Tool For Condition Assessment Research Articles

A Condition Assessment Tool for Steel Bridge Deck Pavement Systems Based on Data Balancing Methods and Machine Learning Algorithms

The primary challenge in the operation of steel deck pavement systems lies in the inspection and assessment of their condition. Traditionally, manual inspection methods are employed. However, these approaches are not only time-consuming and labor-intensive but also prone to human error. As a result, integrating data-driven machine learning technologies into the evaluation of pavement systems presents a significant advantage in addressing these issues. This study proposes a decision-making tool for estimating the condition levels of steel bridge deck pavement systems by employing classification techniques. To address the issue of class imbalance in the dataset, the SMOTE algorithm is utilized. Additionally, seven different machine learning methods—Light Gradient Boosting Machine, Extreme Gradient Boosting, Random Forest, Adaptive Boosting, K-Nearest Neighbor, Multilayer Perceptron, and Logistic Regression—are applied for training. Comparative analysis reveals that the Light Gradient Boosting performs optimally, achieving classification accuracies of 0.841 and 0.929 on the original and synthetic datasets, respectively.

Condition Assessment Tool for Reduced Strength of Eccentrically Loaded Columns with Damaged FRP Wraps

Condition Assessment Tool for Reduced Strength of Eccentrically Loaded Columns with Damaged FRP Wraps

A mechanistic deterioration point assignment model for water pipe condition assessment

ABSTRACT A pipe condition assessment model is required to implement effective and economical planned maintenance of the water distribution system. The application of such a model requires sufficient accuracy, which, however, is limited by the complexity of the pipe deterioration process and data storage capacity of the water utility. The majority of previous studies have focused on the improvement of assessment algorithms for data mining. In this study, a mechanistic deterioration point assignment (MDPA) model is developed to make advancements in the modes of data input and result output to enhance the model's accuracy and application scope for cast iron and steel pipes. In this MDPA model, (1) indicators/sub-indicators on external corrosion, external load, internal corrosion, and internal load are constructed and can be obtained by data estimation or techniques and (2) assessment results include both pipe overall condition and detailed conditions on corrosion and load, offering evidence for primary maintenance measures. The weights of the indicators/sub-indicators are estimated using the Bayesian statistics theory. The modelling results of pipe samples demonstrate that this MDPA model is an effective tool for pipe condition assessment.

Visual inspection results and evaluation of Akhtala tailing safety based on tailing management facility safety methodology

Relevance. Mining sector is one of the leading branches of the industry in Armenia. Therefore, effective government regulation of the sector based on a sound legal basis is a necessity. Accidents at tailing management facilities are posing a high risk to people and environment especially in their near vicinity and often have a transboundary impact. The tailing management facility safety methodology elaborated under the auspices of the German Environmental Agency is an efficient tool for quick assessment of the technical condition of the tailing management facilities. Aim. To present the results of application of the tailing management facility methodology in Armenia, which was applied to the operated Nahatak tailing management facility of the Akhtala Ore Dressing Combine (Lori region, Armenia). Objects. Tailing management facility methodology was applied to the operated tailing management facility Nahatak of the Akhtala Ore Dressing Combine, for which all documentation was checked. Enrichment wastes were stored at the tailing management facility during processing copper ore from the Shamlug mine and polymetals from the Akhtala mine. The main visual inspection was carried out in three parts of the tailing management facility: main dam, drainage canal and secondary dam. Special attention was drawn to main dam conditions. The video recorded by a drone was used to explore the dam bottom-line. Methods. The tailing management facility methodology includes the following elements: the method of evaluation of Tailings Hazard Index; the tailing management facility checklists including the questionnaire for visual and documentation inspection, the evaluation matrix for the tailing management facility safety level; the measures catalogue for taking actions to improve tailing management facility safety. The questions of the each tailing management facility checklist are stated in a way to encompass the minimum set of the requirements, critical for tailing management facility safety, which allows evaluating the tailing management facility conditions. Questions in all groups of the checklist are sorted by the tailing management facility lifecycle and each subsection contains relevant questions applied to a specific stage (design, construction, operation, re-cultivation, closure). Application of the tailing management facility checklist is supported by a measures catalogue with short-, medium- and long-term safety measures. Results. The tailing management facility methodology was used/tested for the operated tailing management facility in Armenia for the first time. Тhe main problems of the Nahatak tailing management facility were identified and analyzed, and appropriate recommendations were provided based on them. Based on the obtained results it can be concluded that the main problems are related to the drainage system and the management of water flows, some parts of the primary dam that require restoration, as well as the improvement of monitoring systems. At the same time the experience of application of the tailing management facility methodology in Armenia, allowed improving this methodology and making it the most effective working tool for quick and efficient visual inspection of tailing management facilities and for thorough inspection of relevant documentations. The user-friendly tailing management facility safety methodology (with questionnaires of visual and documentation checking) is an efficient tool for quick assessment of the technical condition of the tailing management facilities, as well as for training staff both in the governance bodies and in mining enterprises.

