Field Monitoring Research Articles

High pre-tension reinforcing technology and design for ultra-shallow buried large-span urban tunnels

Due to the thin overlying formation of ultra-shallow buried large-span urban tunnels, the surrounding rock can be easily loosened and damaged after excavation. High prestressed anchoring support can improve the self-bearing capacity of the surrounding rock. However, traditional bolts often undergo necking fracture after the yield stage and strengthening stage, and the designed pre-tension is low, generally not exceeding 50 % of the yield strength of the bolt. To this end, our research group developed a new NPR bolt. After comparing the mechanical properties of the NPR bolt and the traditional bolt, the high strength, high elongation, and high prestressed properties of the NPR bolt are revealed. Subsequently, the constitutive relationship of the CABLE element in FLAC3D is modified, and the constitutive model of the NPR bolt is established, which can describe the whole process of bolt. Furthermore, a numerical comparison of prestressed control for ultra-shallow large-span tunnels is carried out. Compared with non-prestress, when the prestress of bolt reaches 100 kN, the area of tensile stress zone and plastic zone of surrounding rock are reduced by 79.6 % and 73.7 %, respectively. At the same time, the settlement of surface and roof is reduced by 66.7 % and 64.1 %, respectively. The control mechanism of the prestressed NPR bolt support is explored, and the design method for ultra-shallow large-span tunnels is proposed. The field application and monitoring results show that the surface and roof settlement of the tunnel supported by NPR bolts are 3.2 mm and 6.3 mm, respectively. The safe and effective stability control of the tunnel surrounding rock is achieved.

Determination of the Advanced Mining Influence Range in Coal Mines Based on the Statistical Analysis of Mining-Induced Seismicity

Determining the advanced mining influence range of an underground working face is crucial for preventing dynamic disasters, such as coal bursts and gas outbursts. In this study, the occurrence of advanced seismicity before the working face as well as its correlation with the acoustic emission (AE) activity of coal and rocks under axial loading was analyzed. Based on the results, a novel statistical method to determine the advanced mining influence range based on advanced seismicity data was proposed and then validated with a case study. The results show that advanced seismicity is caused by the combined effects of static and dynamic stresses at the working face. This seismicity can be used to assess the mining influence degree of the working face on the advanced coal and rock mass, and determine the advanced mining influence range. Using the novel statistical method, the normalized curves for the total number and total energy of the advanced mining-induced seismicity can be plotted. Then, the advanced mining influence range can be determined using thresholds. The thresholds can be established based on the AE activities observed in coal and rock samples under axial static loading. In the case study in this research, the thresholds for the total seismic number and total seismic energy are 0.076 and 0.052, respectively. The corresponding advanced mining influence ranges are 275 m and 245 m, respectively. Field monitoring confirms an advanced mining influence range of 255 m, which validates the results obtained using the novel statistical method.

Deformation analysis of ground and existing tunnel induced by overlapped curved shield tunneling

The deformation caused by tunnel excavation is quite important for safety, especially when it is adjacent to the existing tunnel. Nevertheless, the investigation of deformation characteristics in overlapped curved shield tunneling remains inadequate. The analytical solution for calculating the deformation of the ground and existing tunnel induced by overlapped curved shield tunneling is derived by the Mirror theory, Mindlin solution and Euler-Bernoulli-Pasternak model, subsequently validated through both finite element simulation and field monitoring. It is determined that the overcutting plays a crucial role in the ground settlement resulting from curved shield tunneling compared to straight shield tunneling. The longitudinal settlement distribution can be categorized into five areas, with the area near the tunnel surface experiencing the most dramatic settlement changes. The deformation of the existing tunnel varies most significantly with turning radius compared to tunnel clearance and grouting pressure, especially when the turning radius is less than 30 times the tunnel diameter. The tunnel crown exhibits larger displacement than the tunnel bottom, resulting in a distinctive 'vertical egg' shape. Furthermore, an optimized overcutting mode is proposed, involving precise control of the extension speed and angular velocity of the overcutting cutter, which effectively mitigates ground deformation, ensuring the protection of the existing tunnel during the construction.

