塑料排水管道破裂等级评价的分析研究

城镇排水管道在运行的过程中,常会由于各种原因造成管道破裂。这些破裂缺陷如何影响排水管道承载能力,是排水管道缺陷等级定义和评估要解决的问题。目前国内对排水管道破裂缺陷的研究比较有限,本文主要利用塑料管道作为模型试件,参照《城镇排水管道检测与评估技术规程》CJJ181-2012对破裂缺陷等级的划分,对试件模拟了叉形和圆形缺陷,并进行环刚度、抗压强度、外压破坏荷载试验研究,探讨管道破裂缺陷等级与缺陷数量的等效关系,以及缺陷位置和缺陷分布对管道破坏的影响。结果表明,1个2级叉形破裂缺陷与4个1级叉形破裂缺陷是等效的;1个4级圆形破裂缺陷与7个3级圆形破裂缺陷是等效的;相比排水管道顶部,管道侧壁是排水塑料管道的缺陷敏感区域;管道缺陷分布越均匀,对管道破坏程度越大。     

基于深度学习的排水管道缺陷内窥检测智能识别系统研究

目前国内各城市已普遍采用管道机器人深入管道内部摄取视频影像,有效获取到可供管道缺陷检测的一手资料,但缺陷识别大部分依靠人工目视识别,耗时耗力,生产周期长。利用福州市勘测院多年累积的管道检测数据,基于Pytorch深度学习框架、建立了排水管道缺陷内窥检测智能识别系统,包括:数据预处理,残差神经网络设计与训练、系统集成等。重点实现了三级组合识别模型建构(二分类,类型识别,等级识别),解决了系统准确度等技术难题。经生产实践表明:模型准确率高,可有效提高管道健康状况检查质量和效率。     

A Fast Detection Method via Region‐Based Fully Convolutional Neural Networks for Shield Tunnel Lining Defects

Tunnel lining defects are an important indicator reflecting the safety status of shield tunnels. Inspired by the state‐of‐the‐art deep learning, a method for automatic intelligent classification and detection methodology of tunnel lining defects is presented. A fully convolutional network (FCN) model for classification is proposed. Information about defects, collected using charge‐coupled device cameras, was used to train the model. The model's performance was compared to those of GoogLeNet and VGG. The best‐set accuracy of the proposed model was over 95% at a test‐time speed of 48 ms per image. For defects detection, image features were computed from large‐scale images by the FCN and then detected using a region proposal network and position‐sensitive region of interest pooling. Some indices (detection rate, detection accuracy, and detection efficiency, locating accuracy) were used to evaluate the model. The comparisons with faster R‐CNN and a traditional method were conducted. The results show that the model is very fast and efficient, allowing automatic intelligent classification and detection of tunnel lining defects.     

基于声呐成像的水下桩墩表观病害深度学习与智能检测

声呐成像检测水下桩墩表观病害时，其图像与光学图像的病害特征存在较大差异，病害的位置和类型需要人工识别且易出错。为解决这个问题，提出基于声呐成像的水下桩墩表观病害深度学习与智能检测方法。首先对水下实桥桩墩以及试验模型进行声呐扫描获取大量图像，并分析声呐图像中的病害特征；然后对Faster R-CNN框架下VGG16网络模型进行改进，采用水平、垂直等线性变换实现原始声呐图像的数据增强，对深度学习模型进行近似联合优化训练，用一定概率保证率的矩形识别框实现水下桩墩多类病害的分类定位；最后选取150幅未参与训练的声呐图像进行识别，验证所提出方法的有效性，并通过混淆矩阵、精确率、召回率、准确率以及F1值等评价指标对识别方法性能进行研究。研究结果发现，桩墩孔洞、剥落和位移等病害以及无病害类型的识别结果的总体准确率为88.3%，F1值分别为90.1%、84.9%、78.7%和94.6%，平均F1值为87%。这说明该方法在水下桩墩表观病害识别、定位以及自动化处理方面是可行、有效的，为桥梁水下桩墩表观病害的图像处理、智能化检测与桥梁安全评估提供技术支撑。     

