methods and data sources, a methodological framework is advanced that encompasses statistical methods well suited for providing a scientific basis for identifying important clusters of cost overrun variables. Fifty-three completed questionnaires contributed by knowledge experts and experienced managers from Canada, the United States, the Middle East, and Australia met the sample requirements of statistical methods. Starting from 53 variables, the principal component-supported factor analysis method identified clusters of cost overrun variables and their relative importance was inferred with developed logistic regression models. Deeper insights into the causes of cost overruns obtained from this research suggest mitigation measures (e.g., improved qualification and experience of personnel, enhanced planning and design practices, risk analysis of inputs to cost estimation process) that are within reach of managers. The results can enhance infrastructure planning and management practice including a reduction in claims and disputes. Full article

The demand for sustainable and environmentally friendly soil stabilization methods for subgrade improvement for pavements has led to exploring techniques that minimize ecological impact while optimizing engineering properties. Traditional stabilizers like cement and lime, though effective, have significant environmental drawbacks, including a high carbon footprint, disruption of vegetation, and health risks to workers. This study investigates the efficiency of biopolymers and eggshell powder as eco-friendly, sustainable soil stabilization agents. Parameters such as compaction characteristics, California Bearing Ratio (CBR), and micro-structural analysis were assessed. The research evaluates soil samples treated with varying concentrations of biopolymer (1%, 2%, and 3%) and eggshell powder (4%, 6%, and 8%). Results indicated that biopolymer addition slightly decreased the maximum dry density (MDD) and increased the optimum moisture content (OMC), while eggshell powder slightly increased MDD and decreased OMC. The optimal mix, soil + 1% xantham gum + 6% eggshell powder, enhanced CBR by 225.6% and 323.8% for soaked and unsoaked conditions, respectively. The scanning electron microscope revealed that treated soil samples transformed into a hard solid matrix, demonstrating improved stability. EDX analysis revealed the mineralogical composition of the mixes. Overall, the use of biopolymers and eggshell powder not only enhances soil strength but also promotes environmental sustainability. Full article

The accurate estimation of the fundamental period is critical for seismic design using the Equivalent Lateral Force method. This study evaluates widely used empirical period formulae from international seismic codes and previous research by comparing them with detailed finite element method (FEM) analyses. A total of 93 reinforced concrete building models were assessed. The results show that most empirical formulae, notably the American Society of Civil Engineers Standard (ASCE 7-10), the Eurocode, the National Building Code of Canada (NBCC), and the Saudi Building Code (SBC 301), systematically underestimate the fundamental period in low- and mid-rise buildings often by more than 40% under cracked conditions, while discrepancies reduce under uncracked assumptions. Equations such as those proposed by the Building Standard Law of Japan (BSLJ) and Australian Standard (AS 11407.2) show comparatively closer agreements with FEM predictions, whereas formulae developed by Goel and Chopra and by Alguhane et al. have distinct differences, especially at greater heights. Statistical parameters, including the arithmetic mean difference and the standard deviation, were employed to enhance the comparison and assess the accuracy and dispersion of the estimated fundamental periods. The results indicate that empirical formulae, although beneficial in first-design stages, are likely to yield conservative results and suggest the use of advanced numerical computation or revised models and coefficients for RC high-rise and irregular buildings. Full article

Cascade dams describe an arrangement of several dam structures built along a flow path. Failure of one upstream dam in the cascade system can trigger catastrophic consequences to the downstream dams, as evidenced recently in the Edenville Dam and Sanford Dam. Previous research has mainly focused on rainfall-induced dam failures, although recent failures have demonstrated a combination of floods and earthquakes. Moreover, limited studies have analyzed the sensitivity of dam breach parameters, such as dam breach height and width in dams arranged in a cascade system for seismic events. Most hydraulic simulations that model seismic-induced dam failures assume the complete collapse of dams to analyze the downstream consequences. Hence, this study presents a novel analysis in simulating earthquake-induced failures in a cascade dam system, considering the sensitivity of dam breach parameters. In addition, dam breach parameters have been derived from the structural analysis of dams employing Finite Element Models (FEMs) to a critical Peak Ground Acceleration (PGA) of 0.3 g. Two-dimensional hydrodynamic simulations, along with the full dynamic wave equations, are undertaken in the study to model the earthquake-induced cascade dam failures. The results further elaborate on the significance of modeling cascade dam failures in terms of the consecutive arrival of floods and total flow compared to individual dam failures. Sensitivity analysis of dam breach parameters shows that the breach height is more significant than the breach width and breach slope. However, its significance decreases as the dam breach flood flow path increases in distance. The study further confirms the novel utilization of structural analysis to derive dam breach parameters for seismic-induced dam failures of concrete arch dams and rockfill dams, which will guide the optimization of disaster mitigation strategies and the operational resilience of the dams. Full article

