Pre-stressed concrete continuous box girder bridges are widely used in bridge engineering due to their excellent mechanical properties. However, as the service life of the bridge increases and heavy vehicles exert additional loads, cracks may develop in the structure, leading to pre-stress loss and affecting its safety. This paper focuses on the reinforcement of an actual bridge and determines the pre-reinforcement stress state and stiffness degradation through load testing. The test results are combined with numerical simulations to analyze the stiffness of the box girder section. When the section stiffness is reduced by 5%, the deflection at the mid-span control section of the box girder is 11.7 mm, which is in good agreement with the actual condition. By integrating the bridge's appearance inspection results with numerical simulations, pre-stress loss in the box girder is analyzed. When the pre-stress loss reaches 10%, transverse cracks appear at the bottom of the main girder, similar to the results of field inspections. Based on this, the analysis considers a 5% stiffness reduction and a 10% pre-stress loss to evaluate the box girder.
The reuse of Reclaimed Asphalt Pavement (RAP) in road construction has become increasingly prevalent due to its potential environmental and economic benefits. The aging characteristics of RAP, particularly the degradation of its bitumen content, are critical for evaluating its suitability for future applications. The aging process is influenced by various meteorological factors, including solar radiation, temperature fluctuations, and precipitation. This study investigates the impact of these factors on the properties of bitumen in RAP, focusing on a pavement constructed between 2002 and 2005. After eight years of service, the pavement was milled and the material was stored in a stockpile for an additional eight years. The bitumen properties, specifically penetration and softening point, were measured at regular intervals over the 16-year period. Cumulative meteorological data, including temperature, solar radiation, and precipitation, were recorded and analysed in relation to the observed aging effects on the bitumen. The results demonstrated a linear correlation between the cumulative meteorological conditions and the degree of bitumen aging. Increased exposure to solar radiation and temperature fluctuations accelerated the aging process, while prolonged periods of precipitation appeared to have a moderating effect. These findings suggest that both the duration and intensity of exposure to specific environmental conditions must be considered when assessing the viability of using RAP in future pavement construction.
To address the complexities and inaccuracies associated with traditional methods of concrete compactness monitoring, in this paper, a real-time monitoring approach based on long short-term memory (LSTM) networks has been developed. Traditional methods often involve cumbersome data processing and yield large errors, especially in complex environments, in contrast, the proposed method leverages the LSTM network's ability to process time-series data, enhancing accuracy in detecting compactness defects within concrete structures, and the ultrasonic wave velocity through concrete under standard conditions has been set as a baseline value. The platform can visualize the curve of ultrasonic propagation speed in the monitored concrete over time, allowing for a direct comparison with the baseline to assess the extent and location of potential defects. The degree of deviation from the baseline indicates the compactness and defect severity, facilitating more accurate monitoring. Additionally, a user-friendly monitoring platform interface has been designed using Mock Plus, enabling rapid prototyping and optimization for enhanced data visualization and user interaction, this design allows for effective real-time monitoring, data processing, and user engagement. By integrating advanced machine learning techniques with intuitive platform design, the proposed method offers a significant improvement in monitoring concrete compactness, potentially benefiting both research and practical applications in structural health monitoring.
Field data collection is a crucial component of geological surveys in hydraulic engineering. Traditional methods, such as manual handwriting and data entry, are cumbersome and inefficient, failing to meet the demands of digital and intelligent recording processes. This study develops an intelligent speech recognition and recording method tailored for hydraulic engineering geology, leveraging specialized terminology and speech recognition technology. Initially, field geological work documents are collected and processed to create audio data through manual recording and speech synthesis, forming a speech recognition training dataset. This dataset is used to train and construct a speech-to-text recognition model specific to hydraulic engineering geology, including fine-tuning a Conformer acoustic model and building an N-gram language model to achieve accurate mapping between speech and specialized vocabulary. The model's effectiveness and superiority are validated in practical engineering applications through comparative experiments focusing on decoding speed and character error rate (CER). The results demonstrate that the proposed method achieves a word error rate of only 2.6% on the hydraulic engineering geology dataset, with a single character decoding time of 15.5ms. This performance surpasses that of typical speech recognition methods and mainstream commercial software for mobile devices, significantly improving the accuracy and efficiency of field geological data collection. The method provides a novel technological approach for data collection and recording in hydraulic engineering geology.
