A condition assessment method for roadside assets

Infrastructure condition assessments provide key information for monitoring the quality of infrastructure, planning and budgeting of maintenance and rehabilitation activities and establishing performance goals. Although the literature is rich in condition assessment methods for roadway pavement and bridges (and to some extent for traffic signs), it is lacking such methods for roadside assets. This paper describes the development and validation of a condition assessment method for 12 roadside asset types and maintenance activities that are related to roadway safety, drainage, cleanliness and vegetation. These assets and maintenance activities are located in the area between the outside edges of the outside shoulders and the right-of-way boundaries. On divided highways, the median is also included. The developed condition assessment method consists of a set of performance standards, a visual inspection procedure to assess compliance with these standards and a statistical analysis procedure to determine the roadside level of service. To test and validate the developed method, it was applied to five highway corridors in Texas, USA, representing different climatic conditions, topography, traffic volume and population density (urban vs. rural). These field trials provided insights into the developed method, including practicality, reproducibility and probability distribution function which best represents the sample unit score.     

The use of nested sampling for prediction of infrastructure degradation under uncertainty

Because of the competing demands for scarce resources (funds, manpower, etc) national road owners are required to monitor the condition and performance of infrastructure elements through an effective inspection and assessment regime as part of an overall asset management strategy, the primary aim being to keep the asset in service at minimum cost. A considerable amount of information is then already available through existing databases and other information sources. Various analyses have been carried out to identify the different forms of deterioration affecting infrastructures, to investigate the parameters controlling their susceptibility to, and rate of, deterioration. This paper proposes such an approach by building a transition matrix directly from the condition scores. The Markov assumption is used stating that the condition of a facility at one inspection only depends on the condition at the previous inspection. With this assumption, the present score is the only one which is taken into account to determine the future of the facility. The objective is then to combine nested sampling with a Markov-based estimation of the condition rating of infrastructure elements to put some confidence bounds on Markov transition matrices, and ultimately on corresponding maintenance costs.     

Integrative modeling of performance deterioration and maintenance effectiveness for infrastructure assets with missing condition data

Deterioration modeling is an important analytical component in infrastructure asset management. It concerns the prediction of performance and remaining service life of assets of different designs under omnifarious working environments. For long‐term prediction, it also requires to characterize maintenance effectiveness because maintenance activities do not necessarily bring an asset to a completely renewed status. Deterioration modeling research has for decades been largely focusing on the modeling of the natural deterioration process per se, whereas the modeling of maintenance effectiveness is only a recent focus of investigation, mainly in pavement research. In practice, the asset conditions immediately before and after a given maintenance treatment both are not often known. This has made the modeling of maintenance effectiveness and long‐term deterioration prediction a challenging task. To bridge the gap, this paper presents a novel approach that integrates the modeling of deterioration and maintenance effectiveness into one process. The natural deterioration of asset performance is modeled as a continuous‐time Markov chain, whereas the effectiveness of a maintenance measure is modeled as a discrete‐time Markov chain. To account for missing condition data before and after the maintenance event, the paper also develops a robust statistical method based on Markov chain Monte Carlo simulation. A real‐life case study on a municipal sewer pipe system is carried out for demonstration of the proposed integrated modeling approach. The functional deterioration of sewer pipes and the effectiveness of flushing operations that target to bring flow capacity to intact state are modeled. Influences of pipe length, diameter, slope, and sewershed area are examined. The present work is a valuable step toward development of evidence‐based risk‐informed asset management framework.     

A data‐driven approach to determining freeway incident impact areas with fuzzy and graph theory‐based clustering

Determining spatiotemporal impact areas of incidents plays a significant role in incident impact analysis. Although existing empirical methods have proven to be promising, they suffer from the drawbacks that limit their wide applications in automated freeway safety management. This study presents a data‐driven approach to automatically determining the spatiotemporal impact areas of freeway incidents. The spatiotemporal contour plots were first constructed using three representative traffic measures. Next, a nonrecurrent congestion area identification method based on fuzzy clustering was developed. To distinguish possible multiple independent blocks in the nonrecurrent congestion area, a clustering algorithm based on graph theory was adopted. The incident impact areas were then determined by conducting a postprocessing strategy. The incident records and the associated traffic flow data, collected on I‐5 freeway segments in San Diego Region, CA, were used to evaluate the proposed approach. Experimental results show the proposed approach can automatically and properly determine incident impact areas while accounting for the uncertainty resulting from traffic variations.     