Annex A of EN 1992-1-1:2023—recently revised and amended in the context of the Second Generation of Eurocodes—introduces a method to adjust partial safety factors for the resistance side alongside a set of factors for different conditions and design situations, both for new and existing structures. The method proposed in Annex A is complemented by a set of stochastic models for relevant basic variables and forms a rather simple and objective format to adjust the partial safety factors from the default values offered in EN 1990:2023. Yet, over the last few years, advanced reliability-based methods aligned with modern computational tools have proved to enable rather robust and efficient structural reliability assessments. A thorough comparative analysis is imperative to understand how distinct reliability-based methods can be applied to adjust partial safety factors in the design of new structural components composed of steel-reinforced concrete. This analysis sheds light on the use of different methods to derive partial safety factors for the resolution of common engineering problems and offers inferences regarding possible implications in terms of safety and economic efficiency of design solutions. Full article

Lithium-ion batteries (LIBs) are essential for electric vehicles, consumer electronics, and grid storage, but their rapidly increasing demand is paralleled by growing waste volumes. Current disposal methods remain costly, complex, energy-intensive, and environmentally unsustainable. This pilot study investigates a scalable, low-impact disposal method by incorporating LIB waste into concrete, evaluating both the structural and environmental effects of LIB waste on concrete performance. Several cement–mortar cube specimens were cast and tested under compression using the cement–mortar mix with varying battery waste components, such as black mass and varied metals. All mortar mixes maintained an identical water-to-cement ratio. The compressive strength of the cubes was measured at 3, 7, 14, 21, and 28 days after casting and compared. The mix containing black mass exhibited a 35% reduction in compressive strength on day 28, whereas the mix containing varied metals showed a 55% reduction relative to the control mix without LIB waste. A case study was conducted to evaluate the combined structural and environmental performance of a concrete specimen incorporating LIB waste by estimating the embodied carbon (EC) for each mix and comparing the strength-to-net EC ratio. Selective incorporation of LIB waste into concrete provides a practical, low-carbon upcycling pathway, reducing both embodied carbon and landfill burden while enabling greener, non-structural construction materials. This sustainable approach simultaneously mitigates battery waste and lowers cement-related CO₂ emissions, delivering usable concrete for non-structural and low-strength structural applications. Full article

Construction on soft, highly compressible soils increasingly requires reliable ground improvement solutions. Among these, Rigid Inclusions (RIs) have emerged as one of the most efficient soil-reinforcement techniques. This paper synthesizes evidence from over 180 studies to provide a comprehensive state-of-the-art review of RI technology encompassing its governing mechanisms, design methodologies, and field performance. While the static behavior of RI systems has now been extensively studied and is supported by international design guidelines, the response under cyclic and seismic loading, particularly in liquefiable soils, remains less documented and subject to significant uncertainty. This review critically analyzes the degradation of key load-transfer mechanisms including soil arching, membrane tension, and interface shear transfer under repeated loading conditions. It further emphasizes the distinct role of RIs in liquefiable soils, where mitigation relies primarily on reinforcement and confinement rather than on drainage-driven mechanisms typical of granular columns. The evolution of design practice is traced from analytical formulations validated under static conditions toward advanced numerical and physical modeling frameworks suitable for dynamic loading. The lack of validated seismic design guidelines is high-lighted, and critical knowledge gaps are identified, underscoring the need for advanced numerical simulations and large-scale physical testing to support the future development of performance-based seismic design (PBSD) approaches for RI-improved ground. Full article

This paper presents a quantitative framework for assessing and managing public-safety risks around dams. The framework integrates a hazard–event–objective–control structure with the Analytic Hierarchy Process (AHP) to transform qualitative judgments into quantitative risk measures. Likelihoods, consequences, and overall risk are expressed on a ratio scale, allowing results to be aggregated, compared, and communicated in monetary terms. Probabilistic simulation accounts for uncertainty and generates outputs such as Value-at-Risk (VaR), loss-exceedance curves, and societal F–N charts, providing a clear picture of both expected and extreme outcomes. Optimization identifies control portfolios that achieve the greatest risk reduction for available budgets. A hypothetical dam case study demonstrates the framework’s application and highlights its ability to identify high-value safety investments. The framework offers dam owners and regulators a transparent, data-driven basis for prioritizing public-safety improvements and supports both facility-level (micro) and program-level (macro) decision-making consistent with international risk-tolerability and ALARP principles. Full article

