A novel multi-field coupled experimental system was developed to simulate chloride-induced deterioration of ultra-high-performance concrete (UHPC) under deep underground environments. The system integrates structural component casting, a self-anchored loading device, a corrosion apparatus capable of simultaneously applying mechanical load, hydraulic pressure, and stray current, and an acoustic emission (AE)-based damage monitoring technique. Based on this system, chloride corrosion tests on UHPC slabs were conducted under various environmental conditions. The results demonstrate that the system effectively reproduces deep underground service conditions. The stress ratio was identified as the dominant factor controlling strength deterioration, followed by stray current, while the influence of hydraulic pressure was relatively minor but increased with higher stress levels. Quantitatively, the peak load of UHPC slabs decreased from 9.7 kN to 2-9.1 kN under coupled deep underground conditions. AE analysis revealed that corrosion promoted crack propagation along pre-existing flaws, increasing the proportion of tensile cracks from 47.3
As an efficient preventive maintenance technique, ultra-thin overlay imposes higher performance requirements on its asphalt binder. This study selected high–viscosity high–elasticity asphalt (HVEA) as the binder for ultrathin overlays, with SBS-modified asphalt serving as the control. The investigation focused on the thermal–oxidative aging resistance, cracking resistance, and fatigue resistance of the binders, evaluated through the Thin Film Oven Test. A series of laboratory tests—including force–ductility tests, linear amplitude sweep tests, and self-healing assessments—were conducted alongside Scanning Electron Microscopy (SEM) and Fourier Transform Infrared Spectroscopy (FTIR) to correlate macroscopic performance with microstructural evolution. The results reveal a distinct performance inflection with increasing aging temperature, marking a transition from elasticity–dominated response to stiffness–controlled damage. Quantitatively, compared to unaged conditions, the ductility of HVEA decreased by approximately 50
This research delved into the application of barley straw ash (BSA) as a reactive pozzolan in quaternary-blended ultra-high-performance concrete (UHPC) incorporating cement, sugarcane bagasse ash (SBA), and marble powder (MP). Specifically, BSA was calcined at 400, 600, and 800 °C and used to substitute for cement at 10, 20, and 30
This study investigates the bending moment increase rate ζ in the modified conventional calculation method for shield tunnel segmental lining structure design. Based on similarity theory, a geometric similarity ratio of CL=Cp/Cm=30.0 was adopted. Model segments and longitudinal ring joints were fabricated using high-density polyethylene (HDPE), while polybutylene terephthalate (PBT) bolts were used for transverse segment joints. A staggered joint assembly model of a large-diameter circular shield tunnel was constructed. Following the reinforced concrete simply supported beam positive moment test method, a self-developed loading device was employed to conduct load tests on three staggered-assembled large-diameter circular shield tunnel segment rings. The experimental results revealed the moment distribution characteristics, with bending moments ranging from − 0.378 to +0.353 N·m and corresponding deformations of − 5.880 to +5.250 mm. The bending moment increase rate ζ was determined to be in the range of 0.293–0.509, providing essential data support for the segmental design of large-diameter circular shield tunnels.
Post-grouting piles play an important role in improving the bearing capacity of bored piles; however, in existing research methods, there are relevant limitations, for example, the comparative analysis of the three grouting methods (side grouting, end grouting, and composite grouting) is limited; in actual engineering, due to the lack of accurate theoretical models, it relies on empirical enhancement coefficients. This study focuses on addressing the above shortcomings by combining experimental and numerical simulation methods, with the compressive bearing performance of post-grouting piles as the research object. The main research contents are as follows: 1. Through finite element simulation, it is revealed that the load-settlement response of side-grouted piles exhibits a mechanism of progressive stiffness degradation. 2. The dual reinforcement mechanism in composite grouting is revealed, and under the configuration of this study, the technical advantages of composite grouting are verified. 3. The optimal proportional critical coefficient for side grouting of piles is discovered and demonstrated. Under the factors such as the silty soil parameters and grouting scheme in this test, this value is 0.6. Beyond this value, the gain will gradually decrease due to the limited stress transfer in the lower stratum. 4. A theoretical model for the vertical load of post-grouting piles is established, and its design reliability is verified through experiments and numerical simulations. The above research results not only enrich the ideas for solving the existing shortcomings of post-grouting technology but also provide a theoretical reference for solving on-site pile grouting projects in China.
