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This paper presents a critical review of literature on the emerging technology known as digital twin and its application in structural integrity management for marine structures. The review defines digital twin in relation to structural integrity management as a virtual representation of a physical structure that mirrors the same structural conditions in real time. Twinning is a dynamic process that involves reducing the discrepancy between the virtual representation and physical structure, which is achieved with the aid of monitored data. Regarding the state-of-the-art concerning marine structure applications, all require the creation of a finite element model to represent the physical structure. Several practical schemes for physical to virtual interconnection have been proposed, but few researchers have concentrated on virtual to physical feedback. In addition, most works have focused only on assessing the current states of structures. To address this, a digital twin-based monitoring framework is proposed and three key enabling technologies, namely model updating, real-time simulation, and data-driven forecasting are demonstrated using a numerical case study. Such technologies enable structural diagnostics, as well as prognostics, to support decision making such as inspection/maintenance planning. Based on the case study, the opportunities and associated challenges of digital twin are discussed. For instance, to fully exploit the potential of digital twin, challenges related to monitoring systems such as standardisation, enhanced redundancy for long-term application, and monitored data quality assurance need to be addressed. Further, because digital twin can avail a vast amount of data, a dedicated data mining capability should also be incorporated. [ABSTRACT FROM AUTHOR]

Translation abstract Optimization of the method for calculating influence lines from measurement dataAs part of the measurement‐based reassessment of steel railway bridges, influence lines resulting from the passage of a load vehicle are used to determine structural loading. The commonly used method for deriving influence lines from measurement data is the Braune method, which computes the influence line step by step from the data. This article discusses the matrix method, an alternative method for determining influence lines, and compares it with the Braune method. For this purpose, different factors influencing the result quality of the influence lines were investigated using artificial strain lines generated on the static model of the Norderelbe railway overpass and validated using measurement data obtained during the reassessment of the bridge. The findings demonstrate that the matrix method offers a robust approach to influence line determination and the amount of work involved in determining the influence lines can be reduced in comparison to the Braune method. Furthermore, the matrix method can be flexibly integrated into the existing process of influence line determination. [ABSTRACT FROM AUTHOR]

As aging infrastructures raise public concerns, evaluating their performance is crucial for maintaining structural integrity, especially for corroding prestressed concrete members. These structures may experience substantial tendon cross‐sectional area loss before any visible deterioration becomes detectable. While various non‐destructive techniques (NDT) have proven effective in labs, correlating corrosion‐induced damage in field members remains a challenge. Establishing these correlations is key for understanding the overall performance of aging structural concrete elements and ensuring their continued safe operation through non‐invasive means. This paper investigates various NDTs on a concrete bridge deck, aiming to correlate results. Visual inspection, Schmidt rebound hammer, Ultrasonic Pulse Velocity (UPV), corrosion detection techniques, Ground Penetrating Radar (GPR), Ultrasonic Pulse Echo (UPE), and Impact Echo (IE) methods are evaluated for detecting concrete deck damage. Results show the methods' capabilities in detecting defects to a certain extent, highlighting their potential in assessing aging concrete infrastructures. [ABSTRACT FROM AUTHOR]

Contact probe is broadly used for the continuous monitoring of microelectronic components in manufacturing industries. False rejection of fine product due to defective contact probe significantly reduces the yield in production. Traditionally, defect detection for contact probes heavily depends on a valid range manually defined by engineers over the measured value of certain parameters. However, the subjective range defined according to engineer experience is prone to trigger a high rate of false alarms due to the inherent noise in the measured parameters. To address this issue, we construct a health index (HI) with the contact resistance‐directly‐related features to help monitor and assess the condition of contact probe. Based on the established HI, we develop Long Short‐Term Memory (LSTM) encoder‐decoder machine learning model to assess the condition of contact probe by forecasting the HI value in the future. Encoders from LSTM and convolutional neural network (CNN) are selected as the encoder‐decoder architecture for the sequence‐to‐sequence prediction due to their advantage in extracting the correlation of features at different scales. An explainable Artificial Intelligence (XAI) technique named Local interpretable model‐agnostic explanations (LIME) is used to quantify the contribution of each feature to the model prediction. The encoder from CNN is found to outperform the LSTM encoder in extracting the inter‐feature correlation. Finally, the predicted HI is used to signal the alarm for the maintenance action of contact probe when its value is below a predefined threshold. Comparison between the action alarm triggered by the developed HI and the actual maintenance records suggests that the proposed approach achieves at least 75% accuracy for the triggered alarm in the next 15 mins. [ABSTRACT FROM AUTHOR]