热力管道缺陷弱磁检测技术仿真与试验验证

针对热力管道缺陷,提出基于磁场梯度的非开挖弱磁检测技术.介绍基于磁梯度法的弱磁检测原理.建立管道三维模型,对管道环形凹槽缺陷、穿孔缺陷的磁感应强度变化进行仿真模拟,试验验证弱磁检测技术的可行性.由仿真结果可知:对于环形凹槽缺陷,磁感应强度在环形凹槽缺陷处明显异常,环形凹槽深度越大,变化越小.对于穿孔缺陷,磁感应强度在穿...     

排水塑料管破裂缺陷等效性的研究

根据《城镇排水管道检测与评估技术规程》CJJ181-2012对破裂缺陷等级的划分,对有缺陷的排水塑料管道进行环刚度、抗压强度、外压破坏荷载试验研究,探讨管道在破裂缺陷等级与缺陷数量的等效关系以及缺陷位置和缺陷分布对管道破坏的影响。结果表明,多个低级缺陷与1个高一级的缺陷之间有一定的等效关系;管道缺陷的位置和分布对管道的破坏程度有一定的影响。     

城镇排水管道管理维护中的关键技术

提出了在城镇排水管道维护管理中,如何做好验收接收排水管道、排水许可管理、排水管道检查、管道疏通、维护维修、档案与信息管理等问题。最后对排水管道具体问题提出了改进措施。     

大型检查井、溢流井以及顶管井的特殊施工方法

针对城市排水管网中大型检查井、溢流井及顶管井施工时存在的问题，提出了倒挂井施工方式，介绍了倒挂井的分类及其具体的施工方法和注意事项。倒挂井施工方式已在全国很多城市的排水管网施工中得到成功应用，既解决了施工难题，又取得了良好的经济效益和社会效益。     

Inspection and maintenance planning of pipeline under external corrosion considering generation of new defects

Safe operation of aging pipeline systems under external corrosion can be achieved through inspection and maintenance programs. Tools used for the pipeline inspection are uncertain in detecting a corrosion defect and in sizing a detected defect. The process of generation of new corrosion pits is an uncertain process. These uncertainties must be taken into account in the reliability analysis and in the pipeline inspection and maintenance planning. In this paper the effect of corrosion defect size on the remaining pipeline strength is modeled by a Markov process. Analytical solution of the probability transition matrix is obtained by solving the Kolmogorov forward differential equation. The matrix of probability transition function, the probability of defect detection and the probability distribution of sizing a detected defect is incorporated in estimating the probability of failure. The generation of new corrosion defects is modeled by a Poisson process. The optimal inspection and maintenance schedules are selected based on the reliability constraint. The sensitivity of optimal inspection schedule to the quality of inspection tools and to maintenance criteria is illustrated through examples.     

Automated defect detection for sewer pipeline inspection and condition assessment

To regularly and proactively assess conditions of sewer infrastructure systems to ensure their structural integrity and continuity of services, it is critical to advance the state of automated pipeline inspection and condition assessment. Currently, a critical issue is to address realistic defect detection that deals with real sewer inspection data. This paper presents the findings of a research project that seeks to enable automated detection of defects in sewer pipelines from inspection videos and images. The need for and the challenges of automated defect detection in sewer infrastructure condition monitoring are presented. Based on a general detection and recognition model established in this paper, a change detection based approach which is tailored to solve the challenges in this sewer pipeline domain is described and illustrated through case study.     

