Condition Assessment Data Research Articles

Assessing physical conditions of indoor space enclosing elements in schools in relation to their indoor environmental quality

Field observations conducted during indoor environmental quality studies generally capture relevant building characteristics and occupants’ discomfort coping strategies but do not capture the physical condition of indoor space enclosing elements including walls, floor, and windows despite their likelihood to influence indoor environmental quality. Hence, there is limited empirical evidence on the extent to which the conditions of building elements influence indoor environmental quality. This research investigated the usefulness of the newly developed space level condition assessment instrument for assessing the impact of building elements’ physical conditions on indoor environmental quality particularly in schools. The research involved assessing the physical condition of building elements using the instrument and conducting field measurements of indoor environmental quality parameters in 52 classrooms in new, renovated, and non-renovated schools. Variance analyses of the space level condition assessment data found the most number of statistically significant differences between new and non-renovated schools, and the least between renovated and non-renovated schools. Association analyses found theoretically relevant associations between space level condition assessment scores and indoor environmental quality field measurements, the most significant association being between the main envelope space level condition assessment score and relative humidity in non-renovated schools. Findings of this research should be of interest school divisions looking to improve the indoor environmental quality of their existing schools.

A Maintenance Cost Study of Transformers Based on Markov Model Utilizing Frequency of Transition Approach

In this paper, a maintenance cost study of transformers based on the Markov Model (MM) utilizing the Health Index (HI) is presented. In total, 120 distribution transformers of oil type (33/11 kV and 30 MVA) are examined. The HI is computed based on condition assessment data. Based on the HI, the transformers are arranged according to its corresponding states, and the transition probabilities are determined based on frequency of a transition approach utilizing the transformer transition states for the year 2013/2014 and 2012/2013. The future states of transformers are determined based on the MM chain algorithm. Finally, the maintenance costs are estimated based on future-state distribution probabilities according to the proposed maintenance policy model. The study shows that the deterioration states of the transformer population for the year 2015 can be predicted by MM based on the transformer transition states for the year 2013/2014 and 2012/2013. Analysis on the relationship between the predicted and actual computed numbers of transformers reveals that all transformer states are still within the 95% prediction interval. There is a 90% probability that the transformer population will reach State 1 after 76 years and 69 years based on the transformer transition states for the year 2013/2014 and 2012/2013. Based on the probability-state distributions, it is found that the total maintenance cost increases gradually from Ringgit Malaysia (RM) 5.94 million to RM 39.09 million based on transformer transition states for the year 2013/2014 and RM 37.56 million for the year 2012/2013 within the 20 years prediction interval, respectively.

Transformers Health Index Assessment Based on Neural-Fuzzy Network

In this paper, an assessment on the health index (HI) of transformers is carried out based on Neural-Fuzzy (NF) method. In-service condition assessment data, such as dissolved gases, furans, AC breakdown voltage (ACBDV), moisture, acidity, dissipation factor (DF), color, interfacial tension (IFT), and age were fed as input parameters to the NF network. The NF network were trained individually based on two sets of data, known as in-service condition assessment and Monte Carlo Simulation (MCS) data. HI was also obtained from the scoring method for comparison with the NF method. It is found that the HI of transformers that was obtained by NF trained by MCS method is closer to scoring method than NF trained by in-service condition assessment method. Based on the total of 15 testing transformers, NF trained by MCS data method gives 10 transformers with the same assessments as scoring method as compared to eight transformers given by NF trained by in-service condition data method. Analysis based on all 73 transformers reveals that 62% of transformers have the same assessments between NF trained by MCS data and scoring methods.

Systematic approach for asset management of urban water pipeline infrastructure systems

PurposeThe purpose of this paper is to develop a systemic approach to evaluate physical condition of water pipeline infrastructure with limited condition assessment data that can help asset managers prioritize capital investments in maintenance projects for urban water pipeline systems.Design/methodology/approachSpatial pattern analyses are conducted in this research to find the spatial pattern of the service life of pipelines. Based on the spatial relationship, the critical areas where groups of pipelines with short service life are likely to be found were located using spatial statistical analyses. A visualized platform was also developed and used to validate the implementation of the proposed approach with the case study of urban water pipeline infrastructure in a city in the Midwest region of the USA.FindingsThe results of the spatial pattern analyses reveal that water pipelines are spatially clustered based on their service life. Further, it was found that on average the pipelines in the center of a city have longer service life while the average expected service life of the pipelines in the marginal areas is shorter. The interpolation method produced raster data with continuous information about the service years of pipelines that are useful for asset maintenance planning.Originality/valueWith the limited data, the proposed approach enables identification of the critical area of water pipelines with the likelihood of shorter service life. This result can be used as a priority rule for a rehabilitation plan and contributes to shifting from a responsive to a preventive approach in underground asset management.