Evaluation and strengthening of existing bridges – historical outlines and findings regarding the Hessian road bridge inventory Two short histographical sketches introduce the concepts of refurbishment, condition and load‐bearing capacity assessment between 1700 and 2010 and take a look at the development of the recalculation of existing bridges. Based on project sketches, the hidden safety features of existing road bridges are worked out and procedures for achieving efficient structural bridge strengthening are presented. The focus is on the determination of real object‐specific traffic load models, calibrated structural analyses and additive and non‐destructive strengthening methods. Some critical comments on planning acceleration and the current approval system, together with the demand that bridge construction should be reflected in the common objective of "traffic", broaden the definition of strengthening holistically. This offers the opportunity to stimulate a discussion about the sensible handling of existing bridges as part of our road infrastructure. [ABSTRACT FROM AUTHOR]

Earthen architecture holds deep historical, cultural, and ecological value, forming an essential component of our global cultural heritage. However, these structures face numerous threats, including climate change, socio-economic shifts, and, in many cases, neglection, which accelerate their deterioration. This study introduces a photogrammetry-based methodology adapted for the digital documentation and preservation of earthen architecture within the context of developing countries. We focus on the Ex-Hotel Oasis Rouge in Timimoun, an iconic earthen building in southwestern Algeria and the current headquarters of CAPTERRE (Algerian Centre for Earthen Built Cultural Heritage). This paper details our approach to using photogrammetry to capture both the interior and exterior of the building, produce detailed orthophotos for archiving the unique earthen bas-reliefs, and conduct a condition assessment. Our findings demonstrate the effectiveness of photogrammetry as a cost-effective tool for heritage documentation, highlighting its potential to assist in the ongoing preservation and informed restoration of earthen architecture. [ABSTRACT FROM AUTHOR]

The structural health monitoring (SHM) of buildings provides relevant data for the evaluation of the structural behavior over time, the efficiency of maintenance, strengthening, and post-earthquake conditions. This paper presents the design and implementation of a continuous SHM system based on dynamic properties, base accelerations, crack widths, out-of-plane rotations, and environmental data for the retrofitted church of Kuñotambo, a 17th century adobe structure, located in the Peruvian Andes. The system produces continuous hourly records. The organization, data collection, and processing of the SHM system follows different approaches and stages, concluding with the assessment of the structural and environmental conditions over time compared to predefined thresholds. The SHM system was implemented in May 2022 and is part of the Seismic Retrofitting Project of the Getty Conservation Institute. The initial results from the first twelve months of monitoring revealed seasonal fluctuations in crack widths, out-of-plane rotations, and natural frequencies, influenced by hygrothermal cycles, and an apparent positive trend, but more data are needed to justify the nature of these actions. This study emphasizes the necessity for extended data collection to establish robust correlations and refine monitoring strategies, aiming to enhance the longevity and safety of historic adobe structures under seismic risk. [ABSTRACT FROM AUTHOR]

