Methodology For Condition Assessment Research Articles

Environmental effects on the experimental modal parameters of masonry buildings: experiences from the Italian Seismic Observatory of Structures (OSS) network

AbstractThe paper presents an in-depth analysis of the ambient dynamic behavior of nine masonry buildings monitored by the Italian Seismic Observatory of Structures (OSS). Addressing a significant knowledge gap affecting this structural type, the study reveals how daily and seasonal fluctuations in environmental factors have a notable influence on its experimental modal parameters. A robust frequency-domain tracking algorithm is first developed to identify and follow the evolution of modal parameters over time, exploiting ambient vibration recordings acquired at sub-daily intervals on the structures. The procedure is systematically applied to the entire portfolio of case-study buildings and, in the first year of training, integrated with measurements of environmental parameters provided by nearby weather stations. The multivariate regression analysis indicates that temperature variation is the primary driver of the observed wandering of natural frequencies. The frequency–temperature relationship shows a positive correlation above zero degrees and, in several cases, a significant degree of nonlinearity already present in low-frequency global modes. Simple predictive models are proposed to address such nonlinear behavior, including freezing conditions and accounting for internal heating during winter. Leveraging these novel insights, the work develops strategies to improve the efficiency of data acquisition protocols and training periods, enabling the near-future extension of real-time condition assessment methodologies to the entire OSS network.

Hybrid CNN-LSTM model driven image segmentation and roughness prediction for tool condition assessment with heterogeneous data

Worn tools might lead to substantial detrimental implications on the surface integrity of workpieces for precision/ultra-precision machining. Most previous research has heavily relied on singular information, which might not be appropriate enough to ascertain tool conditions and guarantee the accuracy of workpieces. This paper proposes a CNN-LSTM hybrid model directly utilizing tool images to predict surface roughness on machined parts for tool condition assessment. This work first performs pruning based on UNet3+ architecture to eliminate redundant structures while integrating attention mechanisms to enhance the model's focus on the target region. On this basis, tool wear region information is intensely mined and heterogeneous data is optimized using Spearman correlation analysis. Subsequently, we innovatively proposed a hybrid model that integrates CNN and RNN, endowing the model with the ability to process spatial and sequential information. The effectiveness of the proposed methodology is validated using the practical data obtained from cutting experiments. The results indicate that the proposed tool condition assessment methodology significantly improves the segmentation accuracy of the tool wear region to 94.52 % (Dice coefficient) and predicts the surface roughness of machined parts with an accuracy exceeding 93.1 % (R2). It can be observed that the developed methodology may provide an effective solution for accurate tool condition assessment and the implementation of tool health management.

Research on safety condition assessment methodology for single tower steel box girder suspension bridges over the sea based on improved AHP-fuzzy comprehensive evaluation

In order to propose a reliable method for assessing the safety condition for single-tower steel box girder Suspension bridges over the sea, a condition monitoring system is established by installing sensors on the bridge structure. The system is capable of gathering monitoring data that influence the safety status of the bridge. These include cable tension, load on the main tower and pylon, bearing displacement, wind direction, wind speed, and ambient temperature and humidity. Furthermore, an improved Analytic Hierarchy Process (AHP) algorithm is developed by integrating a hybrid triangular fuzzy number logic structure. This improvement, coupled with comprehensive fuzzy evaluation methods, improves the consistency, weight determination, and security evaluation capabilities of the AHP algorithm. Finally, taking the No.2 Channel Bridge as an example and based on the data collected by the health monitoring system, the application of the safety assessment method proposed in this paper provides favorable results in evaluating the overall safety status of the bridge in practical engineering applications. This provides a basis for management decisions by bridge maintenance departments. This project confirms that the research results can provide a reliable method for assessing the security status of relevant areas.

Life cycle costs and asset management for protective structures against natural hazards

AbstractWith increasing risks from natural hazards in alpine regions in Austria OEBB‐Infra and Hoffmann‐Consult have developed a consistent methodology for condition assessment, prediction, and life cycle optimization of protective structures. Based on a thorough assessment of the asset stock standardized life cycles for key structures like rockfall and avalanche protection structures have been derived. The developed life cycle models allow a generalized assessment of asset condition, residual service life, and remaining asset value. In contrast to typical transport assets, protective structures are both subject to natural aging processes and random events with a certain occurrence rate. Beyond standardized life cycles on network level an adjustment to project level is necessary to account for actual condition development and asset specific events. The paper provides an overview on the developed generic life cycle with application to rockfall protection structures with prices 2022 in Austria.