Zernike‐moment measurement of thin‐crack width in images enabled by dual‐scale deep learning

Although crack inspection is a routine practice in civil infrastructure management (especially for highway bridge structures), it is time‐consuming and safety‐concerning to trained engineers and costly to the stakeholders. To automate this in the near future, the algorithmic challenge at the onset is to detect and localize cracks in imagery data with complex scenes. The rise of deep learning (DL) sheds light on overcoming this challenge through learning from imagery big data. However, how to exploit DL techniques is yet to be fully explored. One primary component of practical crack inspection is that it is not merely detection via visual recognition. To evaluate the potential risk of structural failure, it entails quantitative characterization, which usually includes crack width measurement. To further facilitate the automation of machine‐vision‐based concrete crack inspection, this article proposes a DL‐enabled quantitative crack width measurement method. In the detection and mapping phase, dual‐scale convolutional neural networks are designed to detect cracks in complex scene images with validated high accuracy. Subsequently, a novel crack width estimation method based on the use of Zernike moment operator is further developed for thin cracks. The experimental results based on a laboratory loading test agree well with the direct measurements, which substantiates the effectiveness of the proposed method for quantitative crack detection.     

Bayesian Modeling of External Corrosion in Underground Pipelines Based on the Integration of Markov Chain Monte Carlo Techniques and Clustered Inspection Data

In this study, a model is developed to assess external corrosion in buried pipelines based on the unification of Bayesian inferential structure derived from Markov chain Monte Carlo techniques using clustered inspection data. This proposed stochastic model combines clustering algorithms that can ascertain the similarity of corrosion defects and Monte Carlo simulation that can give an accurate probability density function estimation of the corrosion rate. The metal loss rate is chosen as the indicator of corrosion damage propagation, obeying a generalized extreme value (GEV) distribution. Bayesian theory was employed to update the probability distribution of metal loss rate as well as the GEV parameters in order to account for the model uncertainty. The proposed model was validated with direct and indirect inspection data extracted from a 110‐km buried pipeline system.     

A Clustering Ensemble approach based on the similar ities in 2- mode social networ ks

For a particular clustering problems, selecting the best clustering method is a challenging problem.Research suggests that integrate the multiple clustering can improve the accuracy of clustering ensemble greatly. A new clustering ensemble approach based on the similarities in 2-mode networks is proposed in this paper. First of all, the data object and the initial clustering clusters transform into 2-mode networks, then using the similarities in 2-mode networks to calculate the similarity between different clusters iteratively to refine the adjacency matrix, K-means algorithm is finally used to get the final clustering, then obtain the final clustering results.The method effectively use the similarity between different clusters,example shows the feasibility of this method.     

EMS‐WSV – Ein Erhaltungsmanagementsystem für Verkehrswasserbauwerke

A maintenance system of the German Federal waterways infrastructure. The German waterways network including the associated infrastructure facilities such as locks, weirs, culverts, canal bridges, bridges and lighthouses, has a fixed assets of around 40 billion €. The age structure of the hydraulic engineering structures becomes more and more unfavourable. Regarding locks, more than 35% are older than 80 years and have therefore exceeded their service life. Requirement for a technically reasonable and economic maintenance of the waterways infrastructure is an IT‐based maintenance system based on regular inspections. The article describes the approach of the German Federal Waterways and Shipping Administration (Wasser‐ und Schifffahrtsverwaltung des Bundes, WSV) for the inspection of the infrastructure facilities and gives an overview of the development of a maintenance system, which is currently worked out by the Federal Waterways Engineering and Research Institute (Bundesanstalt für Wasserbau, BAW).     

Fractal Dimension‐Based Damage Detection Method for Beams with a Uniform Cross‐Section

