Assessing the corrosion protection property of coatings loaded with corrosion inhibitors using the real-time atmospheric corrosion monitoring technique

The atmospheric corrosion monitoring(ACM)technique has been widely employed to track the real-time corrosion behavior of metal materials.However,limited studies have applied ACM to the corrosion protection properties of organic coatings.This study compared a bare epoxy coating with one containing zinc phosphate corrosion inhibitors,both applied on ACM sensors,to observe their corrosion protection properties over time.Coatings with artificial damage via scratches were exposed to immersion and alternating dry and wet environments,which allowed for monitoring galvanic corrosion currents in real-time.Throughout the corrosion tests,the ACM currents of the zinc phosphate/epoxy coating were considerably lower than those of the blank epoxy coating.The trend in ACM current variations closely matched the results obtained from regular electrochemical tests and surface analysis.This alignment highlights the potential of the ACM technique in evaluating the corrosion protection capabilities of organic coatings.Compared with the blank epoxy coating,the zinc phosphate/epoxy coating showed much-decreased ACM current values that confirmed the effective inhibition of zinc phosphate against steel corrosion beneath the damaged coating.

Nanomaterial-assisted wearable glucose biosensors for noninvasive real-time monitoring:Pioneering point-of-care and beyond

This review explores glucose monitoring and management strategies,emphasizing the need for reliable and userfriendly wearable sensors that are the next generation of sensors for continuous glucose detection.In addition,examines key strategies for designing glucose sensors that are multi-functional,reliable,and cost-effective in a variety of contexts.The unique features of effective diabetes management technology are highlighted,with a focus on using nano/biosensor devices that can quickly and accurately detect glucose levels in the blood,improving patient treatment and control of potential diabetes-related infections.The potential of next-generation wearable and touch-sensitive nano biomedical sensor engineering designs for providing full control in assessing implantable,continuous glucose monitoring is also explored.The challenges of standardizing drug or insulin delivery doses,low-cost,real-time detection of increased blood sugar levels in diabetics,and early digital health awareness controls for the adverse effects of injectable medication are identified as unmet needs.Also,the market for biosensors is expected to expand significantly due to the rising need for portable diagnostic equipment and an ever-increasing diabetic population.The paper concludes by emphasizing the need for further research and development of glucose biosensors to meet the stringent requirements for sensitivity and specificity imposed by clinical diagnostics while being cost-effective,stable,and durable.

Real-time debris flow monitoring and automated warning system

At present,debris flow warning uses precipitation threshold and issues regional warning throughout the world.Precipitation threshold warning is less accurate and in most of the time large portion of unaffected population are evacuated.More precise warning should use direct monitoring.There are many debris flow monitoring stations but no real time warning system in use.The main reason is that the identification and confirmation of debris flow occurrence requires human interaction and it is too slow.A debris flow monitoring and warning system has been installed in the midstream section of Yusui Stream,Taiwan China.The monitoring station operates fully automatically,providing early warnings without the need for manual intervention.The system comprises two webcam cameras,two Micro-Electro-Mechanical Systems(MEMS),and a rain gauge.The arrival of debris flows is detected and confirmed through both webcam images and MEMS signals.Once debris flow is detected,the system automatically issues a warning to the affected areas via voice messages,line messages,broadcasts,and web-based alerts.The webcam cameras are also used to estimate debris flow velocity and flow height,while the MEMS sensors are utilized to determine the phase speed and flow rate.On July 24th,2014,Typhoon Gaemi triggered several debris flows,and the system successfully issued several warnings automatically.The entire video record,along with depth variation data,was recorded automatically.

Real-Time Monitoring Method for Cow Rumination Behavior Based on Edge Computing and Improved MobileNet v3