In this study, an integrated computational and data-driven framework is proposed for constructing three-dimensional failure envelopes for shallow anchor foundations subjected to combined vertical–horizontal–moment (V–H–M) loading in anisotropic clay. A parametric dataset of normalized load capacities (V/V₀, H/Asu, M/ADsu) is generated using a finite-element limit analysis (FELA), incorporating five governing parameters, namely, the embedment ratio (D/B), anisotropic strength ratio (re), vertical load ratio (V/V₀), load inclination angle (β), and interface adhesion factor (α), which are explicitly incorporated into the dataset. Four machine learning surrogate models, an artificial neural network (ANN), an extreme learning machine (ELM), a general regression neural network (GRNN), and a deep factorization machine (DeepFM), are developed to replace computationally intensive FELA simulations. The proposed machine learning models are rigorously verified and validated against the FELA-generated database. Among the evaluated models, the DeepFM and ANN demonstrate superior predictive accuracy, with R² values of 0.997–0.999 and root mean square errors (RMSEs) between 0.039 and 0.065 for both H/Asu and M/ADsu prediction tasks. In contrast, the ELM and GRNN exhibit substantially lower predictive accuracy, indicating a limited ability to represent highly nonlinear V–H–M interaction behaviors. An analysis of the predicted failure envelopes reveals that β is the dominant parameter governing the horizontal capacity, whereas D/B primarily controls the moment capacity. The proposed FELA–machine learning framework provides a computationally efficient and mechanically consistent surrogate modeling approach for constructing three-dimensional failure envelopes, offering practical utility for the analysis and design of shallow anchor foundations under combined loading.
A sequential interaction analytical method is proposed to predict ground surface settlement induced by shallow twin shield tunnelling. Analytical solutions for the stress and displacement of a shallow single tunnel in a semi-infinite plane are first derived using complex variable theory. The Schwarz alternating method is then employed to iteratively determine the additional interaction stresses between arbitrarily arranged twin tunnels, and the resulting analytical functions are sequentially superimposed to establish a sequential interaction analytical model. The proposed method is evaluated against four engineering case histories through comparison with field monitoring data and numerical simulations. For Case 2, the proposed method predicts a maximum surface settlement of 21.49 mm, which agrees closely with the measured value of 21.35 mm, reducing the relative error to 0.66
Automated pavement damage detection aims to address the inherent limitations of conventional manual inspection procedures, which are labor-intensive, subjective, and difficult to scale. The present study proposes FBI-YOLOv8m, a detection framework that integrates the Forensic-Based Investigation algorithm with YOLOv8m to achieve systematic hyperparameter optimization without modifying the underlying network architecture. The optimization strategy focuses on eight critical training configurations that govern input image resolution, learning dynamics, and data augmentation intensity to improve detection stability and generalization. The proposed framework is evaluated on a combined dataset comprising the multinational Road Damage Dataset 2022 and additional pavement imagery collected in Can Tho City, Vietnam, thereby extending the damage taxonomy by including two region-specific distress categories. Experimental results demonstrate that FBI-YOLOv8m achieves an mAP50 of 0.580 and mAP50:95 of 0.282, representing a 22.88
This study evaluated the functional pavement performance and deterioration behaviour of microsurfacing-treated pavement sections using the International Roughness Index (IRI), Pavement Condition Index (PCI), skid resistance represented by the SCRIM coefficient (SCRIM), and selected traffic-related and environmental variables. The evaluation framework integrated performance jump (PJ), deterioration slopes, deterioration deduction rate (DDR), Pearson correlation, and multiple regression coefficient analyses to assess post-treatment pavement performance and deterioration behaviour. The results showed that microsurfacing treatment was associated with improvements in all analysed pavement performance indicators. PCI recorded the highest mean improvement (75.7
