大型桥梁建管养中的检测监测技术

当前,我国大型桥梁检测监测主要包括结构外观缺陷、内部及隐蔽性损伤、几何形状参数、力学参数、材料参数等内容。传统的结构管养检测监测方法存在人工巡检成本,以及主观性强、不确定性大、效率不高、缺乏科学量化依据等弊端。近年来,行业内先后涌现出一大批新的检测监测方法及手段,如计算机视觉技术、无损检(监)测技术等,推动大型桥梁建管养的检测监测技术向智能化、快速化和系统化发展。此外,5G技术、物联网技术、人工智能技术的兴起更为桥梁智能建管养提供了可靠的数据支持和信息化渠道,显著提升了桥梁检测监测的效率和效果。

煤矿瓦斯监测监控系统日常管理

煤矿的瓦斯事故时有发生,在矿井生产中,实现对瓦斯浓度变化的有效监测监控是确保矿井生产安全的重要手段。基于此,针对我国煤炭行业瓦斯测监监控系统的应用现状,从硬件设施和系统运行中存在的问题进行分析,提出了煤矿安全监测监控系统问题解决措施,为煤矿提高安全管理水平提供参考。

金属材料腐蚀检(监)测常用方法概述

从机械方法、无损检测方法和电化学方法3个方面简要介绍了常用的腐蚀检(监)测方法,并对各种检验方法的特点和适用范围进行了比较。在实际的生产实践中应根据具体情况,依据可靠性和适用性的原则选择合适的方法,从而达到高效、准确的检验目的。

重庆市强化实验室监(检)测结果技术依托的做法

兽医实验室是动物防疫技术支撑体系的重要环节,在疫病防控中起着基础性作用。在长期的工作实践中,重庆市不断探索如何充分发挥兽医实验室的职能职责,推动兽医实验室建设和可持续发展,结合重庆市兽医工作"三项制度"的建设情况,探索建立以兽医实验室监(检)测结果为导向、推进兽医实验室建设与发展的工作模式。

浅谈煤矿安全监测监控系统

随着我国经济的飞速发展,各行各业对煤炭的需求不断扩大,为缓解当前紧张的煤炭供需关系,大部分煤矿都开始扩大生产规模,增加开采量安全的工作环境是煤矿工人在进行煤炭开采时的重要保障,本文针对煤矿安全监测监控系统进行简单分析,旨在促进监控系统的现代化发展,保证煤矿工人的生命财产安全,提高企业的经济效益.

三维激光扫描技术在监(检)测工程中的应用

针对部分监(检)测项目需全方位监测和建模的问题,结合某港口填海区后方道路及建筑物监测工程,创新应用了高精度的徕卡MS60三维激光扫描技术。采用误差分析、性能测试、现场扫侧、内业处理、结果对比分析等方法,实现了从点监测到面监测的转变,避免传统点监测的局限性和片面性,并极大提高了工作效率。经对比分析,三维激光扫描技术具有独特的功能、精度和效率优势,并将在监(检)测工程中得到更广泛的应用。

用户上网监测的SOHO路由器实现

Linux是目前主流SOHO路由器采用的操作系统。这得益于Linux本身强大的网络功能的支持。文章结合Linux中的Netfilter框架,通过对流经路由器的流量进行截取掌握用户网络活动的情况。

腹腔镜宫颈癌手术中烟雾有毒物质的监测及改良密闭排烟气法的净化作用分析

目的探讨腹腔镜宫颈癌手术中烟雾有毒物质的监测及改良密闭排烟气法的净化作用。方法选取80例实施腹腔镜下宫颈癌根治术患者为研究对象,按照随机数字表法分为两组,各40例。对照组实施以往传统的开放、间断排烟气法,研究组实施本研究应用的改良密闭排烟气法。比较两组手术医师口鼻附近PM10.0和PM2.5水平。比较两组手术开始1 h时空气中一氧化碳和二氧化碳浓度。结果研究组手术医师口鼻附近PM10.0和PM2.5水平均显著低于对照组(P<0.05)。手术开始1 h时,研究组空气中一氧化碳(1.04±0.05)ppm和二氧化碳(528.8±35.6)ppm浓度均显著低于对照组的(3.15±0.17)ppm、(1145.6±117.5)ppm(P<0.05)。结论对腹腔镜下宫颈癌根治术患者,术中行改良密闭式排烟法,能有效的改善手术室空气质量,减少医务人员吸入颗粒性有毒有害物质水平,降低手术室空气中一氧化碳和二氧化碳。