Climate impacts on vegetated geotechnical infrastructures

Eco-geotechnical engineering plays a pivotal role in enhancing global sustainability and upholding the performance of earthen structures. The utilization of vegetation to stabilise geotechnical infrastructures is widely recognized and embraced for its environmentally friendly attributes. The spectre of climate change further intensifies focus on the effects of temperature and humidity on vegetated soil. Consequently, there is a pressing need for research exploring the influence of changing climates on vegetated infrastructures. Such research demands a holistic and interdisciplinary approach, bridging fields such as soil mechanics, botany, and atmospheric science. This review underscores key facets crucial to vegetated geotechnical infrastructures, encompassing climate projections, centrifuge modelling, field monitoring, and numerical methodologies.

Effect of super-high water materials backfilling on stress decrease and energy release during strip coal pillar mining: A case study

Mining strip coal pillars left due to strip mining is important to resource-exhausted coal mines in eastern China. Backfilling mining is an effective means to mine strip coal pillars, which could decrease high stress and high energy release. However, materials with super-high water content were not widely applied in deeply isolated coal pillar mining due to lower strength characteristics and durability. In the paper, taking panel c8301 as engineering background, theoretical analysis, numerical simulation, and field monitoring were used to study the feasibility and application effect of the super-high-water backfilling technology in deep isolated coal pillar mining. The results showed that: (1) Panel c8301 is a strip-filling working face with insufficient mining on both sides, and the mining of the panel will trigger the rebalancing of the overlying rock structure on both sides, which increases the rock burst risk. (2) Simulation results indicate that the super-high-water filling method, in comparison to the traditional caving method, significantly reduces peak stress and elastic energy from 112.3 MPa to 4.6 × 106 J to 79.2 MPa and 2.23 × 106 J, respectively. This represents reductions of 29.6 % and 51.5 %, effectively mitigating the impact of mining activities on overburden movement. (3) On-site measurement data confirmed that the measured equivalent mining height was 1.25 m. The total number of microseisms and the amount of released energy decreased significantly, More specifically, three big energy release instances (>1.8 × 105 J) were recorded during the first roof weighting stage and panel in square meters. Super-high-water filling technology has achieved remarkable results in the mining of strip coal pillars and has significant application prospects.

Migration and natural attenuation of leachate pollutants in bedrock fissure aquifer at a valley landfill site

Groundwater pollution from valley type landfills is concerning, and natural attenuation by contaminants is increasingly relied upon. However, the reliability of natural attenuation in such complex sites has been called into question due to incomplete understanding of their attenuation mechanisms. Therefore, we conducted field investigations, monitoring analyses, mathematical statistics, and machine learning techniques to elucidate the natural attenuation mechanisms of pollutants within bedrock fissures at a prototypical valley type landfill located in the east Yanshan Mountains, China. Our results indicate that 50% of the monitored indicators showed extreme pollution in bedrock fissure aquifers, due to seepage from the valley type landfill site. Ammonia nitrogen, arsenic, cadmium, lead, iron, manganese, and mercury were among the contaminants that could pose serious risks to human health. Pollutant concentrations in bedrock fissure aquifers were lower during the rainy season compared to the dry season as the aquifer was rapidly recharged by strong rainfall runoff. The initial concentration of bedrock fissure water generally increased during the flow through the landfill. However, significant natural attenuation of total dissolved solids, oxygen consumption, ammonia, cadmium, and lead occurred after passing through the landfill (p<0.05), with attenuation coefficients of 0.0041 m-1, 2.56×E-5m-2, 4.18×E-5m-2、0.0015 m-0.99, and 6.83×E-33m-12.49, respectively. The driving mechanisms for natural attenuation include physical migration, leaching, microbiological degradation, and adsorption, primarily occurring within 600-650 m downstream of the landfill boundary. This study makes fundamental contribution to the understanding of the migration and natural attenuation process of leachate pollutants in bedrock fissure aquifer, which will provide a scientific basis for implementation of natural attenuation strategies in complex site remediation. Future research should examine more precise evidence of natural attenuation feasibility in complex sites in conjunction with monitoring networks.