This study examines the fracture and mechanical performance of hybrid fiber-reinforced high-strength concrete (HFRHSC) with different water-to-binder (W/B) ratios. Six mixtures incorporating hybrid combinations of steel, polymer, glass, and basalt fibers were investigated at W/B ratios of 0.42, 0.31, and 0.25. The synergistic effects of the fiber systems were evaluated in terms of compressive strength, splitting tensile strength, flexural behavior, fracture energy, residual strength, and toughness indices. Fracture properties were assessed using three-point bending tests on notched beams, where load-crack mouth opening displacement (CMOD) and load–deflection curves were used to characterize post-cracking behavior. In addition, bilinear softening and multi-exponential models were applied to reproduce the experimental load–CMOD response and estimate fracture energy. The multi-exponential model provided a more accurate representation of the nonlinear post-peak response, with coefficients of determination generally exceeding 0.95, whereas the bilinear model remained simpler and more suitable for practical engineering interpretation. The results show that hybridization substantially improved fracture resistance, particularly at lower W/B ratios. The steel–glass fiber system achieved approximately 54
This paper presents a comprehensive scientometric analysis of Transportation Management System (TMS) research in Nigeria and South Africa between 2005 and 2025, with a focus on Global South dynamics. Using data sourced from Scopus and visualized via VOSviewer, the study maps literature trends, publication types, keyword occurrences, institutional affiliations, and bibliometric coupling. The findings reveal a mature and diverse TMS research landscape in South Africa characterized by strong institutional collaboration and international visibility. Research themes include intelligent vehicle highway systems, adaptive traffic management, smart city planning, and decision support systems led by institutions such as the University of Cape Town, University of Johannesburg, CSIR, and the eThekwini Transport Authority. This reflects a well-integrated ecosystem between academia, industry, and government. In contrast, Nigeria demonstrates concentrated but emerging research activity, primarily from institutions such as the University of Lagos and the University of Ilorin. The focus is largely on urban traffic management, congestion reduction, and early-stage TMS initiatives. Despite some emphasis on data-driven approaches, keyword analysis shows limited thematic diversity in Nigerian literature. By comparing the two countries within the South‒South framework, this study identifies a knowledge gap, particularly in Nigeria, which presents an opportunity to expand the research scope and thematic variety. Overall, the paper provides valuable strategic insights into the evolution of TMS research in Africa and underscores the importance of intelligent, sustainable and data-driven transport systems to increase the efficiency of resilience and environmental performance across the continent.
Continuous Water Quality (WQ) monitoring and measurement in dam reservoirs at different locations in the same dam water regions are the major problem. Spatial heterogeneity led to variations in WQ metrics such as pH, DO, TDS in the dam reservoir based on different depths due to sedimentation and nutrient cycling. Manual sampling methods is not suitable for continues WQ parameter measuring in different locations of dam water regions. To solve the above problem, Landsat image of dam water regions is processed with the Transverse Dyadic Wavelet Transform (TDyWT) and the Adam Optimized Vision Transformer (AdamViT) algorithms and obtained the statistical features of water regions for the temporal and spatial regions. Nagi Dam (NGD) and Nakti Dam (NKD) reservoirs in Bihar, India is the study area for the proposed study. The statistical values of water regions measured are the mean, entropy, PSNR, band values. The obtain statistical water feature values are correlated with laboratory-based sample water measured four WQ metrics. AdamViT- Bayesian Optimised-(BO)- (Support Vector Regression) SVR predicts the above four Key WQ metrics. AdamViT- BO-SVR provide the R2 value 0.97, RMSE is 0.15, and MAPE is 3.4% compared to the ground truth.
The analysis of road traffic is fundamental for obtaining reliable information about itself. This paper presents Automatic Smart Vehicle Counting System – Intelligent System (CONTEV-SYS), a framework of a vehicle counting system developed in Python, employing YOLO v8n and the OpenCV library. A mixed method approach was adopted. Initially, a survey in the Likert scale was applied to 40 specialist identified the most extensive processing methods, errors in the data, and the impact in the weather as critical barriers of the traditional manual traffic counting system. Later on, a quantitative validation was carried out in nine vial scenarios, where different circulation patterns are presented in the city of Huancayo, comparing the system with a careful and accurate manual counting system. The results show that the CONTEX-SYS system has an average estimation efficiency of 89.40%, a Mean Absolute Percentage Error (MAPE) of 10.60%, reaching a maximum of 96% in scenario number 5, with a conservative behavior, that is, reducing the false positives in the severe occlusion case (R² = 0.921). The system attain a high precision in the vehicle classification, standing out the identification of heavy vehicles. In terms of operation, it saved 49.49% processing time, ensuring that the analysis was carried out in strict real-time. Finally, its implementation results to be highly promising in the field of civil engineering, particularly in the specialties of transportation and traffic.