广西重力式码头漏砂及检(监)测分析

漏砂为重力式码头常见病害之一,该病害的发展如得不到有效的控制,将严重影响码头安全运营,缩短码头使用寿命。文章结合广西重力式码头检(监)测实例,分析了漏砂病害发生的原因,介绍了该病害的检(监)测手段,并提出了相应的预防措施。

桥梁面相学及其研究进展

为了探索更加实效、经济、便捷、可信的桥梁安全状态检(监)测新方法,受中医望、闻、问、切诊断人体健康状态理念启迪,提出了依据桥梁外观形态变化来获知结构近期安全状态的桥梁面相学;总结了10多年来基于桥梁面相学的桥梁安全状态检(监)研究进展;介绍了基于定点相机平转+竖转拍摄的桥梁动静影像全息性态监测系统、基于激光雷达+全景数码相机拍摄的WWWQ-G桥梁安全巡检车、基于普通摄像头拍摄的常规跨径桥梁安全监测系统和基于定点旋转测量装置的拉索性态远距全域视频检测系统的构成理论,以及各系统应用于桥梁构件损伤检测的试验研究案例和实际工程应用案例;展望了融合“测点传感器+机器视觉”实现桥梁结构状态的“精+密”监测方法及其应用前景。

Mechanism,prevention,and control of mining-induced dynamic disasters in underground metal mines in China:Challenges and solutions

Metal mineral resources play an indispensable role in the development of the national economy.Dynamic disasters in underground metal mines seriously threaten mining safety,which are major scientific and technological problems to be solved urgently.In this article,the occurrence status and grand challenges of some typical dynamic disasters involving roof falling,spalling,collapse,large deformation,rockburst,surface subsidence,and water inrush in metal mines in China are systematically presented,the characteristics of mining-induced dynamic disasters are analyzed,the examples of dynamic disasters occurring in some metal mines in China are summarized,the occurrence mechanism,monitoring and early warning methods,and prevention and control techniques of these disasters are highlighted,and some new opinions,suggestions,and solutions are proposed simultaneously.Moreover,some shortcomings in current disaster research are pointed out,and the direction of efforts to improve the prevention and control level of dynamic disasters in China’s metal mines in the future is prospected.The integration of forward-looking key innovative theories and technologies in the abovementioned aspects will greatly enhance the cognitive level of disaster prevention and mitigation in China’s metal mining industry and achieve a significant shift from passive disaster relief to active disaster prevention.

Electrical Characteristics of Tangjiawan Landslide in Lixian, Sichuan

A wide range of terrain features and landforms,which are exemplified by intricate geological formations and diverse rock compositions,are found in the western mountainous regions of China.These areas frequently encounter geological disasters.As one of the natural disasters,landslides lead to considerable loss of human life and property.Considering mitigation of the losses caused by landslide disasters,a necessary measure for disaster prevention and mitigation involves conducting detailed investigations and monitoring of landslides,which is also the cornerstone of landslide warning.This study compares and analyzes the feasibility of the magnetotelluric detection method for landslides using the results of engineering geological surveys and landslide monitoring.The study aims to address the scientific problem of the validity of using magnetotelluric methods to detect landslide development processes.The Tangjiawan landslide signal on the left side of the K94+000~K94+145 section of the Wenma Expressway is analyzed by employing engineering geological survey,magnetotelluric detection,landslide monitoring,landslide analysis,and other methods.Analysis results provide the static electrical characteristics of lithology,structure,and groundwater,as well as the dynamic electrical characteristics of landslide development.This study focuses on analyzing the relationship between the methods of magnetotelluric detection and engineering geological surveys and the results of landslide monitoring.The workflow and methods for data collection,processing,inversion,interpretation,and analysis using the magnetotelluric method to detect the dynamic development process of landslides are presented in the conclusion.Preliminary conclusions indicate a strong correlation between the dynamic changes in magnetotelluric wave impedance with the surface displacement of landslides and the dynamic changes in groundwater.The use of the magnetotelluric method for landslide detection and monitoring is a feasible example.The research results can offer certain technical references for the detection and monitoring of landslides using magnetotelluric methods and also provide references and guidance for the selection of diversified landslide monitoring methods in the future.