An attempt to describe a relationship between concrete deterioration quantities and bridge deck condition assessment techniques

This paper presents a study of the performance of four techniques – visual inspection, Ground Penetrating Radar (GPR), Ultrasonic Surface Wave (USW), and core control – that were used to assess condition of a concrete bridge deck. The bridge deck was then rehabilitated using hydrodemolition, and the concrete removed during hydrodemolition was assumed to be deteriorated. LiDAR measurements of concrete depth removal collected after hydrodemolition were used as ground truth. Comparisons of bridge deck condition assessment data and LiDAR concrete removal measurements were performed in this study. The comparisons attempt to find and describe a possible relationship between bridge deck assessment techniques and quantities of concrete deterioration.

The Evaluation of Functional Performance of National Roadway using Three Types of Pavement Assessments Methods

The road section of Palur-Sragen-Mantingan categorized as a national road and serve 43.00km long arterial road, is the part of the middle lane that connects the important cities in Java Island. The damage that frequently occurs will certainly affect the safety and comfort of road users. Road surface condition assessment is one step to determine the type of program evaluation needs to be done. This study aims to determine the functional condition of the road by three methods and to assess the closeness of the relationship between these three methods. The descriptive study is carried out to conduct the comparability of the road condition assessment data resulted from three methods for analyzing the results of the assessment of road conditions between the years of 2011-2013. The assessment method of road conditions consists of the International Roughness Index, the Pavement Condition Index and the Surface Distress Index. The correlation between these methods were obtained from the Pearson correlation analysis based on CurveExpert program. The results showed that the linear regression analysis between the methods of IRI and PCI showed the value of PCI=0.966+10.919IRI, it means there is a positive correlation between the two methods and the r value is 0.976 indicates a very close correlation between the two methods. IRI and SDI method show the value of SDI=32.684+3.355IRI, it means a positive correlation between the two methods and the r value is 0.203 indicates a poor relation between the two methods. PCI and SDI method show the value of SDI=18.023+0.592PCI, it means there is a positive correlation between the two methods and the r value is 0.381 indicates a poor relation between the two methods. Limited from this research, the conclusion could be drawn that the different method of functional measurements could potentially result in the different pavement index, consequently the different decision for pavement and rehabilitation scheme.

Compensating the effect of temperature variation on dielectric response of oil-paper insulation used in power transformers

Being an offline measurement technique, Polarization Depolarization Current (PDC) data is conventionally measured after the oil-paper insulation of the equipment reaches thermal equilibrium. Depending on the condition of the insulation, PDC data measurement time typically ranges from several minutes to few hours. It is understood that atmospheric conditions controls the value of ambient temperature. During field measurement, these conditions may not remain constant throughout the PDC recording process. Variation in ambient temperature disturbs the thermal equilibrium that the equipment may have attained at the start of the measurement procedure. Such a condition invariably affects the recorded dielectric response data. However, methods that are available for analyzing relaxation current assume that temperature equilibrium is maintained throughout the measurement period. Results presented in this paper show that dielectric response data is indeed affected if the ambient temperature increases/decreases monotonically. A method is also proposed in this paper using which the effect of such temperature variation on the recorded relaxation current data can be eliminated. This also ensures correct interpretation of PDC data for condition assessment. Analysis presented shows that the value of the equilibrium temperature does not affect the performance of the proposed technique. The applicability of the proposed methodology is first tested on laboratory samples. Thereafter, it is applied to data collected from a real life power transformer.

Cache Management of Big Data in Equipment Condition Assessment

Big data platform for equipment condition assessment is built for comprehensive analysis. The platform has various application demands. According to its response time, its application can be divided into offline, interactive and real-time types. For real-time application, its data processing efficiency is important. In general, data cache is one of the most efficient ways to improve query time. However, big data caching is different from the traditional data caching. In the paper we propose a distributed cache management framework of big data for equipment condition assessment. It consists of three parts: cache structure, cache replacement algorithm and cache placement algorithm. Cache structure is the basis of the latter two algorithms. Based on the framework and algorithms, we make full use of the characteristics of just accessing some valuable data during a period of time, and put relevant data on the neighborhood nodes, which largely reduce network transmission cost. We also validate the performance of our proposed approaches through extensive experiments. It demonstrates that the proposed cache replacement algorithm and cache management framework has higher hit rate or lower query time than LRU algorithm and round-robin algorithm.