Large deformation problems arising from deep excavation in silt strata: A case study in Shenzhen, China

Deep excavations in silt strata can lead to large deformation problems, posing risks to both the excavation and adjacent structures. This study combines field monitoring with numerical simulation to investigate the underlying mechanisms and key aspects associated with large deformation problems induced by deep excavation in silt strata in Shenzhen, China. The monitoring results reveal that, due to the weak property and creep effect of the silt strata, the maximum wall deflection in the first excavated section (Section 1) exceeds its controlled value at more than 93% of measurement points, reaching a peak value of 137.46 mm. Notably, the deformation exhibits prolonged development characteristics, with the diaphragm wall deflections contributing to 39% of the overall deformation magnitude during the construction of the base slab. Subsequently, numerical simulations are carried out to analyze and assess the primary factors influencing excavation-induced deformations, following the observation of large deformations. The simulations indicate that the low strength of the silt soil is a pivotal factor that results in significant deformations. Furthermore, the flexural stiffness of the diaphragm walls exerts a notable influence on the development of deformations. To address these concerns, an optimization study of potential treatment measures was performed during the subsequent excavation of Section 2. The combined treatment approach, which comprises the reinforcement of the silt layer within the excavation and the increase in the thickness of the diaphragm walls, has been demonstrated to offer an economically superior solution for the handling of thick silt strata. This approach has the effect of reducing the lateral wall displacement by 83.1% and the ground settlement by 70.8%, thereby ensuring the safe construction of the deep excavation.

Reproducing vortex-induced vibrations of rooftop twin-mast by multi-scale coupled simulation of urban wind fields

The Shenzhen Electronics Group (SEG) Plaza in Shenzhen, China, experienced visible vibrations attributed to significant vortex-induced vibrations of the large spacing twin-mast atop the building. The vibration event occurred under complex urban wind environments. In order to reproduce wind fields around SEG Plaza, a multiscale simulation was carried out by integrating the Weather Research and Forecasting (WRF) and Computational Fluid Dynamics (CFD) models. The aerodynamic characteristics and wake modes of twin-mast are investigated by a downscale CFD simulation in detail. Finally, vortex-induced vibration (VIV) of twin-mast was analyzed considering fluid-structure interaction. This work reveals that wind conditions during the SEG Plaza vibration incident were appropriate for the VIV occurrence of twin-mast, and the results of VIV simulation agree well with the field monitoring results by computer vision identification. This study effectively reproduces the full pictures of VIVs of the rooftop twin-mast during this rare event, offering a critical view for the analysis of this event.

Grouting characteristic in rock fractures with different structural properties: Apparatus design and experimental study

Rock fracture surfaces are normally rough, with upper and lower surfaces presenting obvious structural properties (i.e., matching or mismatching). In this paper, rock fracture was divided into shear and tensile fractures based on its mechanical origin. A test simulation system for grouting in rough fracture (tensile and shear fractures) with continuous pressure field monitoring was proposed. A series of grouting simulation tests were employed to evaluate the effect of fracture structural property, fracture surface roughness, and aperture on the characteristics of grout flow in the rough fracture. The results show that grouting pressure exhibited a trend of rapid increase followed by a slow increase with time during the grouting in the tensile and shear fractures. Grouting pressure increases with time in a smooth relationship for tensile fracture and a fluctuating upward relationship for shear fracture. The slurry pressure distribution along the penetration length exhibited a nonlinear attenuation trend. The attenuation degree of the slurry pressure was positively related to the fracture roughness and the roughness difference in the upper–lower fracture surface and negative with the fracture aperture. The potential filtration effect during the grouting process is an important factor affecting the grouting behavior in the rough fracture.