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.

Data-Driven Approach toward Long-Term Equipment Condition Assessment in Sterile Drug Product Manufacturing.

A two-stage data-driven methodology for long-term equipment condition assessment in drug product manufacturing is presented with a case study for a commercially operating aseptic filling line. The methodology leverages process monitoring data. Sensor measurements are partitioned using process information and maintenance schedules that are available on different databases. Data is processed to tackle heterogeneity in sources and formats. The data is cleaned to remove the effects of short-term variabilities and to enhance underlying long-term trends. Two approaches are presented for data analysis: first, anomaly detection using independent component analysis (ICA), where clusters of outliers are identified. The frequency and timing of such outliers yield important insights regarding maintenance schedules and actions. The second approach enables condition monitoring using principal component analysis (PCA). Long-term operational baselines are identified and shifts therein are linked with different process and equipment faults. This approach highlights the impact of equipment deterioration on shifting operational data baselines and shows the potential for the combined application of ICA and PCA for equipment condition monitoring. It can be applied within predictive maintenance applications where the installation of new specialized sensors is difficult, like in the pharmaceutical industry.

Multi-stage deep learning networks for automated assessment of electricity transmission infrastructure using fly-by images

The modern electric power transmission system is a geographically extensive network which can span hundreds of kilometres, crossing harsh terrain and making manual inspection of its components costly. This paper proposes a novel condition assessment methodology for transmission overhead lines that is significantly more cost effective compared to traditional foot-patrol visual inspections. The proposed methodology utilizes a multi-stage ensemble deep learning network to automatically classify tower conditions based on high resolution aerial images. While aerial inspections allow relatively quick inspection of transmission routes, they are not usually used for condition assessment due to the high capturing altitude of images which would require time consuming manual processing to identify defects on hundreds to thousands of images. The proposed methodology automatically isolates transmission poles, disaggregates components, detects defects and determines the health index of concrete structures and insulators. The method involves pushing images through layers of automatic detection, region of interest (RoI) extraction and patching. A number of recent object detectors were tested on real-world data to evaluate their performance and an ensemble model is composed to improve the reliability of the detection. Results indicate that the multi-layer approach with output-based ensemble modelling, can effectively detect critical defects, although incipient fault conditions remain uncertain.

PROPOZYCJA MONITORINGU GATUNKU NEPTIS RIVULARIS (SCOPOLI, 1763) W POLSCE

Hungarian Glider (Neptis rivularis), a species of butterfly of the family Nymphalidae, which in Poland is under partial species protection and is included in the Red list of threatened animals in Poland with the EN category. The occurrence and abundance of Neptis rivularis are strictly dependent on the presence of Spiraea salicifolia L., which is the host plant of the species. Limiting the range of the occurrence of the Neptis rivularis in Poland also results from the decline in the number of suitable habitats. This article presents a proposed methodology for monitoring based on the evaluation of indicators of population condition in the form of: number of adults, abundance index, number of caterpillars and population isolation, and indicators of habitat condition – presence of the Spiraea salicifolia and exposure to the sun of microhabitats. The concept of the population condition assessment methodology was based on non-invasive observation of adults and counting caterpillars. The concept of habitat condition assessment methodology is based on a strong relationship between the Neptis rivularis and the Spiraea salicifolia. It is proposed to annually monitor the Neptis rivularis population in at least a dozen research areas within the country. This proposed monitoring methodology requires pilot studies to be carried out within Neptis rivularis occurrence sites in order to assess the effectiveness of the presented assumptions of the monitoring methodology for this species.

Vehicle Bump Testing Parameters Influencing Modal Identification of Long-Span Segmental Prestressed Concrete Bridges.

In-service prestressed concrete box girder bridges have received increasing attention in recent years due to a large number of bridges reaching decades in service. Therefore, the ageing of infrastructure demands the development of robust condition assessment methodologies based on affordable technology such as vehicle-induced vibration tests (VITs) in contrast with more expensive existing technologies such as tests using hammers or shakers. Ambient vibration tests (AVTs) have been widely used worldwide, taking advantage of freely available ambient excitation sources. However, the literature has commonly reported insufficient input energy to excite the structure to obtain satisfactory modal identification results, especially in long-span concrete bridges. On the other hand, the use of forced vibration tests (FVTs) requires more economic resources. This paper presents the results of field measurements at optimally selected locations in VITs consisting of a 32-ton truck and a springboard with a height of 50 mm. AVTs using optimal sensor placement (OSP) provide similar results to VITs without considering OSP locations. Additionally, the VIT/AVT cost ratio is reduced to 2 since a shorter data collection time is achieved within a one-day (8 h) test framework, which minimizes temperature effects, thus leading to improvements in AVT identification results, especially in vertical modes.