Predictive maintenance and its applications in civil engineering structures:A review

Structural health monitoring and performance prediction are crucial for smart disaster mitigation and intelligent management of structures throughout their lifespan.Recent advancements in predictive maintenance strategies within the industrial manufacturing industry have inspired similar innovations in civil engineering,aiming to improve structural performance evaluation,damage diagnosis,and capacity prediction.This review delves into the framework of predictive maintenance and examines various existing solutions,focusing on critical areas such as data acquisition,condition monitoring,damage prognosis,and maintenance planning.Results from real-world applications of predictive maintenance in civil engineering,covering high-rise structures,deep foundation pits,and other infrastructure,are presented.The challenges of implementing predictive maintenance in civil engineering structures under current technology,such as model interpretability of data-driven methods and standards for predictive maintenance,are explored.Future research prospects within this area are also discussed.

Ultrasensitive stretchable patches for joint motion monitoring

Wearable devices have great application potential in the next generation of smart portable electronics,especially in the fields of medical monitoring,soft robotics,artificial intelligence,and human-machine interfaces.Piezoelectric flexible strain sensors are key components of wearable devices.However,existing piezoelectric flexible strain sensors have certain limitations in weak signal monitoring due to their large modulus and low sensitivity.To solve this problem,the concept of Kirigami(paper-cutting)was introduced in this study to design the sensor structure.By comparing the Kirigami structures of different basic structures,the serpentine structure was determined as the basic configuration of the sensor.The serpentine structure not only provides excellent tensile properties,but also significantly improves the sensitivity of the sensor,which performs well in monitoring weak signals.On this basis,the adhesion properties of the flexible sensor were analyzed and tested,and the optimal ratio of the substrate was selected for preparation.In addition,a low-cost and rapid prototyping process for stretchable patches was established in this study.Using this technology,we prepared the sensor device and tested its performance.Finally,we successfully developed a flexible sensor with a sensitivity of 0.128 mV/μɛand verified its feasibility for wrist joint motion monitoring applications.This result opens up new avenues for the recovery care of tenosynovitis patients after surgery.

Improving autoencoder-based unsupervised damage detection in uncontrolled structural health monitoring under noisy conditions

Structural health monitoring is widely utilized in outdoor environments,especially under harsh conditions,which can introduce noise into the monitoring system.Therefore,designing an effective denoising strategy to enhance the performance of guided wave damage detection in noisy environments is crucial.This paper introduces a local temporal principal component analysis(PCA)reconstruction approach for denoising guided waves prior to implementing unsupervised damage detection,achieved through novel autoencoder-based reconstruction.Experimental results demonstrate that the proposed denoising method significantly enhances damage detection performance when guided waves are contaminated by noise,with SNR values ranging from 10 to-5 dB.Following the implementation of the proposed denoising approach,the AUC score can elevate from 0.65 to 0.96 when dealing with guided waves corrputed by noise at a level of-5 dB.Additionally,the paper provides guidance on selecting the appropriate number of components used in the denoising PCA reconstruction,aiding in the optimization of the damage detection in noisy conditions.