Fiber-reinforced polymer (FRP) wraps of reinforced concrete columns might be vulnerable to accidental damage or vandalism. This study aims to develop a simple empirical tool that is based on experimental results to aid in the condition assessment and establish the reduced axial strength ratio (P/Po) of short columns that are loaded at small eccentricities. A total of 47 cylinders, 152 mm in diameter (D), were wrapped with carbon–FRP sheets of 1–3 layers [i.e., producing confinement effectiveness (fcc′/fc′) of 2.3–4.0] and were then subjected to a vertical cut of length (x) that varied from 0.20D to 0.61D at midheight. The loading eccentricity (e) varied from zero to 0.1D. The cut location around the perimeter, for the angle (θ) that was measured from the point of extreme compression, was varied from 0° to 180°. The jacket cuts affect eccentrically loaded cylinders more severely than concentrically loaded ones. As e increased from zero to 0.1D, P/Po reduced from 1.0 to 0.81 for intact samples and from 0.56 to 0.40 for samples with jacket cut. As the cut location traveled around the circumference starting from extreme compression (θ = 0°), less strength reduction was observed where P/Po increased, and at θ = 180° it even exceeded P/Po at zero eccentricity. This suggests a reduction in eccentricity due to a shift in the cross-sectional centroid due to the disturbed confinement on one side. Design models that account for e/D, θ, x/D, and inclined cuts have been developed and calibrated for cylinders of up to 22 slenderness ratio. In addition, a design example is presented. [ABSTRACT FROM AUTHOR]

Damage assessment of tall buildings after an earthquake is important for efficient postearthquake management due to social and economic reasons. Structural health monitoring (SHM) system enables rapid, remote, and objective condition assessment for tall buildings by controlling dynamic properties of structures. However, tracking only the changes in dynamic properties of tall buildings may not be sufficient for damage assessment. In this paper, changes in modal frequencies and maximum interstory drift ratio are investigated as damage assessment indicators because they can be obtained by analyzing vibration data recorded by SHM system. On the other hand, limited number of sensors are used due to economic reasons. Therefore, in this paper, firstly, a unique methodology on development and optimization of nonlinear simplified model for tall buildings is presented to estimate responses of noninstrumented floors from instrumented floors. After that, new threshold values are suggested for changes in dynamic properties and interstory drift ratios to reliably decide performance level of structures after earthquakes. Finally, the proposed method was validated with vibration record of a real damaged building. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: The current clinical practice lacks sufficient objective indicators for evaluating thyroid-associated ophthalmopathy (TAO). This study aims to quantitatively assess TAO by evaluating levator palpebrae superioris (LPS) using Dixon-T2WI. Methods: The retrospective study included 231 eyes (119 patients) in the TAO group and 78 eyes (39 volunteers) in the normal group. Dixon-T2WI provided data on maximum thickness of LPS (LPS_T) and signal intensity ratio (LPS_SIR) between the muscle and ipsilateral brain white matter. TAO diagnosis and assessment of its activity and severity were quantitatively determined using LPS_T and LPS_SIR. Results: In the TAO group, LPS_T and LPS_SIR were higher than those in the normal group (p < 2.2e-16). The upper lid retraction (ULR) ≥ 2 mm group exhibited higher LPS_T and LPS_SIR compared to the ULR < 2 mm and normal groups. Optimal diagnostic performance was achieved with an AUC of 0.91 for LPS_T (cutoff: 1.505 mm) and 0.81 for LPS_SIR (cutoff: 1.170). LPS_T (p = 2.8e-07) and LPS_SIR (p = 3.9e-12) in the active phase were higher than in the inactive phase. LPS_T and LPS_SIR showed differences among the mild, moderate-tosevere, and sight-threatening groups (p < 0.05). ROC showed an AUC of 0.70 for LPS_T (cutoff: 2.095 mm) in judging the active phase, and 0.78 for LPS_SIR (cutoff: 1.129). For judging the moderate-to-severe and above, AUC was 0.76 for LPS_T (cutoff: 2.095 mm) and 0.78 for LPS_SIR (cutoff: 1.197). Conclusion: The maximum thickness and SIR of LPS provide imaging indicators for assisting in the diagnosis and quantitative evaluation of TAO. [ABSTRACT FROM AUTHOR]