Blast-induced ground vibration, a by-product of rock fragmentation, presents significant challenges, particularly in areas adjacent to residential structures, where excessive vibration can cause structural damage and propagate cracks. This study proposes a novel framework integrating Principal Component Analysis (PCA) and Artificial Neural Networks (ANN) to predict Peak Particle Velocity (PPV), a critical metric for assessing ground vibration intensity. Field data were gathered from Singareni coal mines, capturing a range of blasting parameters, including burden, spacing, explosive quantity, and maximum charge per delay. PCA was employed to identify and retain the most influential variables, reducing dimensionality while preserving essential information. The optimised subset of features was subsequently used to train the ANN model. The model’s performance was evaluated using regression analysis, yielding a high coefficient of determination (R² = 0.92), indicating its robustness and accuracy in predicting PPV. A comparative analysis with conventional empirical equations demonstrated the superiority of the ANN model, which consistently provided more precise estimates of vibration intensity. The integration of PCA not only improved model performance but also enhanced computational efficiency by eliminating redundant parameters. This research underscores the potential of combining advanced statistical techniques with machine learning models to improve the predictability of blast-induced ground vibrations. The proposed framework offers a practical tool for mine operators to mitigate the environmental impact of blasting activities, particularly in sensitive areas.
The Smoothed Particle Hydrodynamics (SPH) method has been applied to solve the Boussinesq equations in order to simulate hypothetical one-dimensional dam break flows (DBFs) across varying depth ratios. Initial simulations reveal that the influence of Boussinesq terms remains minimal during the early stages of DBF when the depth ratio is less than 0.4. However, these terms become increasingly significant at later stages of the flow. In comparison to simulations based on the Saint-Venant equations, the Boussinesq-SPH model underestimates flow depths in regions of constant elevation while overestimating the propagation speed of the positive surge wave, with this overestimation becoming more pronounced as the depth ratio increases. Notably, the first and third Boussinesq terms exert the greatest influence on the simulation results. The findings also indicate the presence of non-hydrostatic pressure distributions within the DBF, which contribute to the accelerated movement of the positive surge. This study offers valuable insights into the modelling of flows that exhibit non-hydrostatic behaviour, and the results may be instrumental in improving the analysis of similar flow phenomena, especially those involving complex pressure distributions and wave propagation dynamics.
The Pearl River Delta Water Resources Allocation Project is characterized by an extensive distribution of buildings along a lengthy alignment and the application of diverse construction methodologies. Given these complexities, comprehensive safety monitoring measures are essential during both the temporary construction and operational phases to ensure the structural integrity and safety of the project. This study examines the critical aspects of safety monitoring, tailored to the unique characteristics and demands of the project, by focusing on the monitoring objectives, specific monitoring tasks, and the inherent challenges posed by the project's scope and variety. Emphasis is placed on identifying key safety monitoring difficulties, such as maintaining accuracy across varying construction methods and terrain conditions, and ensuring compliance with evolving regulatory standards. Additionally, innovative solutions and advanced monitoring techniques that address these challenges are explored, highlighting the integration of novel technologies and approaches that enhance monitoring effectiveness. The discussion is framed within the context of existing engineering requirements and regulatory frameworks, providing insights into the strategic implementation of safety monitoring protocols that are both adaptable and robust. This paper contributes to the ongoing discourse on the safety management of large-scale water resource projects by presenting a detailed analysis of the challenges encountered and the innovations employed to mitigate risks, thus supporting sustainable and safe development in complex engineering environments.