Research on High-Velocity Impact Damage Monitoring Method of CFRP Based on Guided Wave

Carbon fiber-reinforced polymer(CFRP)is widely used in aerospace applications.This kind of material may face the threat of high-velocity impact in the process of dedicated service,and the relevant research mainly considers the impact resistance of the material,and lacks the high-velocity impact damage monitoring research of CFRP.To solve this problem,a real high-velocity impact damage experiment and structural health monitoring(SHM)method of CFRP plate based on piezoelectric guided wave is proposed.The results show that CFRP has obvious perforation damage and fiber breakage when high-velocity impact occurs.It is also proved that guided wave SHM technology can be effectively used in the monitoring of such damage,and the damage can be reflected by quantifying the signal changes and damage index(DI).It provides a reference for further research on guided wave structure monitoring of high/hyper-velocity impact damage of CFRP.

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.

Bridge damage identification based on convolutional autoencoders and extreme gradient boosting trees

To enhance the accuracy and efficiency of bridge damage identification,a novel data-driven damage identification method was proposed.First,convolutional autoencoder(CAE)was used to extract key features from the acceleration signal of the bridge structure through data reconstruction.The extreme gradient boosting tree(XGBoost)was then used to perform analysis on the feature data to achieve damage detection with high accuracy and high performance.The proposed method was applied in a numerical simulation study on a three-span continuous girder and further validated experimentally on a scaled model of a cable-stayed bridge.The numerical simulation results show that the identification errors remain within 2.9%for six single-damage cases and within 3.1%for four double-damage cases.The experimental validation results demonstrate that when the tension in a single cable of the cable-stayed bridge decreases by 20%,the method accurately identifies damage at different cable locations using only sensors installed on the main girder,achieving identification accuracies above 95.8%in all cases.The proposed method shows high identification accuracy and generalization ability across various damage scenarios.

基于开源软件的校园网监控体系

快速发现网络故障、定位故障点,需要对接入设备进行管理,了解网络的运行情况,包括端口数据流量、设备运行性能和状况等各项指标.通过开源软件设计了一种利用遵循SNMP协议的高效监控系统,来获取校园网络节点状态信息的有效方法.并将校园网接入设备状况以非常直观的形式显示出来,以便有效地提高网络管理水平.

Tomato detection method using domain adaptive learning for dense planting environments

This study aimed to address the challenge of accurately and reliably detecting tomatoes in dense planting environments,a critical prerequisite for the automation implementation of robotic harvesting.However,the heavy reliance on extensive manually annotated datasets for training deep learning models still poses significant limitations to their application in real-world agricultural production environments.To overcome these limitations,we employed domain adaptive learning approach combined with the YOLOv5 model to develop a novel tomato detection model called as TDA-YOLO(tomato detection domain adaptation).We designated the normal illumination scenes in dense planting environments as the source domain and utilized various other illumination scenes as the target domain.To construct bridge mechanism between source and target domains,neural preset for color style transfer is introduced to generate a pseudo-dataset,which served to deal with domain discrepancy.Furthermore,this study combines the semi-supervised learning method to enable the model to extract domain-invariant features more fully,and uses knowledge distillation to improve the model's ability to adapt to the target domain.Additionally,for purpose of promoting inference speed and low computational demand,the lightweight FasterNet network was integrated into the YOLOv5's C3 module,creating a modified C3_Faster module.The experimental results demonstrated that the proposed TDA-YOLO model significantly outperformed original YOLOv5s model,achieving a mAP(mean average precision)of 96.80%for tomato detection across diverse scenarios in dense planting environments,increasing by 7.19 percentage points;Compared with the latest YOLOv8 and YOLOv9,it is also 2.17 and 1.19 percentage points higher,respectively.The model's average detection time per image was an impressive 15 milliseconds,with a FLOPs(floating point operations per second)count of 13.8 G.After acceleration processing,the detection accuracy of the TDA-YOLO model on the Jetson Xavier NX development board is 90.95%,the mAP value is 91.35%,and the detection time of each image is 21 ms,which can still meet the requirements of real-time detection of tomatoes in dense planting environment.The experimental results show that the proposed TDA-YOLO model can accurately and quickly detect tomatoes in dense planting environment,and at the same time avoid the use of a large number of annotated data,which provides technical support for the development of automatic harvesting systems for tomatoes and other fruits.