IoT-based Smart Agriculture Field Monitoring using Arduino Nano

The rapid developments in the Internet of Things (IoT) have enabled even individuals with limited technical knowledge to access real-time reception of information conveniently at their doorstep. Agriculture, one of the necessities for human livelihood, has been significantly enhanced with technological advancements. The study proposes an IoT-based smart agriculture field monitoring system using Arduino Nano. The device provides real-time data on temperature and soil moisture content for cultivating crops like paddy, tapioca, turmeric, etc. The system also allows farmers to get remote access to the irrigation systems, hence improving the management of the fields along with their resources.

An Effective Res-Progressive Growing Generative Adversarial Network-Based Cross-Platform Super-Resolution Reconstruction Method for Drone and Satellite Images

In recent years, accurate field monitoring has been a research hotspot in the domains of aerial remote sensing and satellite remote sensing. In view of this, this study proposes an innovative cross-platform super-resolution reconstruction method for remote sensing images for the first time, aiming to make medium-resolution satellites capable of field-level detection through a super-resolution reconstruction technique. The progressive growing generative adversarial network (PGGAN) model, which has excellent high-resolution generation and style transfer capabilities, is combined with a deep residual network, forming the Res-PGGAN model for cross-platform super-resolution reconstruction. The Res-PGGAN architecture is similar to that of the PGGAN, but includes a deep residual module. The proposed Res-PGGAN model has two main benefits. First, the residual module facilitates the training of deep networks, as well as the extraction of deep features. Second, the PGGAN structure performs well in cross-platform sensor style transfer, allowing for cross-platform high-magnification super-resolution tasks to be performed well. A large pre-training dataset and real data are used to train the Res-PGGAN to improve the resolution of Sentinel-2’s 10 m resolution satellite images to 0.625 m. Three evaluation metrics, including the structural similarity index metric (SSIM), the peak signal-to-noise ratio (PSNR), and the universal quality index (UQI), are used to evaluate the high-magnification images obtained by the proposed method. The images generated by the proposed method are also compared with those obtained by the traditional bicubic method and two deep learning super-resolution reconstruction methods: the enhanced super-resolution generative adversarial network (ESRGAN) and the PGGAN. The results indicate that the proposed method outperforms all the comparison methods and demonstrates an acceptable performance regarding all three metrics (SSIM/PSNR/UQI: 0.9726/44.7971/0.0417), proving the feasibility of cross-platform super-resolution image recovery.

Agricultural Field Monitoring Robot: Using IoT

Abstract: The agricultural field monitoring robot is a big step forward in precision farming. It uses technology like path memorization and smart IoT to help farmers manage their fields better. The robot can move around the farm on its own, remembering where it's been to do tasks more efficiently later. It's built on a platform-Arduino car system, which is controlled via Bluetooth. The smart IoT part comes from a platform called BlynkIOT, this system gathers data in real-time, helping farmers keep track of things like crop health and environmental conditions. With these technologies combined, the robot can do things like planting seeds, watering crops, and checking their health without needing much help from humans. Overall, it's meant to help farmers work smarter, save money on labor, and farm in a more sustainable way.

Closing the gap in the tropics: the added value of radio-occultation data for wind field monitoring across the Equator

Closing the gap in the tropics: the added value of radio-occultation data for wind field monitoring across the Equator

Temperature prediction of submerged arc furnace in ironmaking industry based on residual spatial-temporal convolutional neural network

The submerged arc furnace is widely regarded as one of the most promising ore smelting technologies. However, the real-time monitoring of the multiple physical fields including electric, thermal, and mas, through computational fluid dynamics demands significant computational resources. This paper introduces the spatial-temporal convolutional neural network algorithm to address this challenge. Initially, the influences of various working conditions on these physical fields are analyzed. Subsequently, a prediction model is developed based on the coupling of these multiple physical fields model. The spatial-temporal convolutional neural network algorithm is then employed to elucidate the main parameter distributions, enabling the automatic real-time detection of temperature variation trends and providing a theoretical foundation for intelligent furnace operation. The findings indicate that the electric field is the predominant factor causing non-uniform heat distribution, with localized overheating primarily occurring at the electrode ends. The application of the proposed model facilitates dynamic prediction of the temperature distribution, establishing relationships between historical and future time steps as well as local and global temperature variations. The reliability of the temperature prediction model is confirmed, with the model achieving an accuracy of 99.76 %, surpassing the 98.18 % accuracy of the traditional multi-layer perceptron model.