What's In The Numbers? A Review and Analysis of More Than 400 Miles of Force Main Inspection and Condition Assessment Data

What's In The Numbers? A Review and Analysis of More Than 400 Miles of Force Main Inspection and Condition Assessment Data

Possibilities to validate design models for corrosion in carbonated concrete using condition assessment data

This study analyses the actual observed corrosion damage and active corrosion in concrete facades in Nordic outdoor climate and compares this data with the DuraCrete design model for carbonation initiated corrosion propagation phase. Active corrosion was examined in this study by calculating backwards the age of the building and the initiation time by carbonation from a large condition assessment data from concrete facades and balconies using statistical simulation methods. The earliest visible corrosion damage on concrete facades was observed in condition assessments already after 8–15years from construction. In a large group of buildings the damage occurrence was found to reasonably well follow a normal distribution. The design model was found in the case of concrete facades and balconies in Finnish climate conditions to overestimate considerably the propagation phase predictions in all of the studied structure types compared to the statistical analysis. The overestimation in the model is due to the high influence of concrete resistivity and the definition of corrosion penetration needed for the initiation of a crack.

Pavement Condition Monitoring with Crowdsourced Connected Vehicle Data

The Michigan Department of Transportation (DOT) is interested in new technologies to improve transportation asset management (TAM) practices. A central component of TAM is pavement condition and performance assessment. To investigate methods to improve TAM data collection, the Michigan DOT and the Center for Automotive Research researched the potential for connected vehicle data to contribute to pavement condition and performance assessment. This effort included the review, critical analysis, and synthesis of several research reports and pilot projects to determine feasible methods of using connected vehicle data for pavement condition and performance assessment. The scope of connected vehicle data was defined to include data collected by sensors installed on consumer available vehicles and smartphones. The technology to allow for data capture from embedded vehicle systems and smartphones was established, but implementing such practices into TAM had not yet proved to be practical. One possibility for effective pavement data collection was to employ crowdsourcing, with the use of numerous probe vehicles. Crowdsourcing was not a common practice in pavement condition assessment. Using crowdsourced data in existing TAM programs may require innovative methods of data collection, processing, and management. This report concluded that by focusing on consumer available equipment and crowdsourced data, new connected vehicle data–based pavement condition metrics could be used for TAM in 3 to 5 years.

Alternate Methods to Obtain Facility Condition Assessment Data Using Non-Engineering Resources

Alternate Methods to Obtain Facility Condition Assessment Data Using Non-Engineering Resources

Development and application of On-line Monitoring Device of Transformer Vibration

A real time on-line transformer vibration monitoring system based on the Labview is proposed and applied in the monitoring of abnormal vibration of transformer caused by DC bias in this paper. The monitoring of the transformer body vibration and the signal online analysis can be well performed by this system. The vibration signals of the transformer body are detected by three acceleration sensors, and the signal features are studied in both the time domain and the frequency domain, which provides the data for the operation condition assessment and fault diagnostics. Combined with the background data base, a virtual instrument technology has been used to realize man machine interface and the real time multifunction and multi-channel monitoring. Compared with the previous similar installations, this monitoring system makes the best use of powerful computer, the flexibility of Labview in the instrument development, and the new data store technology. The experiment results suggest that the installation fully meets the requirements of real time monitoring. Some conclusions of transformer abnormal vibration under the condition of DC bias are gained. The system facilitates the detection of the early signs of transformer mechanical failures. DOI: http://dx.doi.org/10.11591/telkomnika.v11i8.3131

The Feature Analysis for Transformer Reliability Assessment

The power transformer is the most important equipment in the power system, so the condition-based maintenance of the transformer has an important significance for the safe and stable operation of the power grid, and condition assessment provides an important basis for the condition-based maintenance. In this paper, the calculation principle of the reliability assessment method is analyzed, and the contribution of each feature value is calculated. It has an important guiding significance for the Power Supply Bureau to provide the condition assessment data, the data with a contribution of large degree should be collected forcibly, so that the data for transformer reliability assessment is the most simplified, improving the assessment rate.

