Detailed Understanding of Roman concrete requires context from Roman military and civil engineering. The Romans prioritized durable infrastructure due to the impracticality of maintaining temporary wooden structures across their vast empire. This led to the development of long-lasting roads, bridges, and fortifications, many of which still exist today. Roman construction techniques, including concrete use, evolved significantly over time. Although Vitruvius documented early methods in the 1st century BC, later advancements—such as “hot mixing”—were not included in his texts. Roman concrete’s durability, especially in late Empire formulations, contributed to its longevity and continued use through the medieval period. In modern times, concrete construction shifted towards heavily reinforced structures, often without adequate protection. This has led to durability issues, highlighted by events like the collapse of the Morandi Bridge. In contrast, Roman concrete demonstrates superior longevity and self-healing properties despite being unreinforced. The study of Roman concrete offers valuable insights for modern construction, suggesting that minimally reinforced or unreinforced methods inspired by Roman practices could enhance durability and sustainability.
Traditional tensioning monitoring techniques for prestressed concrete structures often exhibit limitations in real-time performance, accuracy, and adaptability to complex stress distributions. To address these challenges, an intelligent monitoring framework is developed based on a Radial Basis Function (RBF) neural network. Using the Dongjiacun aqueduct as a case study, a comprehensive methodology is established, integrating numerical simulation, Machine Learning (ML), and real-time data processing. Initially, Finite Element Analysis (FEA) is conducted to simulate stress distribution during the tensioning process, allowing for the extraction of critical stress data at key structural locations. These data serve as the foundation for training the RBF neural network, which functions as a high-fidelity surrogate model capable of efficiently predicting stress variations with enhanced accuracy. By leveraging the network's strong generalization capabilities, the proposed framework ensures rapid and precise estimation of stress evolution throughout the tensioning process. Furthermore, an intelligent monitoring platform is designed, incorporating real-time data acquisition, automated stress prediction, and visualization functionalities. The platform facilitates prestress control and structural health assessment, contributing to the long-term safety and durability of prestressed concrete structures. Additionally, an interactive user interface is prototyped using Mock Plus to enhance usability and facilitate intuitive interpretation of stress-related insights. The proposed approach not only advances the automation and intelligence of tensioning monitoring but also provides a robust technical foundation for optimizing prestress management in large-scale infrastructure applications.
The accurate estimation of the longitudinal dispersion coefficient is crucial for predicting solute transport in natural water bodies. In this study, an analytical (integral) method based on first principles is compared with Fischer’s widely used empirical approach, which is implemented in hydraulic modeling software such as the Hydrologic Engineering Center-River Analysis System (HEC-RAS). The primary objective is to evaluate the accuracy, applicability, and limitations of both methods under varying hydraulic conditions. A key advantage of the analytical approach is its ability to estimate the dispersion coefficient using velocity data alone, eliminating the need for high-cost tracer experiments that rely on solute concentration measurements. The determination index suggests an acceptable level of agreement between the two methods; however, the empirical approach systematically overestimates dispersion coefficients. Furthermore, a clear inverse relationship is observed between the slope of the channel and the magnitude of the dispersion coefficient, which is attributed to the increasing influence of shear velocity on the diffusion process. As slope values increase, solute separation time decreases, and concentration gradients become steeper. Conversely, at lower slopes, solute dispersion occurs over a broader time frame, resulting in lower concentration peaks. These findings indicate that while Fischer’s method provides a robust empirical framework, it should be supplemented with field measurements to improve reliability. In contrast, the analytical method offers a more theoretically grounded alternative that may enhance predictive accuracy in solute transport modeling. The implications of these results extend to water quality management, contaminant transport studies, and hydraulic engineering applications, where the selection of an appropriate dispersion estimation method significantly influences predictive outcomes.
The effects of polycarboxylate superplasticizer (PCE) on the rheological properties and workability of cement-based composites were investigated by testing parameters such as static yield stress, dynamic yield stress, plastic viscosity, slump flow, bleeding rate, and penetration depth. The correlation between the dosage of PCE and the rheological parameters of fresh cement-based composites was analyzed. The results indicated that with an increase in the PCE dosage, the static yield stress, dynamic yield stress, and plastic viscosity of fresh cement-based composites decreased, demonstrating that PCE can improve the rheological properties of these composites. As the PCE dosage increased, the slump flow and bleeding rate of fresh cement-based composites also increased, but the rate of change decreased at higher dosages. Additionally, with an increase in PCE dosage, the penetration depth gradually increased, while the penetration depth difference ($\Delta {H}$) decreased. Furthermore, the compressive strength of cement-based composite cubes slightly decreased with an increase in PCE dosage.
The instability of mining waste dumps poses significant environmental hazards, including loss of life, damage to infrastructure, and ecological degradation. The complex interdependence of Thermal, Hydraulic, and Mechanical (THM) processes has been increasingly recognised as a critical factor influencing slope stability. In this study, a coupled THM numerical model was developed using the finite element method (FEM) to evaluate slope stability in a coal mine waste dump in Maamba, Zambia. Key parameters, including stress distribution, displacement, pore water pressure, and temperature variations, were incorporated to achieve a comprehensive assessment of slope failure mechanisms. Field data and geotechnical investigations were integrated with advanced computational simulations to ensure realistic modelling. The findings demonstrated that conventional limit equilibrium methods (LEM) underestimated the impact of coupled processes on slope failure. The safety factor was observed to decrease by more than 30% due to THM interactions, with thermal gradients and hydro-mechanical (H-M) responses identified as primary contributors to slope instability. The results underscore the necessity of incorporating THM coupling in slope stability assessments, particularly in geotechnically sensitive mining environments. The proposed framework provides a scientifically grounded methodology for evaluating and mitigating landslide risks in mining waste dumps, offering valuable insights applicable to regions with similar geotechnical and climatic conditions. The findings contribute to the refinement of slope stability management strategies and provide a basis for the development of risk mitigation measures in vulnerable mining areas.