Deep Learning-Based Calculation Method for the Dry Beach Length in Tailing Ponds Using Satellite Images

The dry beach length determines the hydraulic boundary of tailings impoundments and significantly impacts the infiltration line, which is crucial for the tailings dam. A deep learning method utilizing satellite images is presented to recognize the dry beach area and accurately measure the length of dry beaches in tailing ponds. Firstly, satellite images of various tailing ponds were gathered and the collection was enlarged to create a dataset of satellite images of tailing ponds. Then, a deep learning method was created using YOLOv5-seg to identify the dry beach area of tailing ponds from satellite images. The mask of the dry beach region was segmented and contour extraction was then carried out. Finally, the beach crest line was fitted based on the extracted contour. The pixel distance between the beach crest line and the dry beach boundary was measured and then translated into real distance by ground resolution. This paper’s case study compared the calculated length of dry beach with the real length obtained by field monitoring. The results of the case study showed that the minimum error of the method was 2.10%, the maximum error was 3.46%, and the average error was 2.70%, indicating high precision for calculating dry beach length in tailing ponds.

Progressive deformation mechanism of colluvial landslides induced by rainfall: insights from long-term field monitoring and numerical study

Progressive deformation mechanism of colluvial landslides induced by rainfall: insights from long-term field monitoring and numerical study

Miniaturized photoelectrochemical sensing system for reusable detection of macromolecules and its applications for unattended environmental monitoring

Photoelectrochemical (PEC) sensors are widely employed in biochemical detection due to their accuracy and speed, but most PEC measurement systems face challenges with bulky instruments and complex operations. Current miniaturized systems based on Potentiostat and constant light are limited in accuracy and application ranges. PEC systems based on mod/demod signal processing offer high SNR and accuracy but struggles with miniaturization due to complex algorithms of Lock-in amplifier. Herein, discrete Fourier transform (DFT) algorithm instead of Lock-in algorithm was used for the photocurrent demodulation in PEC system. After functional verification, coordinate rotation digital computer (CORDIC) algorithm was used to implement DFT demodulation to further reduce resource occupancy. Afterwards, the spectral leakage was reduced by applying Hanning window, which improves the SNR and accuracy of photocurrent demodulation. Subsequently, a miniaturized, low-cost, low-power and high-accuracy PEC measurement system was obtained by integrating designed Hanning-CORDIC-DFT module, Potentiostat, light source driver and other miniaturized modules. The performances of miniaturized PEC measurement system were compared with that of the commercial PEC measurement system based on Lock-in amplifier by using the same MIP-PEC sensor, validating the feasibility and accuracy of designed miniaturized PEC system. Then, the performance of MIP-PEC sensor was investigated in detail. The results showed that MIP-PEC sensor has good sensitivity, stability, selectivity and recyclability for detecting MC-LR. The reliability of MIP-PEC sensor was further confirmed by the recovery test of MC-LR in lake water. Finally, long-term, cyclic and unattended measurements of microcystin-LR were achieved by designed miniaturized PEC system and MIP-PEC sensor under different conditions, verifying the practicality of the system for field monitoring. The designed measurement system provides a new method for constructing miniaturized PEC sensing platform, and a new solution for continuous biochemical detection in complex environments.

ANN-Based Bridge Support Fixity Quantification Using Thermal Response Data from Real-Time Wireless Sensing.