[Objective]Real-time monitoring of cow ruminant behavior is of paramount importance for promptly obtaining relevant information about cow health and predicting cow diseases.Currently,various strategies have been proposed for monitoring cow ruminant behavior,including video surveillance,sound recognition,and sensor monitoring methods.How‐ever,the application of edge device gives rise to the issue of inadequate real-time performance.To reduce the volume of data transmission and cloud computing workload while achieving real-time monitoring of dairy cow rumination behavior,a real-time monitoring method was proposed for cow ruminant behavior based on edge computing.[Methods]Autono‐mously designed edge devices were utilized to collect and process six-axis acceleration signals from cows in real-time.Based on these six-axis data,two distinct strategies,federated edge intelligence and split edge intelligence,were investigat‐ed for the real-time recognition of cow ruminant behavior.Focused on the real-time recognition method for cow ruminant behavior leveraging federated edge intelligence,the CA-MobileNet v3 network was proposed by enhancing the MobileNet v3 network with a collaborative attention mechanism.Additionally,a federated edge intelligence model was designed uti‐lizing the CA-MobileNet v3 network and the FedAvg federated aggregation algorithm.In the study on split edge intelli‐gence,a split edge intelligence model named MobileNet-LSTM was designed by integrating the MobileNet v3 network with a fusion collaborative attention mechanism and the Bi-LSTM network.[Results and Discussions]Through compara‐tive experiments with MobileNet v3 and MobileNet-LSTM,the federated edge intelligence model based on CA-Mo‐bileNet v3 achieved an average Precision rate,Recall rate,F1-Score,Specificity,and Accuracy of 97.1%,97.9%,97.5%,98.3%,and 98.2%,respectively,yielding the best recognition performance.[Conclusions]It is provided a real-time and effective method for monitoring cow ruminant behavior,and the proposed federated edge intelligence model can be ap‐plied in practical settings.

Redox mechanism of geobattery and related electrical signals using a novel real-time self-potential monitoring experimental platform

Controlled laboratory experiments are proved to be a valuable tool for investigating changes in underground physical properties and the related response of surface geophysical signals.The self-potential(SP)method is widely used in mineral resource exploration due to its direct correlation with underground electrochemical gradients.This paper presented the design and construction of an experimental platform based on a multi-channel SP monitoring system.The proposed platform was used to monitor the anodizing corrosion process of different metal blocks from a laboratory perspective,record the real-time SP signal generated by the redox reaction,as well as investigate the geobattery mechanism associated with the natural polarization process of metal mineral resources.The experimental results demonstrate that the constructed SP monitoring platform effectively captures time-series SP signals and provides direct laboratory evidence for the geobattery model.The measured SP data were quantitatively interpreted using the simulated annealing algorithm,and the inversion results closely match the real model.This finding highlights the potential of the SP method as a promising tool for determining the location and spatial distribution of underground polarizers.The study holds reference value for the exploration and exploitation of mineral resources in both terrestrial and marine environments.

Real-Time Monitoring of Meteorological Data at In-Situ GCW Remediation Sites

To optimize the self-organization network, self-adaptation, real-time monitoring, remote management capability, and equipment reuse level of the meteorological station supporting the portable groundwater circulation wells, and to provide real-time and effective technical services and environmental data support for groundwater remediation, a real-time monitoring system design of the meteorological station supporting the portable groundwater circulation wells based on the existing equipment is proposed. A variety of environmental element information is collected and transmitted to the embedded web server by the intelligent weather transmitter, and then processed by the algorithm and stored internally, displayed locally, and published on the web. The system monitoring algorithm and user interface are designed in the CNWSCADA development environment to realize real-time processing and analysis of environmental data and monitoring, control, management, and maintenance of the system status. The PLC-controlled photovoltaic power generating panels and lithium battery packs are in line with the concept of energy saving and emission reduction, and at the same time, as an emergency power supply to guarantee the safety of equipment and data when the utility power fails to meet the requirements. The experiment proves that the system has the characteristics of remote control, real-time interaction, simple station deployment, reliable operation, convenient maintenance, and green environment protection, which is conducive to improving the comprehensive utilization efficiency of various types of environmental information and providing reliable data support, theoretical basis and guidance suggestions for the research of groundwater remediation technology and its disciplines, and the research and development of the movable groundwater cycling well monitoring system.

A fully integrated electronic fabric-enabled multimodal flexible sensors for real-time wireless pressure-humidity-temperature monitoring

Real-time physiological information monitoring can predict and prevent disease, or improve treatment by early diagnosis. A comprehensive and continuous monitoring of human health requires highly integrated wearable and comfortable sensing devices. To address this need, we propose a low-cost electronic fabric-enabled multifunctional flexible sensing integration platform that includes a flexible pressure sensor for monitoring postural pressure, a humidity sensor for monitoring the humidity of the skin surface, and a flexible temperature sensor for visualizing the ambient temperature around the human body. Thanks to the unique rough surface texture, hierarchical structure, and robust electromechanical features of the MXene-modified nonwoven fabrics, the flexible pressure sensor can achieve a monitoring sensitivity of 1529.1 kPa~(-1) and a pressure range of 150 kPa, which meets the demand for human pressure detection. In addition, the unique porous structure of the fabric and the stacked multilayer structure of MXene enable the humidity sensor to exhibit extremely high monitoring sensitivity, even through clothing, and still be able to detect the humidity on the skin surface.Temperature sensors based on screen-printed thermochromic liquid crystals enable visual monitoring in the range of 0℃–65℃. Through further integration with flexible printed circuit board circuits, we demonstrate a proof-of-concept device that enables real-time monitoring of human physiological information such as physical pressure, humidity, and ambient temperature environment, suggesting that the device provides an excellent platform for the development of commercially viable wearable healthcare monitors.