Abstract: For civil structures, damage usually occurs in localized areas. As fractal dimension (FD) analysis can provide insight to local complexity in geometry, a damage detection approach based on Katz's estimation of the FD measure of displacement mode shape for homogeneous, uniform cross‐sectional beam structures is proposed in this study. An FD‐based index for damage localization (FDIDL) is developed utilizing the difference of angles of sliding windows between two successive points, which is expressed in FD. To improve robustness against noise, FDIDL is calculated using multisliding windows. The influence of the spatial sampling interval length and the number of 2‐sampling sliding windows on sensitivity to damage and robustness against noise is investigated. The relationship between the angle expressed in FD and the modal strain energy is established and thereby an FD‐based index for the estimation of damage extent (FDIDE) is presented. The two damage indices are applied to a simply supported beam to detect the simulated damage in the beam. The results indicate that the proposed FDIDL can locate the single or multiple damages, and FDIDE can reliably quantify the damage extent. The optimal spatial sampling interval and the number of sliding windows are investigated. Furthermore, the simulation with measurement noise is carried out to demonstrate the effectiveness and robustness of the two defined FD‐based damage indices. Finally, experiments are conducted on simply supported steel beams damaged at different locations. It is demonstrated that the proposed approach can locate the damages to a satisfactory precision.     

Using economic asset valuation to meet rehabilitation priority needs in the water sector

ABSTRACT

The current study aims at economic asset valuation of water and wastewater infrastructures to prioritize rehabilitation interventions using the infrastructure value index (IVI) as a key performance indicator. The proposed methodology follows a three-step procedure: (1) identification, characterization and site inspection of existing assets; (2) estimation of the current replacement cost and the current value; and (3) establishment of the priority needs. Data from a utility in the Algarve region of Portugal are used as a case study. The specific objectives are to illustrate the methodology and highlight the inherent challenges and complexities associated with economic asset valuation. Results include the IVI calculation for different assets using scenarios of service lives. Different strategic interventions of rehabilitation in the long term were studied, allowing to assess the evolution of IVI, rehabilitation cost and increased risk assets. Asset valuation is important to establish short-term rehabilitation priorities and to analyse long-term strategies of rehabilitation.     

Unified Vision‐Based Methodology for Simultaneous Concrete Defect Detection and Geolocalization

Vision‐based autonomous inspection of concrete surface defects is crucial for efficient maintenance and rehabilitation of infrastructures and has become a research hot spot. However, most existing vision‐based inspection methods mainly focus on detecting one kind of defect in nearly uniform testing background where defects are relatively large and easily recognizable. But in the real‐world scenarios, multiple types of defects often occur simultaneously. And most of them occupy only small fractions of inspection images and are swamped in cluttered background, which easily leads to missed and false detections. In addition, the majority of the previous researches only focus on detecting defects but few of them pay attention to the geolocalization problem, which is indispensable for timely performing repair, protection, or reinforcement works. And most of them rely heavily on GPS for tracking the locations of the defects. However, this method is sometimes unreliable within infrastructures where the GPS signals are easily blocked, which causes a dramatic increase in searching costs. To address these limitations, we present a unified and purely vision‐based method denoted as defects detection and localization network, which can detect and classify various typical types of defects under challenging conditions while simultaneously geolocating the defects without requiring external localization sensors. We design a supervised deep convolutional neural network and propose novel training methods to optimize its performance on specific tasks. Extensive experiments show that the proposed method is effective with a detection accuracy of 80.7% and a localization accuracy of 86% at 0.41 s per image (at a scale of 1,200 pixels in the field test experiment), which is ideal for integration within intelligent autonomous inspection systems to provide support for practical applications.     

受贯通裂隙控制岩体巷道稳定性试验研究

研究了构造控制性裂隙与巷道布置不同匹配对回采巷道的稳定性影响规律，分析了裂隙间距（I），裂隙倾角（φ）及其裂隙方位和巷道轴向夹角（α）等因素对巷道围岩敛变形的影响，提出构造控制性贯通裂隙条件下保证巷道稳定及其维护的裂隙－巷道轴线匹配布置原则.     

A Condition‐Based Deterioration Model for the Stochastic Dependency of Corrosion Rate and Crack Propagation in Corroded Concrete Structures

Physics‐based models are intensively studied in mechanical and civil engineering but their constant increase in complexity makes them harder to use in a maintenance context, especially when degradation model can/should be updated from new inspection data. On the other hand, Markovian cumulative damage approaches such as Gamma processes seem promising; however, they suffer from lack of acceptability by the civil engineering community due to poor physics considerations. In this article, we want to promote an approach for modeling the degradation of structures and infrastructures for maintenance purposes which can be seen as an intermediate approach between physical models and probabilistic models. A new statistical, data‐driven state‐dependent model is proposed. The construction of the degradation model will be discussed within an application to the cracking of concrete due to chloride‐induced corrosion. Numerical experiments will later be conducted to identify preliminary properties of the model in terms of statistical inferences. An estimation algorithm is proposed to estimate the parameters of the model in cases where databases suffer from irregularities.     