Development of Rating System of Existing Railway Steel Bridges

Abstract: Railway bridges are designed to be serviceable for a long time. However, the structural conditions of railway bridges change over time due to environmental effects, fatigue and structurally unplanned modifications etc. which affect health of the structure significantly. Hence inspection and condition assessment of structure is required for their uninterrupted operation. Condition assessment is also required for maintenance and repairs of existing structures so as to avoid any mishaps and save valuable human life. Dealing with thousands of bridges and several factors that cause deterioration, makes the rating process extremely complicated. It is therefore necessary to develop a practical and accurate system, which will be capable of rating a network of railway bridges. Several bridge inspectionstandards and condition assessment practices have been developed around the globe. Some practices employ four linguistic expressions to rate bridge elements while other practices use five or six, or adopt numerical ratings such as 1 to 9. This research article introduces six colour codes in the proposed rating system for a network of railway bridges based on their current structural condition by means of visual inspection and different non-destructive evaluation techniques. The proposed rating system could provide bridge stakeholders with a rational appraising tool for condition assessment of railway bridges, allowing better allocation of budget and more precise maintenance decisions.

Damage assessment automation for single storey detached masonry houses: a probabilistic approach

ABSTRACT Assessing the existing condition of aging masonry houses are of high interest as the cost of retrofitting and repairing becomes significantly higher. Conventional condition assessment tools and methods for single storey detached masonry houses (SSDMH) are time-consuming, subjective, tedious, and sparse. This study aims to formulate a novel framework for assessing the condition of those houses by proposing a user-friendly, effective, and impartial model, for existing structures considering cracks in the masonry walls and the age of the house. This study adopted the bayesian belief network (BBN) method since the existing data on building assessment are subjective and consider multiple parameters. The application of the proposed model was formulated using wall cracks observed in a sample of thirty SSDMH. The Expectation Maximization (EM) algorithm was used to compute the conditional probabilities from the data set. The model was tested on ten houses for which the results were positive and validated with the Receiver Operating Characteristic (ROC) curve. However, the scope of the model is limited to SSDMH. Further development of this model may benefit the Surveyors, Engineers, and Architects to make informed decisions quickly by placing the structure at the correct severity level to decide on the renovation strategies.

Comparison of Rating Systems for Alberta Rock Slopes, and Assessment of Applicability for Geotechnical Asset Management

In 1999, Alberta Transportation and Economic Corridors (TEC) implemented the Geohazard Risk Management Program (GRMP) to identify, assess, monitor, and prioritize the mitigation of risk resulting from geohazard events at specific sites along the provincial highway network. The GRMP was developed to address a variety of geohazard types including rockfall hazards that occur at natural and constructed (cut) highway backslopes. An evaluation of various methods for the condition assessment of rockfall geohazards, including TEC’s current GRMP risk rating system, has been completed with the intent of better understanding the suitability of each method as TEC transitions to a formalized GAM program. The GRMP risk rating values for selected rockfall geohazard sites along highway corridors in Alberta were compared to values developed from the results of five established rock mass and rock slope rating systems. The results of this study demonstrate that TEC’s current GRMP risk rating system is a viable tool for the condition assessment and performance monitoring of rockfall geohazards, which could be utilized within a formalized GAM program, further benefitting from years of recorded application in Alberta. Of the other rating systems tested, the rockfall hazard rating system (RHRS) showed a strong correlation with the GRMP risk rating while Q-Slope, the Geological Strength Index (GSI) and Rock Mass Rating (RMR) correlation were marginal but displayed a potential for use as condition assessment tools. The work presented in this paper provides the first evaluation of rock slope rating systems for rockfall hazards along corridors in Alberta, directly comparing them to the slope performance as observed by TEC in a quantitative manner.