Framework and Elemental Analysis for Constructing a Real-Time New Quality Productivity Monitoring System

New quality productivity represents the core force driving global economic transformation and high-quality development.It signifies a shift in the economic system from being driven by traditional factors to a focus on innovation,technology intensiveness,and green,sustainable transformation.In this context,establishing an effective quality productivity monitoring system is of critical importance.This paper aims to construct a theoretical framework for the new quality productivity monitoring system and analyze its key elements.The goal is to provide a robust data foundation and scientific guidance for policy planning,platform development,talent cultivation,and introduction.The ultimate aim is to achieve real-time monitoring and precise evaluation of new quality productivity,ensuring its alignment with the long-term development of the social economy.

Real-time Monitoring Scheme of Soil Moisture Content in Paddy Field

The monitoring of soil moisture content in paddy field is one of important parts and contents of regional soil moisture monitoring. But a good monitoring scheme hasn’t been established. A real-time monitoring scheme of soil moisture content in paddy field was put forward from two key links of soil moisture content monitoring and field water-layer monitoring. This scheme could meet the alternative monitoring requirements of soil moisture content in water layer and none-water layer. It had a good maneuverability and could provide references for practical work.

A Fully‑Printed Wearable Bandage‑Based Electrochemical Sensor with pH Correction for Wound Infection Monitoring

Wearable sensing systems have been designed to monitor health conditions in real-time by detecting analytes in human biofluids.Wound diagnosis remains challenging,necessitating suitable materials for high-performance wearable sensors to offer prompt feedback.Existing devices have limitations in measuring pH and the concentration of pH-dependent electroactive species simultaneously,which is crucial for obtaining a comprehensive understanding of wound status and optimizing biosensors.Therefore,improving materials and analysis system accuracy is essential.This article introduces the first example of a flexible array capable of detecting pyocyanin,a bacterial virulence factor,while correcting dynamic pH fluctuations.We demonstrate that this combined sensor enhances accuracy by mitigating the impact of pH variability on pyocyanin sensor response.Customized screen-printable inks were developed to enhance analytical performance.The analytical performances of two sensitive sensor systems(i.e.,fully-printed porous graphene/multiwalled carbon nanotube(CNT)and polyaniline/CNT composites for pyocyanin and pH sensors)are evaluated.Partial least square regression is employed to analyze nonzero-order data arrays from square wave voltammetric and potentiometric measurements of pyocyanin and pH sensors to establish a predictive model for pyocyanin concentration in complex fluids.This sensitive and effective strategy shows potential for personalized applications due to its affordability,ease of use,and ability to adjust for dynamic pH changes.

Low‑Temperature Fabrication of Stable Black‑Phase CsPbI_(3) Perovskite Flexible Photodetectors Toward Wearable Health Monitoring

Flexible wearable optoelectronic devices fabricated fromorganic–inorganic hybrid perovskites significantly accelerate the developmentof portable energy,biomedicine,and sensing fields,but their poor thermal stabilityhinders further applications.Conversely,all-inorganic perovskites possessexcellent thermal stability,but black-phase all-inorganic perovskite filmusually requires high-temperature annealing steps,which increases energy consumptionand is not conducive to the fabrication of flexible wearable devices.In this work,an unprecedented low-temperature fabrication of stable blackphaseCsPbI3perovskite films is demonstrated by the in situ hydrolysis reactionof diphenylphosphinic chloride additive.The released diphenyl phosphateand chloride ions during the hydrolysis reaction significantly lower the phasetransition temperature and effectively passivate the defects in the perovskitefilms,yielding high-performance photodetectors with a responsivity of 42.1 AW−1 and a detectivity of 1.3×10^(14)Jones.Furthermore,high-fidelity imageand photoplethysmography sensors are demonstrated based on the fabricated flexible wearable photodetectors.This work provides a newperspective for the low-temperature fabrication of large-area all-inorganic perovskite flexible optoelectronic devices.