The scouring process, characterised by the erosion of sediment around bridge piers due to fluid flow, poses a significant risk to the structural integrity of bridges. Scour depth, defined as the vertical distance from the initial riverbed level to the bottom of the scour hole, is driven by the formation of vortices near bridge piers. Mitigating scour damage after it has advanced to a critical stage is often more disruptive and costly than preemptive measures based on accurate predictions. In response to this challenge, a range of one-dimensional (1D) and two-dimensional (2D) numerical modelling techniques has been developed for scour depth estimation around bridge piers. Among the available methods, the Hydrologic Engineering Center's River Analysis System (HEC-RAS) is widely employed, with the majority of studies focusing on the 1D modelling approach. The current study evaluates the relative efficacy of 1D and 2D models using the case of the Kelanisiri Bridge, which traverses the Kelani River in Sri Lanka. The performance of the 1D model was assessed by comparing predicted water levels at an intermediate river gauge with field data, while the 2D model was calibrated and validated against observed riverbed levels. Both approaches were applied to estimate scour depths following the 2016 flood event. The findings revealed that the 2D HEC-RAS model provided a superior match with observed field data when compared to the 1D model, achieving a coefficient of determination (R$^2$) of 0.98 and a root mean square error (RMSE) of 0.13, indicating a higher degree of accuracy and reliability. As a result, the 2D model is recommended as the more effective approach for predicting scour depth around bridge piers. Further validation of these numerical results through scaled laboratory physical modelling is recommended to ensure greater accuracy in future predictive efforts.
The substitution of cement with coal gangue powder (CGP) offers significant potential for energy conservation, emission reduction, and environmental sustainability. To optimize the mechanical properties of coal gangue cement paste, a modified response surface methodology (RSM) model was developed, incorporating grinding parameters as independent variables and compressive strength as the response variable. The feasibility of the model was validated through coefficient estimation, variance analysis, and fitting statistics. The analysis revealed that milling speed was the most significant factor influencing the compressive strength at 20% substitution, while the ball-to-material ratio predominantly affected the strength at 50% substitution. An increase in milling speed was observed to significantly broaden the particle size distribution, with larger particles (15.14$\mathrm{\mu m}$ to 275.42$\mathrm{\mu m}$) serving primarily as micro-aggregates, and smaller particles (0.32$\mathrm{\mu m}$ to 15.14$\mathrm{\mu m}$) functioning as fillers within ultra-fine pores. Scanning Electron Microscopy (SEM) further corroborated these findings. Numerical optimization based on the RSM model identified optimal grinding parameters: a ball-to-material ratio of 1.40, a milling time of 0.843 hours, and a milling speed of 300 rpm. These parameters are recommended to achieve the target compressive strengths of 25 MPa at 20% CGP substitution and 10 MPa at 50% CGP substitution. This study provides a cost-effective and feasible approach for the utilization of coal gangue in cementitious materials, contributing to the advancement of sustainable construction practices.
Rainwater harvesting (RH) techniques, specifically the implementation of Bio-pore Infiltration Holes (BIH), have been investigated as cost-effective and practical methods for managing surface runoff and mitigating flood risks. This study aimed to evaluate the infiltration rates of BIH in secondary forest and agricultural moorland areas, providing a basis for sustainable soil and water conservation practices. A survey methodology was employed to assess infiltration rates using the Horton equation model applied to circular holes with a depth of 50 cm. Soil samples were collected from the vicinity of the BIH for analysis of physical properties at the Soil Science Laboratory, Faculty of Agriculture, Tadulako University. A 4-inch diameter PVC pipe, inserted 30 cm into the soil, was used to measure water infiltration, with water levels recorded up to 60 cm. The findings indicated that infiltration rates in both secondary forest and agricultural lands were moderate. The physical characteristics of the soil, including its texture and organic carbon content, were identified as suboptimal, which constrained the efficiency of waste absorption through the infiltration process. The soil texture in both land types was classified as sandy according to USDA standards, making it susceptible to erosion, which is directly related to the infiltration capacity and the potential for soil transport during erosion events. The carbon organic content was relatively low, at 2.50% in secondary forest land and 1.17% in agricultural land, indicating medium-level criteria for organic content. To enhance soil conservation and flood mitigation, it is recommended that efforts be made to increase organic material content through compost application and post-flood land rehabilitation. Expanding the use of BIH in high-risk flood areas is advocated to effectively reduce and control surface runoff.
