Influence of available data on sewer inspection program efficiency

One key aspect of sewer inspection programs is the prediction of sewer condition. Despite the development of deterioration models, the influence of available data on models' predictive power has not been studied in depth. In this article, numerical experiments on a semi-virtual asset stock have been conducted to answer two main questions: how to establish a list of the most informative factors and whether it is better to have data imprecision instead of data incompleteness in a utility database. Two approaches for establishing a list of the most informative factors are compared. The results show a statistical analysis (a priori analysis) can predict the impact of available data on inspection program efficiency (a posteriori analysis). This can be used to plan data acquisition programs. Finally, we show that using the notion of “district” (data imprecision) can provide efficient results when the most informative factor “age” is not available (data incompleteness).     

Computational fluid dynamics based assessment of discharge-water depth relationships for combined sewer overflows

This article describes a general method to assess discharge-water depth relationships for combined sewer overflow (CSO) chambers. This method is particularly suitable for: complex geometries, unavailable univoc relationship between upstream and downstream flow and complex downstream hydraulic conditions. The methodology presented in this article is based on computational fluid dynamics. Its aim is to propose a relationship between the water depths in the CSO chamber and the overflow discharge. This study focuses on the evaluation and integration of the uncertainties in the determination of the most suitable depth-overflow relationship. Uncertainties related to numerical model, boundary conditions and water depth have been taken into account in order to elaborate water depth-overflow rate relationship. This approach is illustrated through the example of the ‘Milan’ CSO of Mulhouse city, France.     

钢筋的混凝土保护层厚度检测技术探讨

钢筋的混凝土保护层厚度检测作为强制性验收检测的内容之一,在全国范围内得到了广泛应用。对于新建结构验收检测、既有建筑质量检测及结构评估检测等不同检测目的,在检测的抽样数量、检测部位、现场检测技术、测试精度、结果评定等方面,各个地区存在很大的差异。本文结合实际工作情况对上述内容进行探讨,并就如何做好钢筋的混凝土保护层厚度检测提出一些观点。     

Benefits of using basic,imprecise or uncertain data for elaborating sewer inspection programmes

One key goal of sewer inspection programmes is to target segments in the worst condition. Despite the development of deterioration models, the influence of available data on models’ predictive power has not been studied in depth yet. In this article, numerical experiments have been conducted to answer three main questions: (1) How can the data most probably available within a utility be used to define an effective inspection programme? (2) Can we use an auxiliary variable in order to compensate effects of missing data on inspection programmes? (3) Is it worth to accept a degree of uncertainty within data instead of not having them? In other words, is it preferable to have uncertainty instead of incompleteness within utility database? In order to respond to these questions, we considered an asset stock and then degraded the information by introducing uncertainty, imprecision and incompleteness within, to form a utility's database. The results show that significant improvement of inspection programmes could be achieved by using the most probably available data within utilities. We also show that using the notion of ‘district’ can provide efficient results when the most informative factor ‘age’ is not available. Finally, it is shown that having uncertain data is preferable to having incompleteness.     

钢筋的混凝土保护层厚度检测技术探讨

钢筋的混凝土保护层厚度检测作为强制性验收检测的内容之一,在全国范围内得到了广泛应用。对于新建结构验收检测、既有建筑质量检测及结构评估检测等不同检测目的,在检测的抽样数量、检测部位、现场检测技术、测试精度、结果评定等方面,各个地区存在很大的差异。本文结合实际工作情况对上述内容进行探讨,并就如何做好钢筋的混凝土保护层厚度检测提出一些观点。     

基于单目视觉和简单手势的空间交互技术

和传统接触型交互设备相比,新颖的非触摸型空间交互技术在空间自由度方面有着明显优势.主要探讨基于单目摄像头和简单手势的双手空间交互技术.首先利用肤色分割、滤波优化、运动轨迹预测等算法实现了双手的跟踪与识别.由于需要用空间手势及其运动状态来替代鼠标,提出了基于中心偏离控制和基于线性映射的两种鼠标映射模式,进而实现了四种基本空间手势.基于提出的空间交互技术,实现了软件控制、游戏体验等多个应用.最后讨论了基于单目视觉的双手空间交互技术在手部分割、学习算法、交互方式等方面的改进方向.     