Buildings play an important role in socio-economic and environmental systems, yet their environmental footprint is considerable, surrounding energy and water usage, waste production, and greenhouse gas outputs. The building, functioning, and tearing down of structures utilize precious resources and worsen climate change. To tackle these challenges, Green Building Standards and Rating Systems (GBRS) have been created to minimize the environmental impact of buildings throughout their complete life cycle. These systems prioritize Energy Efficiency (EE), Water Conservation (WC), and eco-friendly building materials. Also, the important study conducted on new constructions, there is still limited exploration of prevailing buildings. The majority of research highlights individual sustainability elements rather than offering a thorough examination of Green Building (GB) principles. The absence of uniformity across various rating systems, including LEED, BREEAM, and GRIHA, NGBS, and Green Star, hinders the effective comparison of their sustainability performance. Moreover, insufficient data and budget limitations obstruct the adoption of green certification for current buildings. This research intends to address these gaps by assessing the performance of current structures in relation to GB principles.
Solid concrete block masonry is widely used in construction, but its performance is affected by variability in material properties, workmanship, and block quality. Traditional deterministic design methods often fail to capture these uncertainties, potentially leading to unsafe designs. This research proposes a reliability approach to assess masonry prism compressive strength through the integration of predictive modelling and probabilistic analysis. The objective is to perform a reliability analysis of masonry prisms and determine the Probability of failure. Five cement-sand mortar ratios (1:3 to 1:7) were experimentally evaluated, and Multiple Linear Regression (MLR) models were developed to estimate prism strength as a function of block and mortar strengths. The developed regression equations were incorporated into a Monte Carlo simulation framework with 40,000 iterations to evaluate the Probability of failure and reliability index for a typical residential building configuration. Results indicate that the 1:3 mortar mix provides superior structural performance, yielding a probability of failure (𝑃𝑓) of 0.0479 and a reliability index (𝛽) of 1.66, satisfying the adopted target reliability criterion. Floor optimization analysis further revealed that the structure can safely support up to 14 floors while maintaining the adopted Probability of failure limit of 5%. The findings highlight the significant influence of material variability on masonry reliability and demonstrate the effectiveness of probabilistic methods for realistic safety assessment and performance-based masonry design.
Road traffic accidents continue to be a leading public safety concern in urban areas, and particularly for vulnerable road users including pedestrians and motorcyclists. While the increasing fatality and casualty counts urge a comprehensive approach to understand the factors contributing to crash risks, only a few studies in Mumbai focus on the dynamic interplay between the predictor variables of traffic volume, road condition, and types of road users in the prediction of crash risk. This study uses traffic data from Mumbai's road safety reports from 2019 to 2024 to develop a Negative Binomial Regression Model in order to predict crash risk. Safety Performance Functions are applied to estimate the influence of road features, traffic volume, location type, and weather conditions. The model is tested on real-world data, analyzing factors such as speed limits, road geometry, and user-specific risk factors. Some of the most significant factors that were identified in the model include the volume of traffic, geometric and location factors. The volume of traffic is an important factor, as the additional increase of 1,000 vehicles per day raises the risk of the accident by 0.23%. It has been predicted that 726 injuries have been caused by high-speed high-traffic peak-hour intersections, while 595 injuries have been caused by high-speed mid-block. The highest pedestrian crash risk was found to be in high-traffic intersections with 45.3%, compared to lower traffic intersections with only 15.2% in rural conditions. In terms of practical application, this model offers a means of identifying hazardous locations at a particular time that allows making effective measures to prevent accidents.
Many RC structures are deteriorating due to aging, increased service loads, environmental deterioration, corrosion of the reinforcement, and design deficiencies. Therefore, strengthening and retrofitting techniques are essential to improve the performance of RC structures. A promising method for strengthening RC structures is by using fiber-reinforced polymer composites because of their specific strength, rust resistivity and ease of application in construction. Currently, mesh-based reinforcement systems have emerged as an acceptable alternative to conventional FRP sheets based on improved bonding characteristics, improved crack-resistant performance and improved stress redistribution. A systematic review was performed to investigate mesh-induced strengthening techniques for the RC beams under combined shear, flexural and torsional loading conditions. A total of 52 studies published between the years 2000 to 2026 were included for analysis from a variety of databases. The studies were categorized based on the strengthening materials used, wrapping configurations, loading conditions, and performance indicators such as load-carrying capacity, ductility, torsional capacity, crack prevention and energy absorption. The conclusion of the review indicates that while GFRP systems show greater ductility and are usually less costly, CFRP systems typically result in the largest increases in loading capacity. Hybrid FPR systems utilising carbon and glass fibers produced a balanced performance by increasing both loading and deformation capacity. The performance characteristics for strengthening have a direct bearing on how the RC beam behaves. Overall, the inclusion of mesh in the construction improved strengthening techniques and shows strong potential for enhancing the efficiency and durability of RC beams under complex loading conditions.