The impact of artificial hailstones on G300 steel sheets with varying thicknesses has been systematically investigated to evaluate the resulting dent depths. Two distinct methods for producing simulated hailstones were employed: one utilizing polyvinyl alcohol (PVA) adhesive and the other incorporating liquid nitrogen. Comparative analyses of these techniques revealed that the liquid nitrogen method, in conjunction with demineralized water, yielded more accurate results than the PVA adhesive-based method. Experimental findings were cross-referenced with theoretical predictions and finite element simulations, with model accuracy being validated against existing research in the field. The study focused on three hailstone diameters—38mm, 45mm, and 50mm—across various sheet thicknesses. Results indicate that dent depth is primarily influenced by the impact energy, sheet metal thickness, and hailstone diameter. Notably, the momentum of the hailstone plays a critical role, with smaller, higher-momentum hailstones inducing permanent deformations comparable to those of larger, lower-momentum hailstones, even when the impact energies are equivalent. The findings suggest that variations in hailstone momentum can lead to similar deformation patterns across different sizes, emphasizing the importance of momentum in the design of steel sheet materials for enhanced hailstone impact resistance. This study contributes valuable insights for the development of more resilient materials in industries subject to dynamic impact loading, such as automotive and aerospace engineering.
This study investigates the effect of magnesium hydroxide Mg(OH)$_2$ flame retardant treatment on corn stalk fiber and its impact on the properties of fiber asphalt when mixed at a 3% concentration with asphalt. The study examines the changes in fiber aspect ratio and microscopic morphology before and after flame retardant treatment, and explores the underlying mechanisms that influence the basic performance of fiber asphalt. The effects of flame retardant-treated corn stalk fibers on the asphalt binder were assessed using tests such as softening point, penetration, elongation, temperature scanning, and bending beam rheometer. The results indicate that as the concentration of magnesium hydroxide increases, the three main indicators of the fiber asphalt binder first increase and then decrease. The highest softening point (49.8°C) occurred at a concentration of 2%, the highest penetration (7.6mm) at 1%, and the highest elongation (12.7cm) at 1%. The high and low-temperature performance tests show that the fiber asphalt binder made with 1% magnesium hydroxide-treated corn stalk fibers achieves the best balance of both high and low-temperature properties.
In urban environments, the scarcity of available land often necessitates the construction of closely spaced, high-rise buildings, which rely heavily on pile foundations to support substantial loads. However, the pile-driving process, essential for such foundations, generates vibrations that can propagate through the ground and affect surrounding structures, potentially leading to adverse consequences. These vibrations can disrupt the comfort of residents and cause structural damage to adjacent buildings, including residential properties, hotels, and hospitals, where both the comfort and safety of occupants are of paramount importance. Furthermore, pile-driving-induced vibrations can result in the development of cracks in the architecture, settlement of foundations, or even severe structural failure in sensitive installations. To assess the effects of pile-driving on nearby buildings, a series of 77 finite element models were developed using PLAXIS 3D, which simulated varying pile-to-building distances and driving depths. The analyses focused on both the comfort of residents and the structural integrity of adjacent buildings, with comparisons drawn against international standards for vibration levels. The results revealed that the optimal driving depth could effectively minimize peak vibration levels, thereby reducing the risk of disruption to nearby structures. Additionally, the influence of parameters such as pile-driving load, pile penetration depth, and soil characteristics on vibration propagation was systematically explored. The findings provide critical insights into the mitigation of pile-driving-induced vibrations in urban settings and offer guidance for optimizing pile-driving operations to safeguard both resident comfort and structural safety.
Cutoff walls are an essential method for seepage prevention in dams. During the construction and operation of reservoirs, factors such as construction techniques, variations in groundwater conditions within the dam body, geological movements, and climatic factors may lead to potential seepage risks, necessitating inspection. Traditional methods like borehole coring and water pressure tests have limited monitoring ranges, while non-destructive methods like high-density electrical surveys and shallow seismic exploration have low deep-resolution capabilities, making them unsuitable for detecting deep-seated seepage in concrete walls. In recent years, Cross-borehole Tomography (CT) geophysical techniques, based on boreholes on both sides, have been widely applied in various engineering geophysical projects. Seepage in cutoff walls can lead to an increase in local moisture content, resulting in low-resistivity anomalies, providing a physical basis for the exploration using cross-borehole resistivity CT. This study investigates the resistivity response characteristics of cross-borehole resistivity CT through numerical simulation based on the resistivity characteristics of seepage in cutoff walls. The numerical simulation results indicate that this method effectively identifies seepage conditions in cutoff walls, and the resolution of cross-borehole resistivity CT is significantly related to the cross-hole spacing and the distance to the seepage points. This study provides a preliminary verification of the feasibility of applying cross-borehole resistivity CT for detecting seepage in cutoff walls and offers insights for seepage detection strategies.
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.
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