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.     

Optical flow techniques for estimation of camera motion parameters in sewer closed circuit television inspection videos

This paper discusses a novel approach for automated analysis and tracking of camera motion in sewer inspection closed circuit television (CCTV) videos. This approach represents an important building block for any system that supports automated analysis and defect detection of CCTV videos. The proposed approach employs optical flow techniques to automatically identify, locate, and extract a limited set of video segments, called regions of interest (ROI), which likely include defects, thus reducing the time and computational requirements needed for video processing. Tracking the camera motion parameters is used to recover the operator actions during the inspection session, which would provide important clues about the location and severity of the ROI. Techniques for estimating the camera travelling distance, position inside the sewer, and direction of motion from optical flow vectors are discussed. The proposed techniques were validated using a representative set of sewer CCTV videos obtained from the cities of Regina and Calgary, Canada.     

超声波平测法检测混凝土裂缝深度

以简化数学模型为基础,推导了超声波平测法检测裂缝深度的计算公式。根据超声波的传播原理,研究了超声波声速、实际声程和裂缝深度值的确定方法。在实验室制作试件,预设不同深度的垂直裂缝,采用平测法测定裂缝深度值,在此基础上,探讨了超声波平测法测定混凝土裂缝深度的精度和适用范围。     

基于置信度议混凝土强度批量检测

在对混凝土强度进行批量检测时,需根据样本的平均值、标准差推定检验批的混凝土强度.国家现行规范有三种方法推定混凝土强度:①计算推定区间,并以推定区间的上限值作为混凝土强度推定值;②计算推定区间,并根据实际情况在推定区间取值;③用样本平均值减去1.645倍标准差作为推定值.本文通过分析三种方法的计算的理论依据,发现以推定区...     

基于运行速度检验公路路线设计的研究

结合某项目一级公路的勘察设计,运用运行速度检验的方法,研究了运行速度与路线线形之间的规律,研究结果表明：对设计速度与运行速度之差大于20 km/h的路段,应从路线平、纵、横断面上提出改进措施;应用运行速度检验结果,合理设置爬坡车道、紧急避险车道等设施。     

基于BP网络的混凝土碳化深度预测

基于用改进的BP算法,建立了3-5-1（输入层为3个神经元,隐含层为5个神经元,输出层为1个神经元）混凝土碳化深度BP网络训练及预测模型,模型以水灰比、单位水泥用量及混凝土暴露时间为输入,训练模型以暴露时间为5年、10年、15年的混凝土碳化深度为输出,预测模型以暴露时间为20年、25年、30年的混凝土碳化深度为输出,训练及预测结果较为理想。     

Development of track condition assessment model based on visual inspection

Inspection and evaluation of railway track conditions are usually accomplished by analysis of geometric data obtained from track recording cars. Although most geometric deviations can be recorded by these cars, track structural defects cannot be identified during automated inspections and the causes of track problems cannot be fully recognised. This limitation makes the efficiency of current maintenance approaches questionable. This research proposes a new method of track evaluation based on the observation of track structural defects. Dominant observable structural distresses were studied and a methodology was proposed to translate structural problems into a meaningful numerical rating. The contribution of a track structural defect to track quality was investigated and a track structural quality index model was developed. This index is a function of the severity and density of distress types. To examine the practicability and applicability of the new index, a practical use of the model is presented.     

The effects of scum on sewer flows

This paper presents the results of an experimental study on the effects of scum on sewer flows. In this study, experiments were made in a laboratory flume taking into account two different kinds of scum, representative of the real scum typologies, which can be found in sewers: the first kind is mobile scum, made up of oil, grease and small pieces of wood; the second is fixed scum, made up of foam. Experiments have shown the reduction in flow velocity and the increase in water depth due to the presences of the scum, depending on the various hydraulic conditions and the scum characteristics.

The experiments allowed the derivation of a correction factor that can be utilized to determine flow velocity reduction under different operational conditions of flow. Engineers can make use of the results of the study in order to design more reliable sewer channels, which do not get surcharged in the presence of scum.     