A Simplified Facility Management Tool for Condition Assessment through Economic Evaluation and Data Centralization: Branch to Core

The field of facility management, especially concerning condition assessment, is affected by two main issues: one is the incompleteness and heterogeneity of information transfer between the involved subjects; the other is the frequent lack of specific advanced skills needed for technically complex tools. The immediate consequences of this process inefficiency fall on economic and environmental aspects: the unavailability or incorrect structuring of data related to building conditions does not allow for making optimal choices concerning interventions on components. This paper attempts to provide a solution in this framework by presenting a methodology for simplified condition assessment, in which the evaluation of decay parameters draws from economic evaluation techniques, and which optimizes data collection, systematization, and elaboration, also integrating it with a mobile app for automatic data upload and centralization. The research underlying its development draws from decay evaluation criteria and national standards for the analysis and breakdown of buildings. The methodology was tested on a case study of the Cloister of Santa Croce in Florence, which also served as the client of the tool. The proposed methodology stands as an easily implementable integration to condition assessment for maintenance planning and building inspection activities.

Psychological Care as an Interdisciplinary Approach in the Management of Diabetes: A Narrative Review of Literature

Objective: Diabetes, a serious chronic disease, demands a lot of patients in terms of daily self-care and often complicated treatment. Psychological problems are prevalent among people with diabetes. Burden with daily restrictions to diet and activity, risks of the treatment, fear of losing metabolic control and disease progression may furthermore reduce quality of life and aggravate psychological condition. This paper provides a current overview of prevalence, screening and general psychological managements in diabetes according to the worldwide recommendations. In addiction it updates the medical literature refer to Poland. Materials and methods: The search was conducted in Google Scholar, PubMed, Scopus, Web of Science, and ScienceDirect. The final set of 75 articles, include 33 papers that refer to psychological condition assessment tools. Results: The analysis for this review has been packaged into themes in order to generate a very useful and practical tool for all health professionals. It summarized in systematic and comprehensive way all psychological states that can appear in patients with diabetes. The most prevalent and important problems are: diabetes distress, depression, anxiety disorders, disordered eating behavior, and cognitive impairment/dementia. Conclusions: All worldwide guidelines strongly recommend psychological and social care as integrated part of the management of the diabetes. Healthy psychological state with proper screening, monitoring and the management could help in reaching therapeutic success and better quality of life.

Real-time cutting tool condition assessment and stochastic tool life predictive models for tool reliability estimation by in-process cutting tool vibration monitoring

Real-time cutting tool condition assessment and stochastic tool life predictive models for tool reliability estimation by in-process cutting tool vibration monitoring

Pavement Crack Detection and Clustering via Region-Growing Algorithm from 3D MLS Point Clouds

Road condition monitoring plays a critical role in transportation infrastructure maintenance and traffic safety assurance. This research introduces a methodology to detect cracks on pavement point clouds acquired with Mobile Laser Scanning systems, which offer more versatility and comprehensive information about the road environment than other specific surveying systems (i.e., profilometers, 3D cameras). The methodology comprises the following steps: (1) Road segmentation; (2) the detection of candidate crack points in individual scanning lines of the point cloud, based on point elevation; (3) crack point clustering via a region-growing algorithm; and (4) crack geometrical attributes extraction. Both the profile evaluation and the region-growing clustering algorithms have been developed from scratch to detect cracks directly from 3D point clouds instead of using raster data or Geo-Referenced Feature images, offering a quick and effective pre-rating tool for pavement condition assessment. Crack detection is validated with data from damaged roads in Portugal.

User Experience during an Immersive Virtual Reality-Based Cognitive Task: A Comparison between Estonian and Italian Older Adults with MCI.

Mild cognitive impairment (MCI) is an early stage of cognitive abilities loss and puts older adults at higher risk of developing dementia. Virtual reality (VR) could represent a tool for the early assessment of this pathological condition and for administering cognitive training. This work presents a study evaluating the acceptance and the user experience of an immersive VR application representing a supermarket. As the same application had already been assessed in Italy, we aimed to perform the same study in Estonia in order to compare the outcomes in the two populations. Fifteen older adults with MCI were enrolled in one Rehabilitation Center of Estonia and tried the supermarket once. Afterwards, they were administered questionnaires aimed at evaluating their technology acceptance, sense of presence, and cybersickness. Estonian participants reported low side effects and discrete enjoyment, and a sense of presence. Nonetheless, their intention to use the technology decreased after the experience. The comparison between Italian and Estonian older adults showed that cybersickness was comparable, but technology acceptance and sense of presence were significantly lower in the Estonian group. Thus, we argue that: (i) cultural and social backgrounds influence technology acceptance; (ii) technology acceptance was rather mediated by the absence of positive feelings rather than cybersickness.