Working Condition Real-Time Monitoring Model of Lithium Ion Batteries Based on Distributed Parameter System and Single Particle Model

Lithium ion batteries are complicated distributed parameter systems that can be described preferably by partial differential equations and a field theory. To reduce the solution difficulty and the calculation amount, if a distributed parameter system is described by ordinary differential equations （ODE） during the analysis and the design of distributed parameter system, the reliability of the system description will be reduced, and the systemic errors will be introduced. Studies on working condition real-time monitoring can improve the security because the rechargeable LIBs are widely used in many electronic systems and electromechanical equipment. Single particle model （SPM） is the simplification of LIB under some approximations, and can estimate the working parameters of a LIB at the faster simulation speed. A LIB modelling algorithm based on PDEs and SPM is proposed to monitor the working condition of LIBs in real time. Although the lithium ion concentration is an unmeasurable distributed parameter in the anode of LIB, the working condition monitoring model can track the real time lithium ion concentration in the anode of LIB, and calculate the residual which is the difference between the ideal data and the measured data. A fault alarm can be triggered when the residual is beyond the preset threshold. A simulation example verifies that the effectiveness and the accuracy of the working condition real-time monitoring model of LIB based on PDEs and SPM.

Construction Monitoring and Analysis of Asymmetric Prestressed Concrete Bridge Crossing Multiple-Line Railways

Complex bridge structures designed and constructed by humans often necessitate extensive on-site execution,which carries inherent risks.Consequently,a variety of engineering practices are employed to monitor bridge construction.This paper presents a case study of a large-span prestressed concrete(PC)variable-section continuous girder bridge in China,proposing a feedback system for construction monitoring and establishing a finite element(FE)analysis model for the entire bridge.The alignment of the completed bridge adheres to the initial design expectations,with maximum displacement and pre-arch differences from the ideal state measuring 6.39 and 17.7 mm,respectively,which were less than the 20 mm limit required by the specification.Additionally,the stress monitoring showed that the maximum compressive stress was 10.44 MPa,which was 7.5%different from the finite element results,and better predicted the most unfavorable possible location.These results demonstrate that a scientifically rigorous construction monitoring and feedback system can ensure the safety of bridge construction and meet the expected construction standards.The findings presented in this paper provide valuable insights for bridge construction monitoring practices.

Correlation between key indicators of continuous glucose monitoring and the risk of diabetic foot

BACKGROUND Continuous glucose monitoring(CGM)metrics,such as time in range(TIR)and glycemic risk index(GRI),have been linked to various diabetes-related complications,including diabetic foot(DF).AIM To investigate the association between CGM-derived indicators and the risk of DF in individuals with type 2 diabetes mellitus(T2DM).METHODS A total of 591 individuals with T2DM(297 with DF and 294 without DF)were enrolled.Relevant clinical data,complications,comorbidities,hematological parameters,and 72-hour CGM data were collected.Logistic regression analysis was employed to examine the relationship between these measurements and the risk of DF.RESULTS Individuals with DF exhibited higher mean blood glucose(MBG)levels and increased proportions of time above range(TAR),TAR level 1,and TAR level 2,but lower TIR(all P<0.001).Patients with DF had significantly lower rates of achieving target ranges for TIR,TAR,and TAR level 2 than those without DF(all P<0.05).Logistic regression analysis revealed that GRI,MBG,and TAR level 1 were positively associated with DF risk,while TIR was inversely correlated(all P<0.05).Achieving TIR and TAR was inversely correlated with white blood cell count and glycated hemoglobin A1c levels(P<0.05).Additionally,achieving TAR was influenced by fasting plasma glucose,body mass index,diabetes duration,and antidiabetic medication use.CONCLUSION CGM metrics,particularly TIR and GRI,are significantly associated with the risk of DF in T2DM,emphasizing the importance of improved glucose control.

Risk factors,monitoring,and treatment strategies for early recurrence after rectal cancer surgery

Early recurrence(ER)following surgery for rectal cancer is a significant factor impacting patient survival rates.Tsai et al identified age,preoperative neoadjuvant therapy,length of hospital stay,tumour location,and pathological stage as factors influencing the risk of ER.Postoperative monitoring for ER should encompass a thorough medical history review,physical examination,tumour marker testing,and imaging studies.Additionally,noninvasive circulating tumour cell DNA testing can be utilized to predict ER.Treatment strategies may involve radical surgery,radiation therapy,chemotherapy,and immunotherapy.Through a comprehensive analysis of risk factors,the optimization of monitoring methods,and the development of personalized treatment strategies,it is anticipated that both the efficacy of treatment and the quality of life for rectal cancer patients with postoperative recurrence can be significantly improved.