In the inner areas of large cities, many residential buildings built at the turn of the 19th and 20th centuries remain standing. The maintenance and renovation of these buildings have emerged as critical priorities over recent decades. E.g., in Budapest during the socialist era, the majority of these buildings were not renovated, and maintenance was largely neglected. In the subsequent 10–15 years following the end of socialism, financial resources for renovations were scarce due to the extensive transfer of properties from state to private ownership. It is only in the last decade or so that renovations have begun to be systematically addressed. Consequently, a significant portion of the building stock is still pending renovation. Given the current economic conditions, sustainable maintenance and necessary conversion are of paramount importance. Unfortunately, few standardized condition assessment methods are implemented in industrial practice, and the literature on this topic is limited. To address these challenges, we have developed an algorithm and model for condition assessment and decision support, which we refer to as the Complex Building's Decision Support System based on Fuzzy Signatures (CBDF system). Our model employs a fuzzy signature-based approach to account for uncertainties, errors, and potentially missing data that may arise during the assessment process. The primary aim of this model is to equip professionals involved in building condition assessment with a tool that enables them to make consistent and objective decisions while minimizing errors. This paper provides a brief overview of the CBDF system and presents test results from the assessment of a selected structural component of a building, demonstrating the system's functionality. [ABSTRACT FROM AUTHOR]

Steel poles are extensively used as street light posts and various other applications within the power distribution network. For partially buried pole, the effect of corrosion below and at the ground level significantly controls the overall load capacity. In this study, number of in-service steel pole specimens extracted from field were assessed for the corrosion damage. The level of corrosion was estimated by remaining wall thickness measured using ultrasonic testing and the surface corrosion was estimated using 3D laser scanning to adopt an appropriate existing model for buried steel structures. A relationship between maximum and average corrosion pit depth and loss of embedment depth was then developed. Finally, the corrosion parameters, combined with uncertainty in loading and soil properties were employed to perform time-dependent reliability assessment for steel light poles which can be used to assist the asset maintenance team in planning the replacement or strengthening works for steel pole.

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. HIGHLIGHTS The condition of indicators/sub-indicators of this MDPA model for pipe condition assessment can be obtained either by data estimation or techniques.; The MDPA model is capable of exporting pipe detailed conditions for suggesting primary maintenance measures.; The MDPA model can be tested or verified by advanced techniques to guarantee model accuracy.;

Prefabricated electrochemical energy storage stations are crucial for the promotion and application of energy storage projects. To conduct research on digital twin technology throughout the lifecycle， the paper proposes a method for establishing a digital twin model for electrochemical energy storage power stations based on electric-thermal-fluid fields. Firstly， standardized testing protocols for batteries are designed to acquire fundamental characteristics such as capacity， energy， open circuit voltage （OCV）， and power， as well as the coupling laws of multiple physical fields of batteries. Subsequently， battery model parameters are identified through test data， and an aging model parameter database is constructed. Next， the aging process of batteries is revealed based on incremental capacity analysis （ICA）， and the maximum available capacity of the batteries is extracted through feature mapping. Following this， leveraging the coupling characteristics of multiple physical fields of batteries， thermal and fluid field models of battery compartments are established using real-world laser scanners， engineering drawings， and COMSOL Multiphysics software. Finally， technical validation is performed using a user-side prefabricated energy storage station in Zhejiang Province， demonstrating that the established digital twin model enables deduction and state assessment of electric-thermal-fluid fields.