Condition grading for dysfunction indicators in sewer asset management

Asset management is an increasing concern for the water and wastewater industry. Condition assessment of sewer segments is an important component of sewer asset management and relies mostly on visual inspection. Observed defects are translated into a score for each segment. Although most protocols give a segment a condition grade by comparing its score with a subjective scale of numerical values, we propose a protocol to calibrate thresholds for each asset stock. Thresholds are calculated according to two sets of parameters: overall condition of the asset stock in question (estimated by a representative sample or provided by the utility manager) and assignment-error weighting (determined by the utility manager) linked to either over-estimation or under-estimation of condition grade. This method is applied to 150 km of sewers from the Greater Lyon asset stock. Sensitivity analyses of these parameters are then implemented. Three hypotheses about overall condition of the asset stock are combined with three matrices of assignment-error weights. Both parameters influence thresholds and change the assessment of the studied segments. The synthesis of such sensitivity analyses can be used to prioritise complementary investigations.     

Algorithm for Spatial Clustering of Pavement Segments

Abstract:   An algorithm is developed to enable the Georgia Department of Transportation (GDOT) to determine pavement preservation project termini by analyzing segment-level pavement condition rating. This article formulates a new spatial search model for determining appropriate pavement preservation project termini. A spatial clustering algorithm using fuzzy c-mean clustering is developed to minimize the rating variation in each cluster (project) of pavement segments while considering minimal project scope (i.e., length) and cost, initial setup cost, and barriers, such as bridges. A case study using the actual roadway and pavement condition data in fiscal year 2005 on Georgia State Route 10 shows that the proposed algorithm can identify more appropriate segment clustering scheme, than the historical project termini. The benefits of using the developed algorithm are summarized, and recommendations for future research are discussed.     

Integration of the component of financial statement in highway maintenance planning

Highway agencies attempt to apply efficient accounting systems to help improve the transparency of their budget allocation and their overall performance. The accrual accounting system has been adopted to substitute the cash accounting system by many central governments around the world. The system reports transactions of economic value changes rather than when cash movements are made. The asset value and its depreciation determined by the accrual accounting system illustrate the total amount of economic value owned by an agency and the consumption of the future economic benefits for an asset. The age‐based depreciation approach, which is a traditional approach, is applied to determine the depreciation of an asset based on the defined useful life and the straight‐line depreciation method. The advantage of this approach is its simplicity and its ability to support the long‐term asset management system. The condition‐based depreciation is recommended as an alternative approach since the actual condition of an asset can reflect its depreciation. The depreciation of an asset based on the condition‐based approach is suitable for single‐year maintenance planning since the executives cannot estimate the required budget for replacing or preserving of an asset in the long‐term period. This research focuses on developing the new depreciation approach by integrating the age‐based and the condition‐based depreciation approaches in order to strategically plan long‐term highway maintenance and to enable the actual condition of an asset to be reflected.     

Image‐based crack assessment of bridge piers using unmanned aerial vehicles and three‐dimensional scene reconstruction

Crack assessment of bridge piers using unmanned aerial vehicles (UAVs) eliminates unsafe factors of manual inspection and provides a potential way for the maintenance of transportation infrastructures. However, the implementation of UAV‐based crack assessment for real bridge piers is hindered by several key issues, including the following: (a) both perspective distortion and the geometry distortion by nonflat structural surfaces usually appear on crack images taken by the UAV system from the pier surface; however, these two kinds of distortions are difficult to correct at the same time; and (b) the crack image taken by a close‐range inspection flight UAV system is partially imaged, containing only a small part of the entire surface of the pier, and thereby hinders crack localization. In this paper, a new image‐based crack assessment methodology for bridge piers using UAV and three‐dimensional (3D) scene reconstruction is proposed. First, the data acquisition of UAV‐based crack assessment is discussed, and the UAV flight path and photography strategy for bridge pier assessment are proposed. Second, image‐based crack detection and 3D reconstruction are conducted to obtain crack width feature pair sequences and 3D surface models, respectively. Third, a new method of projecting cracks onto a meshed 3D surface triangular model is proposed, which can correct both the perspective distortion and geometry distortion by nonflat structural surfaces, and realize the crack localization. Field test investigations of crack assessment of a real bridge pier using a UAV are carried out for illustration, validation, and error analysis of the proposed methodology.