Transfer learning of Impact Echo signal classification from laboratory to the field

Impact Echo (IE) is a popular nondestructive testing tool for condition assessment of concrete bridge decks. Traditionally, IE signals require to be manually interpreted and classified into discrete classes – good, fair and poor - representing the different conditions at test points. This study aims to develop an automated fuzzy classification approach for IE signals trained on a set of eight reinforced concrete specimens constructed at the FHWA Advanced Sensing Technology (FAST) NDE laboratory with artificial defects that are known apriori. We choose fuzzy classification to obtain condition “goodness” represented on a continuum rather than on discrete classes (good, fair and poor). We use fuzzy classification models informed by physics-based features and automatically extracted features to determine the departure of a bridge deck condition from ‘good’. Next, using transfer learning, we suitably tune the classification model for optimum performance on data from FHWA’s InfoBridge database that has a limited amount of IE data from real bridge decks. We evaluate the transferred model performance against expert-assisted manual signal classification. This study will demonstrate the utility of laboratory studies for developing models applicable to real field data, where little or no ground truth data are available.

EASIX, Modified EASIX and Simplified EASIX as an Early Predictor for Intensive Care Unit Admission and Mortality in Severe COVID-19 Patients.

COVID-19 receives a lot of attention due to its threat to global public health. Research is ongoing to find universal methods to assess the baseline health status of a patient to determine prognosis and management strategies. This study aims to assess the predictive potential of the EASIX (Endothelial Activation and Stress Index) and two of its modifications (mEASIX and sEASIX) in terms of the need for admission to the ICU (intensive care unit), the use of IMV (invasive mechanical ventilation) and death due to COVID-19. The medical data of 370 severely ill patients hospitalised in the COVID-19 departments of the Regional Specialist Hospital in Wroclaw (Poland), including the ICU, were analysed retrospectively. The mortality rate in the group studied was 65.7% (243 cases). In the case of all three indices, EASIX, mEASIX and sEASIX, there was a statistically significant correlation between the need for admission to the ICU (p = 0.026, p = 0.019, p = 0.001, respectively) and the risk of death (p < 0.001). In terms of the risk of death, the high values of the assessed indices (EASIX ≥ 2.36, mEASIX ≥ 704.03, sEASIX ≥ 3.81) were characterised by low sensitivity (≤40%), high specificity (approximately 90%) and low NPV (negative predictive value) (approximately 40%) with high PPV (positive predictive value) (approximately 80%). Due to the ease of implementation and the low cost of performing basic laboratory tests, the above-mentioned indices can be used as an additional, but not universal tool for the initial assessment of the health condition of patients admitted to the hospital.

Space images as one of the tools for the rangelands condition assessment in Kazakhstan

Relevance. Kazakhstan possesses enormous pasture resources, which in turn requires proper management and regulation. Drought, climate change, plant degradation and declining grazing resources have led to a shortage of feed , which led to massive livestock deaths in 2021. Lack of up-to-date information on the condition of rangelands is the main problem for Kazakhstan. The use of Earth remote sensing data makes it possible to obtain important information about the dynamic and spatial characteristics of pasture vegetation. Methodological substantiation of remote sensing data for the assessment of pasture lands formed the basis of the conducted research. Detailed and regularly updated results of assessing the rangelands condition based on space monitoring data will increase the efficiency of using the country’s pasture resources.Materials and methods. For remote assessment of pasture lands the following methods were used: cartographic, geographical, mathematical, methods of spatial analysis of remote sensing data and geoinformation design. Optical images from KazEOSat-2 and Sentinel-2 satellites for 2021, the results of a field survey, online platforms Land Viewer from EOS and Earth Explorer from USGS were used to mapping the main indicators of pasture lands of the Pavlodar region of the Republic of Kazakhstan as a remote sensing data.Results. This article presents the results of using space monitoring methods to assess the condition and productivity of rangelands on the example of one region of Kazakhstan. Interpretation methods of remote sensing data using field data decryption and verification of data, initial cartographic data and geobotanical analysis of reference territories have been substantiated. The main stages of data processing are presented. Based on the results of the work, schematic maps of the main indicators of the pastures of the studied territory were developed according to remote sensing data, information was formed on the parameters of the productivity of the pastures of the studied territory.