Bridges are critical infrastructures that support our economic activities and daily lives. Aging bridges have been a major issue for decades, prompting researchers to improve resilience and performance through structural health monitoring. While most research focuses on superstructure damage, the majority of bridge failures are associated with support or joint damages, indicating the importance of bridge support. Indeed, bridge support affects the performance of both the substructure and superstructure by maintaining the load path and allowing certain movements to mitigate thermal and other stresses. The support deterioration leads to a change in fixity in the superstructure, compromising the bridge's integrity and safety. Hence, a reliable method to determine support fixity level is essential to detecting bearing health and enhancing the accuracy of the bridge health monitoring system. However, such research is lacking because of its complexity. In this study, we developed a support fixity quantification method based on thermal responses using an Artificial Neural Network (ANN) model. A finite element (FE) model of a representative highway bridge is used to derive thermal displacement data under different bearing stiffnesses, superstructure damage, and thermal loading. The thermal displacement behavior of the bridge under different support fixity conditions is presented, and the model is trained on the simulated response. The performance of the developed FE model and ANN was validated with field monitoring data collected from two in-service bridges in Connecticut using a real-time Wireless Sensor Network (WSN). Finally, the support stiffnesses of both bridges were predicted using the ANN model for validation.

Over 25-year monitoring of the Tsing Ma suspension bridge in Hong Kong

AbstractBridges in service are subjected to environmental and load actions, but their status and conditions are typically unknown. Health monitoring systems have been installed on long-span bridges to monitor their loads and the associated responses in real time. Since 1997, the Tsing Ma suspension bridge in Hong Kong has been the world’s first of the type equipped with a long-term health monitoring system. For the first time, this study reports the first-hand field monitoring data of the bridge from 1997 to 2022. The 26-year data provide an invaluable and rare opportunity to examine the long-term characteristics of the loads, bridge responses, and their relationships, thereby enabling the assessment of the bridge’s load evolution and structural condition over time. Results show that traffic loads have remained stable after 2007, highway vehicles kept increasing until the COVID-19 pandemic in 2020, the annual maximum deck temperature continued to increase at a rate of 0.51 °C/decade, typhoon durations increased by 2.5 h/year, and monsoon speeds decreased and became dispersed and variable. For the bridge responses, deck displacement is governed by the varying temperature. Natural frequencies in the past 26 years were almost unchanged. The overall condition of the bridge is very satisfactory. Current status and recent update of the health monitoring system are also reported. Lastly, prospects of bridge health monitoring are discussed. This study is the first to report the over one-quarter century status of a structural health monitoring system and the behavior of a long-span suspension bridge. This research provides a benchmark for many other bridge monitoring systems worldwide.

The effect of redox fluctuation on carbon mineralization in riparian soil: An analysis of the hotspot zone of reactive oxygen species production

Riparian zones are important depositional environments at the catchment scale and provide environmental services such as carbon sequestration. This zone is a highly dynamic interface for oxygen and electron exchange, which confers the basis for reactive oxygen species (ROS) production. However, the differences in soil ROS production and their impact on carbon turnover across various redox locations within the riparian zone remain to be fully elucidated. In this study, we investigated the distribution characteristics and generation mechanism of ROS in riparian soil based on soil samples collected in a three-month field monitoring experiment, with additional incubation experiments conducted to examine the effect of hydroxyl radical (•OH) on soil organic carbon (SOC) mineralization. The obtained results demonstrated that the riverine wetland was the hotspot zone for •OH production, with the production flux of 13.05 μmol kg−1 d−1, which was significantly higher than that in floodplain (7.29 μmol kg−1 d−1) and riverbank soils (8.61 μmol kg−1 d−1). Moreover, •OH levels displayed distinct rhythmic fluctuations, with significantly higher concentrations at low water levels compared to those at high water levels, and remained essentially flat over three cycles. The statistic analysis revealed that the ROS production was highly dependent on reduced species and microbial community structure, which function as biogeochemical batteries and electron shuttles under redox fluctuations. Furthermore, the generated •OH involved in the abiotic mineralization of SOC, contributing to 13.1‒21.8 % of total CO2 efflux. Compared to particulate organic carbon (POC), mineral-associated organic carbon (MAOC) fractions of SOC were more susceptible to •OH attacks. The findings provide a novel insight to comprehensively assess the redox process on riparian carbon turnover.