Automated defect detection in sewer closed circuit television images using histograms of oriented gradients and support vector machine

Condition assessment of municipal sewer pipes using closed circuit television (CCTV) inspections is known to be time consuming, costly, and prone to errors primarily due to operator fatigue or novicity. Automated detection of defects can provide a valuable tool for ensuring the quality, accuracy, and consistency of condition data, while reducing the time and cost of the inspection process. This paper presents an efficient pattern recognition algorithm to support automated detection and classification of pipe defects in images obtained from conventional CCTV inspection videos. The algorithm employs the histograms of oriented gradients (HOG) and support vector machine (SVM) to identify pipe defects. The algorithm involves two main steps: (1) image segmentation to extract suspicious regions of interest (ROI) that represent candidate defect areas; and (2) classification of the ROI using SVM classifier that was trained using sets of HOG features extracted from positive and negative examples of the defect. Proposed algorithm is applied to the problem of detecting tree root intrusions. The performance of linear and radial basis function SVM classifiers evaluated. The algorithm was tested on a set of actual CCTV videos obtained from the cities of Regina and Calgary in Canada. Experimental results demonstrated the viability and robustness of the algorithm.     

基于ADAMS的铁路桥梁检查车动力学仿真

铁路桥梁检查车是对铁路桥梁进行检查的专用车辆，其上车作业部分的运动机构有2个回转机构、1个变幅机构、3个折臂机构、3个伸缩机构和1个调平机构，运动组合状态十分复杂。为了在设计开发阶段全面分析车辆在任意运动状态下各零部件受力情况，本文采用ADAMS虚拟样机技术，建立铁路桥梁检查车上车系统的ADAMS模型，通过已知各运动部件的运动规律，确定各液压油缸工作载荷及各运动副的反力，为系统各零部件强度分析及液压系统设计提供依据。     

Vision-based workface assessment using depth images for activity analysis of interior construction operations

Workface assessment – the process of determining the overall activity rates of onsite construction workers throughout a day – typically involves manual visual observations which are time-consuming and labor-intensive. To minimize subjectivity and the time required for conducting detailed assessments, and allowing managers to spend their time on the more important task of assessing and implementing improvements, we propose a new inexpensive vision-based method using RGB-D sensors that is applicable to interior construction operations. This is a particularly challenging task as construction activities have a large range of intra-class variability including varying sequences of body posture and time-spent on each individual activity. The skeleton extraction algorithms from RGB-D sequences produce noisy outputs when workers interact with tools or when there is a significant body occlusion within the camera's field-of-view. Existing vision-based methods are also limited as they can primarily classify “atomic” activities from RGB-D sequences involving one worker conducting a single activity. To address these limitations, our method includes three components: 1) an algorithm for detecting, tracking, and extracting body skeleton features from depth images; 2) a discriminative bag-of-poses activity classifier for classifying single visual activities from a given body skeleton sequence; and 3) a Hidden Markov Model to represent emission probabilities in the form of a statistical distribution of single activity classifiers. For training and testing purposes, we introduce a new dataset of eleven RGB-D sequences for interior drywall construction operations involving three actual construction workers conducting eight different activities in various interior locations. Our results with an average accuracy of 76% on the testing dataset show the promise of vision-based methods using RGB-D sequences for facilitating the activity analysis workface assessment.     

Evaluating the system reliability of corroding pipelines based on inspection data

This paper presents a methodology for evaluating the time-dependent system reliability of a pressurised gas pipeline segment containing multiple active metal-loss corrosion defects. The methodology incorporates three distinctive failure modes of the pipe segment due to corrosion, namely small leak, large leak and rupture. The growth of the depth of individual corrosion defect is assumed to follow a power-law function of time. The Bayesian updating and Markov Chain Monte Carlo (MCMC) simulation techniques are used to quantify the parameters of the power-law growth model based on data obtained from multiple inspections carried out at different times. The simple Monte Carlo and MCMC techniques are combined to evaluate the system reliability. A numerical example involving an in-service gas pipeline located in Alberta, Canada, is used to illustrate the proposed methodology. Results of the sensitivity analysis suggest that the use of a defect-specific or segment-specific growth model for the defect depth has a marked impact on the evaluated system reliability. The proposed methodology can be incorporated in reliability-based pipeline corrosion management programmes to assist integrity engineers in making informed decisions about defect repair and mitigation.