优化市政排水管网设计探讨

阐述了优化城市排水管网的必要性,结合具体案例,通过分析其排水管网现状,对市政给水管网、排水管网进行了优化设计,以发挥市政管网系统的真正作用,同时为相关研究人士提供理论依据。     

水力模型在城市排水管网改造设计中的应用

水利模型在城市排水管网改造设计中,需要先利用相关的软件将排水管网的模型建立出来,再根据数学模型在排水管网改造的作用进行分析,设计出科学合理的方案,最后利用水力模型对方案进行审核。     

Deep learning-based automatic classification of three-level surface information in bridge inspection

Bridge inspection ensures that in-service bridges are managed and maintained in conformity. To enhance the accuracy and efficiency of bridge inspection, an automatic hierarchical model is proposed, which enables the classification and correlation of bridge surface images at three levels, namely, at the structure, component, and defect type level. Thus, the impact of both the defect types and the affected components on bridge safety can be simultaneously considered. The proposed model uses a group of sub-models instead of the common flat network to realize the multiple tasks, which is advantageous in accuracy, training simplicity, and scalability. The classification accuracy of the hierarchical model in three levels has reached 96%, 92%, and 81%. Results demonstrate the effectiveness of the proposed method in the classification of multi-scale targets. This study may provide a new strategy for developing a systematic and easily adaptable detection framework for practical bridge engineering.     

Neuro-fuzzy network for the classification of buried pipe defects

Pipeline infrastructure is decaying at an accelerating rate due to reduced funding and insufficient quality control resulting in poor installation, little or no inspection and maintenance, and a general lack of uniformity and improvement in design, construction and operation practices. The current practice that is being followed to inspect the conditions of pipes is usually time consuming, tedious and expensive. It may also lead to diagnostic errors due to lack of concentration of human operators. Buried pipe defect classification is thus a practical and important pattern classification problem. These defects appear in the form of randomly shaped cracks and holes, broken joints and laterals, and others. This paper proposes a new neuro-fuzzy classifier that combines neural networks and concepts of fuzzy logic for the classification of defects by extracting features in segmented buried pipe images. A comparative evaluation of the K-NN, fuzzy K-NN, conventional backpropagation network, and proposed neuro-fuzzy projection network classifiers is carried out. Among the five neural methods implemented and tested, the proposed neuro-fuzzy classifier performs the best, with classification accuracies around 90% on real concrete pipe images.     

北京某区域低影响开发措施实施效果分析

以北京市某新建区域的雨水系统为例,利用InfoWorks ICM水力评估软件建立区域地块及周边市政设施的一维雨水系统模型,对区域开发前后的雨水外排量及其排入的市政管线运行情况进行模拟分析。为使地块开发后外排雨水量满足建设区外排水量的要求,以及不对其排入的市政管网运行造成影响,借鉴海绵城市建设理念,提出了几种雨水排除及调蓄的低影响开发方案,并进行综合分析比较,得出可行的区域建设方案。结果表明,下凹式绿地、透水路面等"绿色"低影响开发措施可削峰,"灰色"调蓄池既可削峰又可错峰,且相同容积下离线调蓄池的运行效果较在线调蓄池更优,结合项目实际情况进行"灰""绿"结合更有利于控制雨水外排。该研究提供了一种区域雨水外排评估方法及低影响开发措施在区域中实施的思路。     

MSDF在城市排水管网监测系统中的应用研究

为了保护排水设施,健全我们的城市信息化管理系统,保障城市安全,对影响城市排水安全运行因素进行了综合分析,本文提出了基于Zigbee技术的城市排水管网监测无线传感器网络,从多传感器数据融合(MSDF)的角度对排水管网监测系统的网络以及管网安全状态信息进行了分析和研究,提出了相应的城市排水管网监测系统中的多传感器数据融合模型。从而准确的对排水管网安全运行情况作出判断,建立对排水管网安全运行的风险分析,适时合理的采取措施,预防事故的发生。     

Automated detection of sewer pipe defects in closed-circuit television images using deep learning techniques