Advanced criticality assessment method for sewer pipeline assets

For effective management of water and wastewater infrastructure, the United States Environmental Protection Agency (US-EPA) has long emphasized the significant role of risk in prioritizing and optimizing asset management decisions. High risk assets are defined as assets with a high probability of failure (e.g. soon to fail, old, poor condition) and high consequences of failure (e.g. environmental impact, high expense, safety concerns, social disruption). In practice, the consequences of failure are often estimated by experts through a Delphi method. However, the estimation of the probability of failure has been challenging as it requires the thorough analysis of the historical condition assessment data, repair and replacement records, and other factors influencing the deterioration of the asset. The most common predictor in estimating the probability of failure is calendar age. However, a simple reliance on calendar age as a basis for estimating the asset's deterioration pattern completely ignores the different aging characteristics influenced by various operational and environmental conditions. This paper introduces a new approach of using 'real age' in estimating the probability of failure. Unlike the traditional calendar age method, the real age represents the adjusted age based on the unique operational and environmental conditions of the asset. Depending on the individual deterioration pattern, the real age could be higher or lower than its calendar age. Using the concept of real age, the probability of failure of an asset can be more accurately estimated.

Decision Model for Repair Prioritization of Reinforced-Concrete Structures

In general, the information obtained using a structural condition assessment is used for making decisions about repair needs, but the obtained information does not specify the repair urgency of deteriorating RC structures. Decisions about urgency of repair are usually made by professionals involved in structural condition assessment and hence are typically subjective. Moreover, comparisons between decisions made for several structures become difficult and inconsistent, primarily due to the lack of clearly defined relations between various condition assessment data and associated repair urgency. This paper proposes an innovative model to obtain repair urgency using the information obtained through various condition assessment techniques. The proposed model utilizes a consistent repair priority scale to categorize various types of condition assessment data. For defined categorizations, a normalized condition index function is assigned as a measure of relative importance of repair urgency. Aggregation rules are then discussed, and combined repair urgencies are subsequently computed to achieve rational prioritization. A detailed case study is presented to demonstrate the application of the proposed model.

Analysis of methods of evaluation of the accuracy of input data for air condition assessment

The article concerns the issues of reliability of input data for modeling of admixtures propagation in the air, and possible results of unreliable input data application at further optimization and control of the air quality. The main sources of unreliable data are given. There are objectives of the research in the sphere of input data limitation, review of approaches, decreasing its effect. The requirements to the methods of overcoming of input data limitation, as well as certain demands determined by Ukrainian realias, were formulated. The method based on the Bayes networks was suggested to assess the probabilistic properties of the modeling results unreliability during the application of unreliable input data. The article reveals the advantages of Bayes networks and the stages of their plotting. The structure of the network was suggested and its variables were described to assess the results of modeling. There is an approach to find out the values of conditional probabilities for the network

Evaluation of a strain monitoring system for existing steel railway bridges

The old steel bridges that integrate the existing railway lines are structures built with materials that are no longer used and whose knowledge has being lost over the years, often presenting severe problems of deterioration and subjected to loading environments very different from those for which they were designed. In this context, adequate strain monitoring is a crucial tool in supporting the behavior characterization and safety assessment of these structures.This article presents and discusses the monitoring systems installed in the Trezói Bridge, within a research project aimed at developing and applying procedures for evaluation of the structural integrity of steel railway bridges. The field observations of the structural behavior were accomplished by using two different types of sensors: electric and fiber optic strain sensors. The electric monitoring system was designed and installed on the bridge to supply the experimental data for the research project, while the fiber optic monitoring system was firstly applied to evaluate the reliability of the former and to check its efficiency, and secondly to provide some redundancy of the measurements at critical locations. The obtained results are analyzed to characterize the bridge behavior and the capabilities and limitations of both types of sensors to acquire the relevant data for the bridge service condition and fatigue assessment are discussed, namely in what concerns the ability to accurately capture the static and dynamic components of the structural response and the frequency content of interest.

Dynamic Deterioration Models for Sewer Pipe Network

One of the key components in successfully implementing asset-management programs is to have accurate and reliable deterioration models for the assets because the deterioration models are the core computational basis for predicting and prioritizing future maintenance, rehabilitation, or replacement activities of the assets. Many large and advanced utilities have put extensive efforts into collecting condition assessment data of their assets since the late 1990s. This change has posed new challenges in developing deterioration models. This paper presents a framework for developing dynamic deterioration models that can avoid the uniform treatment of the entire sewer pipe network by using the clustering and filtering process on the basis of location-related attributes and operational conditions. The dynamic deterioration models are dynamic because they are dual models for a single sewer network; one model is for individual prediction and the other for group prediction. The performance and benefits of the dyna...

Remote Sensing Laser and Imagery Data for Inventory and Condition Assessment of Road and Airport Infrastructure and GIS Visualization

Remote Sensing Laser and Imagery Data for Inventory and Condition Assessment of Road and Airport Infrastructure and GIS Visualization