The fatigue life of H-type rigid hangers, crucial components in bridge engineering, is investigated in this study, particularly under the influence of torsional vibrations induced by wind loads. These hangers, integral to the integrity and longevity of bridge structures, are characterized by their high aspect ratio and low torsional stiffness, which predispose them to fatigue under such conditions. The focus of the research is the hangers of Dongping Bridge, located in Foshan, Guangdong. Through the application of theoretical analysis and finite element simulation using ABAQUS, the effects of bolting actions were simulated using connector elements, which enhanced computational efficiency and facilitated the stress analysis at the bolt holes in node plates. Furthermore, fe-safe fatigue analysis software was utilized to evaluate the fatigue life, adhering to established guidelines. The findings reveal that selecting an appropriate stiffness for the connector elements is critical in accurately simulating the bolting action. It was determined that the torsional amplitude at mid-span is a viable indicator for assessing fatigue damage. A torsional vibration control threshold of 6.25° is recommended for hangers measuring 40.212 meters in length.
To address the lack of clear formulae for calculating the circumferential stress in steel epoxy sleeve-reinforced pipelines under internal pressure, this study constructs a mechanical model based on the specific stress characteristics of these pipelines. Using stress solution methods and deformation compatibility relationships, theoretical formulas for circumferential stress in the pipeline layer, epoxy resin layer, and sleeve layer under internal pressure are derived. The theoretical formulas are validated through numerical simulations using ANSYS software, which includes models with and without flanges. The calculations were performed for common pipelines with outer diameters of 219mm, 660mm, and 1219mm. The results show that the discrepancies between theoretical and numerical solutions of circumferential stress in all layers of both model types are within 10%. Specifically, the circumferential stress in the pipeline layer of the flanged model is lower than that of the non-flanged model and also lower than the theoretical values. The error between the theoretical and numerical solutions for pipelines of different diameters does not exceed 10%, confirming the validity and applicability of the theoretical formulas. This suggests that using the simplified mechanical model for circumferential stress calculations ensures a conservative approach for the structural assessment of pipelines. The formulas provided herein can serve as a reference for the design and evaluation of steel epoxy sleeve-reinforced pipelines under internal pressure.
This study investigates the stability of steel columns subjected to axial compression, focusing on square hollow sections (SHS) with both uniform and non-uniform cross-sections. The stability of fixed-free end SHS columns with uniform cross-sections was initially verified using analytical equations. To obtain the critical load and design buckling resistance for each SHS column, Finite Element Analysis (FEA) was employed. The results indicate that while analytical equations can validate the stability of uniform SHS columns, they are insufficient for columns with non-uniform cross-sections. Consequently, the FEA emerges as a robust alternative for analyzing columns with varying cross-sections along their length. This study highlights the necessity of numerical methods for verifying the stability of structurally complex columns, such as those with perforations for mechanical and electrical applications. The finite element model was validated and applied to non-uniform cross-section columns, providing insights into the stability of these columns under practical conditions. This research aims to offer an alternative analytical approach for structural engineering applications where column stability is critical, especially for non-uniform cross-sectional designs that facilitate handling processes in various engineering scenarios.
Recent observations of global warming phenomena have necessitated the evaluation of the service performance of asphalt pavements, which is substantially influenced by surface temperature levels. This study employed twelve distinct machine learning algorithms—K-neighbors, linear regression, multi-layer perceptron, lasso, ridge, support vector regression, decision tree, AdaBoost, random forest, extra tree, gradient boosting, and XGBoost—to predict the surface temperature of asphalt pavements. Data were sourced from the Road Weather Information System of Iowa State University, comprising 12,581 data points including air temperature, dew point temperature, wind speed, wind direction, wind gust, and pavement sensor temperature. These data were segmented into training (80%) and testing (20%) datasets. Analysis of model outcomes indicated that the Extra Tree algorithm was superior, exhibiting the highest R$^2$ value of 0.95, whereas the Support Vector Regression algorithm recorded the lowest, with an R$^2$ value of 0.70. Furthermore, Shapley Additive Explanations were utilized to interpret model results, providing insights into the contributions of various predictors to model outcomes. The findings affirm that machine learning algorithms are effective for predicting asphalt pavement surface temperatures, thereby supporting pavement management systems in adapting to changing environmental conditions.
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