Damage Types and Deterioration Characteristics of Check Dams Built on Mountain Streams in Southeast Korea

Check dams are one of representative structural measures to reduce sediment disaster at a forest watershed. Therefore, it is essential to assess the structural vulnerability and functionality of check dams in the management of sediment disaster risk at watersheds. This study examined the main types of damages and deterioration characteristics in concrete and stone check dams, which comprise more than 80.0% of Korea’s check dam stock. This was based on the 4-step condition assessment, which is the modified version of condition assessment methodology for embankment dams to meet the characteristics of check dams. As a result, the damages and deterioration of concrete check dams were classified into ten representative types. And representative damage types of stone check dams were classified into nine types. The condition assessment results demonstrated that the spillway part frequently contacted with water and debris flows was deteriorated approximately 1.2 times faster than the wing parts for both concrete and stone check dams, and the maintenance demands of both types of check dams increased 15–20 years after construction. Thus, these deterioration characteristics of concrete and stone check dams should be considered in decision-making to determine maintenance priorities among the check dams stock. Furturemore, we analyzed the relationship between condition index and service time in the examined concrete and stone check dams. The degree of deterioration in concrete and stone check dams increased significantly as the service time increased. Based on the relationship between condition index and service time, we classified deterioration characteristics of check dams into two groups: 1) those that follow the average deterioration trends of their facility group (Group II) and 2) those that undergo an unexpected end-of-service life because of exceptional incidents or severe defects (Group I). We hope that our work will form a useful resource for engineers and decision-makers involved in planning, designing, constructing, and maintaining check dams.

An Unsupervised Approach to Partial Discharge Monitoring in Rotating Machines: Detection to Diagnosis With Reduced Need of Expert Support

Condition monitoring and maintenance in rotating machine are generally managed through expert evaluation of the diagnostic properties and relevant harmfulness of the underlying aging mechanisms. In order to prompt broad diffusion of condition monitoring systems, especially in large assets and in the rising field of electrification transport, however, ways to get rid of the time-consuming and expensive support of experts must be prompted. A straightforward solution is transiting towards automatic and unsupervised diagnostics and condition assessment methodologies. This paper proposes and applies algorithms for the automatic detection of partial discharges, which is the property most often associated to the fastest accelerated aging mechanisms in electrical insulation, including noise rejection, identification of the type of partial discharge sources and estimation of an health condition index in rotating machines fed by AC sinusoidal voltage. The goal is to obtain a self-assessment of the health condition by each rotating machine of an asset, which can thus interact with the asset or maintenance manager when needed, that is, when reliability is at risk.

A Simplified Methodology for Condition Assessment of Bridge Bearings Using Vibration Based Structural Health Monitoring Techniques

The mechanical properties of bridge bearings gradually deteriorate over time resulting from daily traffic loading and harsh environmental conditions. However, structural health monitoring of in-service bridge bearings is rather challenging. This study presents a bridge bearing condition assessment framework which integrates the vibration data from a non-contact interferometric radar (i.e. IBIS-S) and a simplified analytical model. Using two existing concrete bridges in Australia as a case study, it demonstrates that the developed framework has the capability of detecting the structural condition of the bridge bearings in real-time. In addition, the results from a series of parametric studies show that the effectiveness of the developed framework is largely determined by the stiffness ratio between bridge bearing and girder ([Formula: see text], i.e. the structural condition of the bearings can only be effectively captured when the value of [Formula: see text] ranges from 1/100 and 100.