Long-term monitoring of active large-scale landslides for non-structural risk mitigation-integrated sensors and web-based platform

Large-scale deep-seated landslides pose a significant threat to human life and infrastructure.Therefore,closely monitoring these landslides is crucial for assessing and mitigating their associated risks.In this paper,the authors introduce the So Lo Mon framework,a comprehensive monitoring system developed for three large-scale landslides in the Autonomous Province of Bolzano,Italy.A web-based platform integrates various monitoring data(GNSS,topographic data,in-place inclinometer),providing a user-friendly interface for visualizing and analyzing the collected data.This facilitates the identification of trends and patterns in landslide behaviour,enabling the triggering of warnings and the implementation of appropriate mitigation measures.The So Lo Mon platform has proven to be an invaluable tool for managing the risks associated with large-scale landslides through non-structural measures and driving countermeasure works design.It serves as a centralized data repository,offering visualization and analysis tools.This information empowers decisionmakers to make informed choices regarding risk mitigation,ultimately ensuring the safety of communities and infrastructures.

Role of disturbance coefficient in monitoring and treatment of cerebral edema in patients with cerebral hemorrhage

BACKGROUND At present,the conventional methods for diagnosing cerebral edema in clinical practice are computed tomography(CT)and magnetic resonance imaging(MRI),which can evaluate the location and degree of peripheral cerebral edema,but cannot realize quantification.When patients have symptoms of diffuse cerebral edema or high cranial pressure,CT or MRI often suggests that cerebral edema is lagging and cannot be dynamically monitored in real time.Intracranial pressure monitoring is the gold standard,but it is an invasive operation with high cost and complications.For clinical purposes,the ideal cerebral edema monitoring should be non-invasive,real-time,bedside,and continuous dynamic monitoring.The dis-turbance coefficient(DC)was used in this study to dynamically monitor the occu-rrence,development,and evolution of cerebral edema in patients with cerebral hemorrhage in real time,and review head CT or MRI to evaluate the development of the disease and guide further treatment,so as to improve the prognosis of patients with cerebral hemorrhage.AIM To offer a promising new approach for non-invasive adjuvant therapy in cerebral edema treatment.METHODS A total of 160 patients with hypertensive cerebral hemorrhage admitted to the Department of Neurosurgery,Second Affiliated Hospital of Xi’an Medical University from September 2018 to September 2019 were recruited.The patients were randomly divided into a control group(n=80)and an experimental group(n=80).Patients in the control group received conventional empirical treatment,while those in the experimental group were treated with mannitol dehydration under the guidance of DC.Subsequently,we compared the two groups with regards to the total dosage of mannitol,the total course of treatment,the incidence of complications,and prognosis.RESULTS The mean daily consumption of mannitol,the total course of treatment,and the mean hospitalization days were 362.7±117.7 mL,14.8±5.2 days,and 29.4±7.9 in the control group and 283.1±93.6 mL,11.8±4.2 days,and 23.9±8.3 in the experimental group(P<0.05).In the control group,there were 20 patients with pulmonary infection(25%),30 with electrolyte disturbance(37.5%),20 with renal impairment(25%),and 16 with stress ulcer(20%).In the experimental group,pulmonary infection occurred in 18 patients(22.5%),electrolyte disturbance in 6(7.5%),renal impairment in 2(2.5%),and stress ulcers in 15(18.8%)(P<0.05).According to the Glasgow coma scale score 6 months after discharge,the prognosis of the control group was good in 20 patients(25%),fair in 26(32.5%),and poor in 34(42.5%);the prognosis of the experimental group was good in 32(40%),fair in 36(45%),and poor in 12(15%)(P<0.05).CONCLUSION Using DC for non-invasive dynamic monitoring of cerebral edema demonstrates considerable clinical potential.It reduces mannitol dosage,treatment duration,complication rates,and hospital stays,ultimately lowering hospital-ization costs.Additionally,it improves overall patient prognosis,offering a promising new approach for non-invasive adjuvant therapy in cerebral edema treatment.