Кросс-культурная адаптация и валидация опросника SPOT-25 для русскоязычной популяции

Life quality assessment is one of the most popular and relevant field in modern medicine. Hearing loss due to otosclerosis may significantly affect different areas of the patient’s life, thereby changing the level of quality of life. At the same time, there is a certain subjectivity in the assessment of the patient’s own condition and the expert opinion of the doctor, including due to the discrepancy between the concept of a satisfactory level of quality of life. One of the problems of life quality assessment of patients with otosclerosis among Russian-speaking population is the lack of validated nosologically specific questionnaires. The aim of our study was to assess the quality of life of patients with otosclerosis by means of cross-cultural adaptation and validation of the Russified version of the SPOT-25 questionnaire (Stapesplasty Outcome Test 25) to assess the quality of life of patients with otosclerosis. During the study, the SPOT-25 questionnaire was translated using a double reverse independent translation, agreed and formed into a single version for further validation work. As part of the study, various observation groups were formed, which included patients suffering from otosclerosis and healthy volunteers. The main internal characteristics of the questionnaire were determined, such as validity, reliability, and sensitivity. The results obtained demonstrated sufficient reliability and sensitivity as well as clinical validity of the new condition assessment tool. Thus, the Russified version of the SPOT-25 questionnaire can be used both in further scientific research and in the daily practice of an otorhinolaryngologist.

Integrating data, expert opinion and fuzzy logic in the development of an index of wetland condition

Wetlands face an intensifying level of degradation, and management to protect their extent and character is paramount. To support wetland management in south-east Australia, we developed a wetland condition assessment tool for palustrine and lacustrine wetlands. Through extensive consultation with end users during its development, the tool, the Index of Wetland Condition (IWC), considers user needs and skills, as well as attempts to assess the complex nature of wetland systems and their inherent variability, both spatially and temporally. The IWC is structured as a hierarchical index with 13 indicators nested under six characteristics (subindices) that influence wetland function: wetland catchment, physical form, wetland soils, water properties, hydrology and biota. The contribution of each to the overall index (scored along a condition gradient) was estimated from quantitative biological and physicochemical data from 24 wetlands using a fuzzy cognitive map approach. The IWC development framework will be particularly useful in jurisdictions globally where reference condition information is limited or lacking for indicators, or where there are substantial practical constraints that limit the selection of indicators, such as management staff capability or capacity. Uptake of the IWC with wetland practitioners in south-east Australia has been substantial.

Practical experience condition diagnosis gained from oil-immersed power transformers voltage class 10 kV

Dissolved Gas Analysis (DGA) for oil samples has been the most widely used diagnosis tool for transformer condition assessment for many years. However, DGA use to oil-filled transformers with a voltage class up to 100 kV. The aim of this paper is to address the issue of DGA interpretation to oil-filled transformers with a voltage class of 10 kV. This paper will present DGA tests results from 57 power transformers and will propose a maintenance decision making procedure using the IEC 60599-2015 Ratio Method, IEEE Std C57.104-2008 include Dornenberg Ratio Method and Rogers Ratio Method, and Russian Std CTO 56947007-29.180.010.094-2011 and Russian Std RD 153-34.0-46.302-00.

A Machine-Learning Approach to Identify the Influence of Temperature on FRA Measurements

Frequency response analysis (FRA) is a powerful and widely used tool for condition assessment in power transformers. However, interpretation schemes are still challenging. Studies show that FRA data can be influenced by parameters other than winding deformation, including temperature. In this study, a machine-learning approach with temperature as an input attribute was used to objectively identify faults in FRA traces. To the best knowledge of the authors, this has not been reported in the literature. A single-phase transformer model was specifically designed and fabricated for use as a test object for the study. The model is unique in that it allows the non-destructive interchange of healthy and distorted winding sections and, hence, reproducible and repeatable FRA measurements. FRA measurements taken at temperatures ranging from −40 °C to 40 °C were used first to describe the impact of temperature on FRA traces and then to test the ability of the machine learning algorithms to discriminate between fault conditions and temperature variation. The results show that when temperature is not considered in the training dataset, the algorithm may misclassify healthy measurements, taken at different temperatures, as mechanical or electrical faults. However, once the influence of temperature was considered in the training set, the performance of the classifier as studied was restored. The results indicate the feasibility of using the proposed approach to prevent misclassification based on temperature changes.

The Proposal of a New Tool for Condition Assessment of Medium Voltage Power Cable Lines

A medium voltage (MV) cable network is a substantial component of the distribution network. Present management of this grid segment is mainly based on the failure rate analysis, i.e., a rise in the number and kind of faults on the actual line means that its technical condition is getting worse. The efficiency of the power system is low and additional costs of repair works, supply interruption, difficulties in the investment planning and operation and maintenance works are necessary. The aim of the R&amp;D works done in the realised project is to implement the management of the MV cable network based on the estimated condition of the individual cable line, obtained from diagnostic measurements. The diagnostic investigations of the cable lines are the reference. Many years of research work have led to the development of the Health Index based on diagnostic, technical and service data.