PROPOZYCJA METODYKI MONITORINGU ŻAGNICY ZIELONEJ Aeshna viridis Eversmann, 1836 (Odonata: Aeshnidae)

Aeshna viridis, a species of dragonfly of the family Aeshnidae, is listed in Appendix II of the Bern Convention as well as Annex IV of the Habitats Directive. The decline in the range and abundance of A. viridis is associated with a strong dependence of this species on the presence of Stratiotes aloides in the water body and results from a decrease in the number of suitable habitats. So far, attempts to develop a monitoring methodology for this species have been made in several European countries, including Sweden, Denmark and the Netherlands. This article presents a proposal for a monitoring methodology based on the evaluation of indicators of population condition in the form of exuviae density and number of adults, and indicators of habitat condition: the area of the water body covered by S. aloides, the presence of dense and undivided patches of S. aloides, succession in the water body and anthropopressure. The concept of population condition assessment methodology is based on observation of adult specimens and collecting exuviae, avoiding larvae sampling, which is invasive and associated with technical difficulties. The concept of the habitat condition assessment methodology, in turn, is based on strong association between A. viridis and S. aloides. It is proposed to monitoring A. viridis population on a minimum of several research areas within the country, on a two-year cycle. The presented proposal of the monitoring methodology requires pilot studies to be carried out within the A. viridis localities in order to determine the validity of assumptions made in the monitoring methodology.

Toward automatic condition assessment of high-voltage transmission infrastructure using deep learning techniques

Electrical Transmission System Operators (TSO) are trusted with ensuring the safety and reliability of transmission infrastructure which can span thousands of kilometers. Maintenance of such a geographically expansive system is naturally a matter of concern and companies invest heavily in tracking infrastructure state which still relies predominantly on visual inspection. This paper presents an automated condition assessment methodology for concrete poles supporting overhead conductors based on deep learning object detection networks. Nine defect conditions ranging from incipient to severe are automatically detected from infrastructure photographs and mapped onto established Health Indices used by maintenance personnel. Three different deep learning networks are tested and new metrics, specific to this problem, are defined to evaluate their performance based on asset Health Index (HI) values. Results indicate that deep learning object detection networks hold promise for significantly reducing manual labour associated with visual inspection, especially when combining with automatic asset identification based on image geotag. This paper shows acceptable performance on more severe defect types.

Knowledge-Based Fuzzy Expert System to the Condition Assessment of Historic Road Bridges

A systematic methodology for condition assessment of the historic road bridges was needed because of the poor and inadequate condition of bridges which cannot satisfy everyday-day dynamic loads and deteriorations due to the aging process. Thus in this study, a new expert system based on the knowledge approach has been proposed to develop a systematic procedure for condition assessment of these bridges using fuzzy logic and sets of α-cuts. Each bridge is divided into three components: superstructure, substructure, and equipment, and each component is divided into relevant elements. These elements are evaluated by an expert and their ratings are fuzzified according to defined fuzzy sets, their membership functions, and linguistic values. Furthermore, fuzzy structural importance is given to ratings of each element. Combinations of these two values are calculated to obtain a fuzzy rating of the component using the Fuzzy Weighted Geometric Mean (FWGM). Finally, for the defuzzification of the component rating, the centroid method is proposed. The Analytic Hierarchy Process (AHP) is used for comparison of the components. The bridge condition rating is achieved by summering all the components ratings multiplied by their relative importance, and it is presented as a value of the Historic Road Bridge Condition Assessment Index (HRBCAI). The validation is conducted on the bridges built until the end of the Austro-Hungarian Monarchy in Split-Dalmatia County, Croatia.

Advances in Condition Assessment of Water Pipelines

Condition assessment forms an important component of prudent asset management. Information gained through condition assessment contributes to good decision making and optimizing asset life. Methodologies for inspecting pipelines are presented including screening techniques and more detailed techniques. Advances in technology have opened opportunities for new methods of inspection and evaluation of results. In some cases, the performance of an asset is just as important as its condition and sensors can be used to gain a good understanding of pipeline network performance and assist in optimizing maintenance scheduling to provide ongoing service to customers. Astute choice of condition assessment methodology can enhance asset life and achieve organizational targets.

Condition assessment of RC beams using artificial neural networks

In this study, an imaged-based methodology for condition assessment of Reinforced Concrete (RC) road bridge components is presented. The study is divided into experiments on RC rectangular beams and scaled (1:12) T-beams, validation of numerical models for T-beams and training of corresponding artificial neural networks (ANNs). In the experimental work, Digital Image Correlation (DIC) was used as a virtual sensor for data extraction. Rectangular RC beams of size 1800 mm × 150 mm × 200 mm and scaled (1:12) RC T-beams were tested under four-point flexural loading on a 100-ton dynamic testing machine. The experimental stress-strain curves obtained from the compression test on prism specimens at 28 days were used as input data for material model parameters in finite element model (FEM) software SAP2000. To assess the condition of structural components, a local damage index (LDI) was developed. Validation of FEM results with experimental results enables derivation of moment-curvature backbone curve for full-scale bridge girders, which enables further quantification of damage and residual moment capacity of full-scale bridges designed by Ministry of Road Transport and Highways (MoRTH). The correlation between the experiments, simulation and ANN predictions was found to be very satisfactory.