Climate change causes rising temperatures, changes in precipitation and other climatological variables that are related to reference Evapotranspiration (ET₀). The estimation of ET₀ is crucial for irrigation scheduling and water resource management. In this sense, the Penman-Monteith (PM) method is considered the most precise for ET₀ calculation, but the use of the method is often constrained by limited climatological data. In Peru, the PISCOeo_pm dataset provides ET₀ values for the period 1981–2016 that apply the PM method and presents a baseline to train machine learning models that can use limited data to extend ET₀ estimates to recent years and make precise calculations of water demand requirements for irrigation systems. In this research, a random forest model and a multilayer perceptron neural network are developed using PISCOeo_pm data and then used to estimate recent ET₀ values. Daily estimation performance and climatological consistency revealed that the MLP is superior to the RF model. The MLP-estimated ET₀ values for the 2017–2025 period is then used to calculate and update the water demand in the Pucapuquio irrigation system located in the district of Pucará, in Huancayo province, Peru. The results revealed that water demand has increased in recent years and that the previous water demand for the critical month of August, originally 20,769 m³, has increased to 24,150 m³ under current climatological conditions. Potential mitigation strategies are presented that include increasing irrigation application efficiency or reducing cultivated areas by 15% for critical months, until more robust solutions are implemented, such as evaluating supplementary water sources that can potentially satisfy the new demand or the implementation of more efficient irrigation systems.
This study is an academic attempt aimed at shedding light on the impact of design elements of internal circulation corridors (transitional spaces) on the level of user satisfaction. The study represents an exploratory research into users' opinions towards the design of closed interior corridors in the University's National College of Technology building. The design of the circulation corridors in the building includes a set of elements, namely: visual composition, color harmony, visual rhythm, aesthetic lighting, architectural details, integration with works of art, transparency, visual Porosity, and visual identity. These elements were considered independent variables, while user satisfaction was considered the dependent variable in the study. To achieve the research objectives, an electronic questionnaire was developed that included four main exploratory axes distributed over nine dimensions, and it was then distributed to the study sample. The sample consisted of 113 users within the college, including students, employees, and visitors, who were selected randomly. All responses were analyzed using appropriate statistical methods, in particular, single-sample multiple regression analysis, based on SPSS. The main results of the study showed that there was a noticeable impact on the design of circulation paths in enhancing the level of user satisfaction, as the quality of spatial organization and visual treatments contributed to improving the circulation experience within the building. Dimensions such as color harmony, aesthetic lighting, and visual cohesion also received high ratings compared to other dimensions, while elements such as visual rhythm and artistic integration received average ratings. In light of these results, the study concluded with a set of recommendations that emphasize the importance of developing some design aspects in a way that contributes to raising the efficiency of movement and improving the quality of the spatial experience inside the building.
Problems with punching shear failure at slab-column joints have remained one of the major problems in RC flat slabs, especially where there is a combination of loadings from both gravity and lateral forces acting on the slab. This paper presents a numerical study of the use of drop panels and shear reinforcements in improving slab-column joints using nonlinear FEM with the CDP approach. Ten models (C25-C34) were considered while considering the two grades of concrete (M35 and M40), three types of reinforcement systems (unreinforced, with stirrups, and with stud rails) under static and Lateral loading conditions. The numerical results suggest that drop panels increase initial stiffness and Peak lateral load (for seismic analyses) capacity of slab–column joints. But, without geometric enhancement, the brittle punching behaviour under Lateral loading could not be prevented. The unstiffened configurations showed substantial loss of strength and higher deformations. Stirrups had been introduced to provide better confinement and post-peak stability. The systems with stud rail reinforcement had the best numerical response when measured in terms of load-carrying capacity, residual strength and ductility. The presence of a stirrup gives the added advantage of increased lateral load capacity of about 330% as well as good post-peak behaviour. The stud rail system exhibits the best overall behaviour, where the lateral load capacity can be increased by about 400%. The grade effect of concrete reveals that more the strength, the higher the stiffness and capacity and the lower the ductility in non-reinforced applications. The sensitivity analysis proves that the most important parameter that controls the performance is the shear reinforcement type. A comparison with codal provisions such as IS 456, ACI 318, and Eurocode 2 indicates that the existing formulations do not adequately capture the effect of reinforcement and the post-peak structural behaviour. The numerical results indicate that, out of all the examined configurations, the use of both drop panels and stud-rail reinforcement would yield the most favourable structural response. These results, however, are based on finite element simulation and require experimental validation in the future.