Intelligent field monitoring system for cruciferous vegetable pests using yellow sticky board images and an improved Cascade R-CNN

Cruciferous vegetables are important edible vegetable crops.However,they are susceptible to various pests during their growth process,which requires real-time and accurate monitoring of these pests for pest forecasting and scientific control.Hanging yellow sticky boards is a common way to monitor and trap those pests which are attracted to the yellow color.To achieve real-time,low-cost,intelligent monitoring of these vegetable pests on the boards,we established an intelligent monitoring system consisting of a smart camera,a web platform and a pest detection algorithm deployed on a server.After the operator sets the monitoring preset points and shooting time of the camera on the system platform,the camera in the field can automatically collect images of multiple yellow sticky boards at fixed places and times every day.The pests trapped on the yellow sticky boards in vegetable fields,Plutella xylostella,Phyllotreta striolata and flies,are very small and susceptible to deterioration and breakage,which increases the difficulty of model detection.To solve the problem of poor recognition due to the small size and breaking of the pest bodies,we propose an intelligent pest detection algorithm based on an improved Cascade R-CNN model for three important cruciferous crop pests.The algorithm uses an overlapping sliding window method,an improved Res2Net network as the backbone network,and a recursive feature pyramid network as the neck network.The results of field tests show that the algorithm achieves good detection results for the three target pests on the yellow sticky board images,with precision levels of 96.5,92.2 and 75.0%,and recall levels of 96.6,93.1 and 74.7%,respectively,and an F_(1) value of 0.880.Compared with other algorithms,our algorithm has a significant advantage in its ability to detect small target pests.To accurately obtain the data for the newly added pests each day,a two-stage pest matching algorithm was proposed.The algorithm performed well and achieved results that were highly consistent with manual counting,with a mean error of only 2.2%.This intelligent monitoring system realizes precision,good visualization,and intelligent vegetable pest monitoring,which is of great significance as it provides an effective pest prevention and control option for farmers.

New digital drilling process monitoring: Instrumentation, validation and calibration

This study aims to enhance the digital drilling process monitoring(DPM)or monitoring while drilling(MWD)technique,which is a widely recognized method in geological exploration for evaluating rock mass quality.First,robust displacement and torque measurement facilities for rotary-core drilling are discussed.The conventional cable encoder for displacement measurement is replaced with a magnetostrictive displacement sensor,which is more reliable in harsh field drilling environments.This enables the measurement of the bit position with an accuracy of<1 mm.Most importantly,this new instrument is proven to be successful in improving the detection of structural discontinuities with thicknesses>1 mm.In addition,by measuring the electric current of the driving motor,the torque applied to the bit is conveniently and accurately converted.These innovations ensure high-quality data collection for DPM practices.Second,two indices derived from DPM are proposed to quantitatively describe rock mass quality.The specific energy index(SEI)and specific penetration index(SPI)are based on the principles of energy conservation and Mohr-Coulomb failure criterion,respectively.Extensive field tests conducted in a dam grouting area confirm a linear relationship between the thrust force and penetration per rotation,and between the torque and penetration per rotation.The correlation ratios of the related regressions are typically>0.9.These two indices allow for the quantitative interpretation of DPM data into rock mechanics characteristics,such as uniaxial compressive strength,rock quality designation(RQD),and rock mass permeability,eliminating the need for subjective judgment normally involved in the currently used rock mass quality rating approaches.

Characterizing large deformation of soft rock tunnel using microseismic monitoring and numerical simulation

Surrounding rock deterioration and large deformation have always been a significant difficulty in designing and constructing tunnels in soft rock.The key lies in real-time perception and quantitative assessment of the damaged area around the tunnel.An in situ microseismic(MS)monitoring system is established in the plateau soft tock tunnel.This technique facilitates spatiotemporal monitoring of the rock mass's fracturing expansion and squeezing deformation,which agree well with field convergence deformation results.The formation mechanisms of progressive failure evolution of soft rock tunnels were discussed and analyzed with MS data and numerical results.The results demonstrate that:(1)Localized stress concentration and layered rock result in significant asymmetry in micro-fractures propagation in the tunnel radial section.As excavation continues,the fracture extension area extends into the deep surrounding rockmass on the east side affected by the weak bedding;(2)Tunnel excavation and long-term deformation can induce tensile shear action on the rock mass,vertical tension fractures(account for 45%)exist in deep rockmass,which play a crucial role in controlling the macroscopic failure of surrounding rock;and(3)Based on the radiated MS energy,a three-dimensional model was created to visualize the damage zone of the tunnel surrounding rock.The model depicted varying degrees of damage,and three high damage zones were identified.Generally,the depth of high damage zone ranged from 4 m to 12 m.This study may be a valuable reference for the warning and controlling of large deformations in similar projects.