Sewer Pipes Condition Prediction Models: A State-of-the-Art Review

Wastewater infrastructure systems deteriorate over time due to a combination of aging, physical, and chemical factors, among others. Failure of these critical structures cause social, environmental, and economic impacts. To avoid such problems, infrastructure condition assessment methodologies are developing to maintain sewer pipe network at desired condition. However, currently utility managers and other authorities have challenges when addressing appropriate intervals for inspection of sewer pipelines. Frequent inspection of sewer network is not cost-effective due to limited time and high cost of assessment technologies and large inventory of pipes. Therefore, it would be more beneficial to first predict critical sewers most likely to fail and then perform inspection to maximize rehabilitation or renewal projects. Sewer condition prediction models are developed to provide a framework to forecast future condition of pipes and to schedule inspection frequencies. The objective of this study is to present a state-of-the-art review on progress acquired over years in development of statistical condition prediction models for sewer pipes. Published papers for prediction models over a period from 2001 through 2019 are identified. The literature review suggests that deterioration models are capable to predict future condition of sewer pipes and they can be used in industry to improve the inspection timeline and maintenance planning. A comparison between logistic regression models, Markov Chain models, and linear regression models are provided in this paper. Artificial intelligence techniques can further improve higher accuracy and reduce uncertainty in current condition prediction models.

Bridge damage detection using rotation measurements – Experimental validation

• Rotation is a sensitive parameter for identifying damage in a bridge. • Rotation can be accurately measured using a high-grade DC accelerometer. • Difference in rotation influence lines for healthy and damaged states successfully identifies damage in a model bridge. • For simply supported bridges, supports are optimum sensor locations for damage identification. This paper presents a novel bridge condition assessment methodology using direct rotation measurements. Initially, numerical analyses are carried out to develop the theoretical basis of the proposed bridge damage detection methodology. As a result of this study the difference in rotation influence lines obtained for healthy and damaged bridge states is proposed as a damage indicator. The sensitivity of rotation to damage and the effect of sensor locations on sensor sensitivities are investigated. Subsequently, extensive laboratory experiments are conducted on a 5.4 m long simply supported bridge structure in an effort to validate the results from the numerical analyses. The test structure is instrumented with rotation sensors and axle detectors and loaded with a four-axle moving vehicle. In this study, rotations are measured using high grade uniaxial accelerometers. The procedure of measuring rotations using accelerometers is explained within the scope of this study. To test the robustness of the proposed bridge condition assessment methodology, a wide range of single and multiple damage scenarios is investigated and the results from this study show that the proposed methodology can successfully identify damage on a bridge. For the model considered, damage as low as 7% change in stiffness over an extent of 2.5% bridge span is shown to be detectable.

Pyrite decay - into the great unknown

More than 200 years of research have brought us little closer to understanding, let alone controlling, pyrite oxidation in geological collections. This appears paradoxical, because thousands of papers have been written on the deterioration of iron sulfides by geochemists, engineers and conservators. But whilst reaction products and pathways are characterised well, the causes of deterioration of different types of iron sulfide specimens in geological collections remain elusive. To make matters even more complicated for the museum conservator, published guidelines for the management of such specimens are patchy and often contradictory. Currently available condition assessment methodologies are barely suitable for routine monitoring of large collections, results of specimen monitoring exercises are not necessarily replicable, and, in the absence of guidance on suitable storage conditions, triggers for, and suitability of, conservation actions are difficult to determine. A new approach is required to end the guess work and add some substance, based on evidence, to the collection care of geological collections in museums. This paper does not answer these questions but introduces a framework for a research agenda that would underpin a robust approach to the delivery of preventive conservation of geological collections. This includes a definition of what kind of material change in minerals constitutes damage; categorisation of damage/change; development of a protocol for routine condition assessments; the definition of an adequate storage environment; and testing rigorously the suitability of conservation treatments.