Sanitary sewer systems are designed to collect and transport sanitary wastewater and stormwater. Pipe inspection is important in identifying both the type and location of pipe defects to maintain the normal sewer operations. Closed-circuit television (CCTV) has been commonly utilized for sewer pipe inspection. Currently, interpretation of the CCTV images is mostly conducted manually to identify the defect type and location, which is time-consuming, labor-intensive and inaccurate. Conventional computer vision techniques are explored for automated interpretation of CCTV images, but such process requires large amount of image pre-processing and the design of complex feature extractor for certain cases. In this study, an automated approach is developed for detecting sewer pipe defects based on a deep learning technique namely faster region-based convolutional neural network (faster R-CNN). The detection model is trained using 3000 images collected from CCTV inspection videos of sewer pipes. After training, the model is evaluated in terms of detection accuracy and computation cost using mean average precision (mAP), missing rate, detection speed and training time. The proposed approach is demonstrated to be applicable for detecting sewer pipe defects accurately with high accuracy and fast speed. In addition, a new model is constructed and several hyper-parameters are adjusted to study the influential factors of the proposed approach. The experiment results demonstrate that dataset size, initialization network type and training mode, and network hyper-parameters have influence on model performance. Specifically, the increase of dataset size and convolutional layers can improve the model accuracy. The adjustment of hyper-parameters such as filter dimensions or stride values contributes to higher detection accuracy, achieving an mAP of 83%. The study lays the foundation for applying deep learning techniques in sewer pipe defect detection as well as addressing similar issues for construction and facility management.     

Attention-guided multiscale neural network for defect detection in sewer pipelines

Sanitary sewer systems are major infrastructures in every modern city, which are essential in protecting water pollution and preventing urban waterlogging. Since the conditions of sewer systems continuously deteriorate over time due to various defects and extrinsic factors, early intervention in the defects is necessary to prolong the service life of the pipelines. However, prior works for defect inspection are limited by accuracy, efficiency, and economic cost. In addition, the current loss functions in object detection approaches are unable to handle the imbalanced data well. To address the above drawbacks, this paper proposes an automatic defect detection framework that accurately identifies and localizes eight types of defects in closed-circuit television videos based on a deep neural network. First, an effective attention module is introduced and used in the backbone of the detector for better feature extraction. Then, a novel feature fusion mechanism is presented in the neck to alleviate the problem of feature dilution. After that, an efficient loss function that can reasonably adjust the weight of training samples is proposed to tackle the imbalanced data problem. Also, a publicly available dataset is provided for defect detection tasks. The proposed detection framework is robust against the imbalanced data and achieves a state-of-the-art mean average precision of 73.4%, which is potentially applied in realistic sewer defect inspections.     

非金属材质排水管线的综合探测方法研究

针对城市中最常见的非金属材质排水管线,结合徐州市城市排水系统研究——排水管网普查工作,总结出“直接、观察、辨音、询查、物探”的综合探测方法,简述各种方法的实用性及优缺点。基于探测“泰奎大沟”的实例,阐述了使用地质雷达(GPR)获得地下反射波剖面,并分析排水管线反射特征来确定平面位置及深度,得到了很好的效果。五种方法可以综合使用,以获得排水管线埋深、管径、流向、空间位置等属性数据,提高管线探测的准确率,为管线探测者提供宝贵的经验。     

Pavement defect detection with fully convolutional network and an uncertainty framework

Image segmentation has been implemented for pavement defect detection, from which types, locations, and geometric information can be obtained. In this study, an integration of a fully convolutional network with a Gaussian‐conditional random field (G‐CRF), an uncertainty framework, and probability‐based rejection is proposed for detecting pavement defects. First, a fully convolutional network is designed to generate preliminary segmentation results, and a G‐CRF is used to refine the segmentation. Second, epistemic and aleatory uncertainties in the model and database are considered to overcome the disadvantages of traditional deep‐learning methods. Last, probability‐based rejection is conducted to remove unreasonable segmentations. The proposed method is evaluated on a data set of images that were obtained from 16 highways. The proposed integration segments pavement distresses from digital images with desirable performance. It also provides a satisfactory means to improve the accuracy and generalization performance of pavement defect detection without introducing a delay into the segmentation process.