Base isolation is known as a useful and popular technique for seismic upgrading of reinforced concrete buildings. Predicting damage levels based on relative inter-story drift plays an important role for designing optimal base isolation systems. However, the existing codes usually rely on the acceleration spectrum for calculating the relative inter-story drift, and they do not provide an accurate estimation of the relative inter-story drift. Consequently, to cover the research gap, machine learning algorithms are being trained and used for identification of damage levels in retrofitted reinforced concrete buildings. More than 7000 datasets were derived by using nonlinear time-history and incremental dynamic analysis. A total of 48 reinforced concrete buildings with different stories and bay numbers were designed based on an older version of existing building codes, and then, base isolation systems were designed for the seismic retrofit. The machine learning algorithms used here were Decision Tree, Random Forest, Support Vector Machine, Extreme Gradient Boosting, and an Artificial Neural Network. Based on the results, four of the mentioned algorithms have the capability of predicting the damage level with an accuracy of more than 85%, with the best performance being reached by extreme gradient boosting with an accuracy of 89%. Finally, the most important parameters affecting the damage levels of retrofitted reinforced concrete buildings were derived. Full article

The construction industry significantly contributes to global sustainability challenges, producing 30–40 percent of global carbon dioxide emissions and consuming large amounts of natural resources. Pervious concrete has emerged as a sustainable alternative to conventional pavements due to its ability to promote stormwater infiltration and groundwater recharge. However, the absence of fine aggregates creates a highly porous structure that results in reduced compressive strength, limiting its broader structural use. Determining compressive strength traditionally requires destructive laboratory testing of concrete specimens, which demands considerable material, energy, and curing time, often up to 28 days—before results can be obtained. This makes iterative mix design and optimization both slow and resource intensive. To address this practical limitation, this study applies Machine Learning (ML) as a rapid, preliminary estimation tool capable of providing early predictions of compressive strength based on mix composition and curing parameters. Rather than replacing laboratory testing, the developed ML models serve as supportive decision-making tools, enabling engineers to assess potential strength outcomes before casting and curing physical specimens. This can reduce the number of trial batches produced, lower material consumption, and minimize the environmental footprint associated with repeated destructive testing. Multiple ML algorithms were trained and evaluated using data from existing literature and validated through laboratory testing. The results indicate that ML can provide reliable preliminary strength estimates, offering a faster and more resource-efficient approach to guiding mix design adjustments. By reducing the reliance on repeated 28-day test cycles, the integration of ML into previous concrete research supports more sustainable, cost-effective, and time-efficient material development practices. Full article

This study examines the use of electronic waste (e-waste) as an alternative material in concrete for sustainability and natural resource conservation. Various e-wastes, such as Polyvinyl Chloride (PVC), Glass-Reinforced Plastic (GRP), Glass Fiber-Reinforced Polymer (GFRP), cross-linked polyethylene (XLPE), polyethylene (PE), electronic cable waste (ECW), Waste Electrical Cable Rubber (WECR), copper fiber (Cu Fib.), aluminum Fibers (Al fib.), steel fibers, basalt fibers, glass fibers, aramid−carbon fibers, Kevlar fibers, jute fibers, and optical fibers, were tested for influence on compressive, flexural, tensile strength, modulus of elasticity, and water absorption. Outcomes show that fine particle waste at low levels (0.2–1.5%) can improve mechanical performance, while higher levels of replacement or coarse particles generally reduce performance. Mechanical and physical properties are highly sensitive to material type, particle size, and dose. Life cycle assessment (LCA) and predictive modeling are recommended as validation for sustainability benefits. Full article

The growing demand for sustainable pavement materials has driven increased interest in asphalt mixtures incorporating recycled crumb rubber (CR). While CR modification enhances mechanical performance and durability, its often increases initial production costs and energy demand. This study develops an integrated framework that combines machine learning (ML) and economic analysis to identify the optimal balance between performance and cost in CR-modified asphalt overlay mixtures. An experimental dataset of conventional and CR-modified mixtures was used to train and validate multiple ML algorithms, including Random Forest (RF), Gradient Boosting (GB), Artificial Neural Networks (ANNs), and Support Vector Regression (SVR). The RF and ANN models exhibited superior predictive accuracy (R² > 0.98) for key performance indicators such as Marshall stability, tensile strength ratio, rutting resistance, and resilient modulus. A Cost–Performance Index (CPI) integrating life-cycle cost analysis was developed to quantify trade-offs between performance and economic efficiency. Environmental life-cycle assessment indicated net greenhouse gas reductions of approximately 96 kg CO₂-eq per ton of mixture despite higher production-phase emissions. Optimization results indicated that a CR content of approximately 15% and an asphalt binder content of 4.8–5.0% achieve the best performance–cost balance. The study demonstrates that ML-driven optimization provides a powerful, data-based approach for guiding sustainable pavement design and promoting the circular economy in road construction. Full article