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The condition assessment of water distribution pipes is of utmost importance for the prioritization of rehabilitation interventions. However, the application of available methodologies for condition assessment by water utilities with limited human, technological and financial resources is becoming increasingly complex. The current paper aims at the development and application of a methodology for the prediction of the physical condition of water distribution pipes without the need for visual inspection. The methodology includes the development and application of three different algorithms (heuristic, linear regression and support vector regression). The methodology is applied to a water distribution network located in the Algarve region, Portugal. The results obtained from each algorithm are compared with a well-known performance indicator, the ratio of useful life, and present significant differences in its overall pipe condition classification. Results have demonstrated the following: the ratio of useful life tends to distribute pipe classification more equally in the three classes (i.e., good, average and unsatisfactory); the heuristic algorithm classifies most pipes as average condition; and the linear regression algorithm classifies with unsatisfactory conditions. The support vector regression algorithm stands out as the main classifier for identifying pipes in good condition when compared to other algorithms. [ABSTRACT FROM AUTHOR]

Oil–paper insulation is the critical insulation element in the modern power system. Under a harsh operating environment, oil–paper insulation will deteriorate gradually, resulting in electrical accidents. Thus, it is important to evaluate and monitor the insulation state of oil–paper insulation. Firstly, this paper introduces the geometric structure and physical components of oil–paper insulation and shows the main reasons and forms of oil–paper insulation's degradation. Then, this paper reviews the existing condition assessment techniques for oil–paper insulation, such as the dissolved gas ratio analysis, aging kinetic model, cellulose–water adsorption isotherm, oil–paper moisture balance curve, and dielectric response technique. Additionally, the advantages and limitations of the above condition assessment techniques are discussed. In particular, this paper highlights the dielectric response technique and introduces its evaluation principle in detail: (1) collecting the dielectric response data, (2) extracting the feature parameters from the collected dielectric response data, and (3) establishing the condition assessment models based on the extracted feature parameters and the machine learning techniques. Finally, two full potential studies are proposed, which research hotspots' oil–paper insulation and the electrical–chemical joint evaluation technique. In summary, this paper concludes the principles, advantages and limitation of the existing condition assessment techniques for oil–paper insulation, and we put forward two potential research avenues. [ABSTRACT FROM AUTHOR]

• ATR-FTIR spectroscopy is used to assess the condition of historical silk samples. • Evaluating the conditions of historical textiles guides conservation and preventive measures. • Differentiation between original and later interventions is also possible. • Visual, bivariate and multivariate methods are used to evaluate the data. • Spectral evaluation reveals deterioration markers, explained according to literature. Understanding the conservation condition of historical silk yarn allows to define appropriate storage, care and display of historical silk collections. This paper discusses the characterisation of silk fabrics from a collection of traditional Japanese samurai armours which date back from the 16th to the 20th century (Morigi Collection, Museo delle Culture, Lugano, Switzerland). An analytical protocol to assess silk fabrics conditions was defined, based on microinvasive ATR-FTIR spectroscopy. In particular, the amide I and II region was studied in order to extrapolate the conformational information about silk proteins. According to literature, this kind of information can be related to different degradation stages. A linear correlation was found between the amide I and the amide II shifts, allowing to assess the silk fibre condition. Along with this bivariate approach based on intensity ratios, a multivariate approach based on Principal Component Analysis was also applied to ATR-FTIR spectra. This allowed to group together silks with the same state of preservation. The findings of this research offer a valuable method to researchers and conservators to identify the most damaged textiles; the differentiation between original and restoration materials was also possible in some cases. [ABSTRACT FROM AUTHOR]

Facility management maintains building service quality through cycles of condition assessments and rehabilitation. Building components, however, differ in their nature, service lives, deterioration patterns, and textual/visual inspection data. This complicates the condition assessment process and subsequent rehabilitation decisions. This paper proposes a smart condition assessment framework that uses different artificial intelligence (AI) techniques that suit the condition data analysis of different building components. The framework has been applied to a dataset of over 2000 maintenance requests for roof and heating, ventilation, and air conditioning (HVAC) systems across a 600-villa portfolio. To address their varying needs, convolutional neural networks were used on images of roof defects, while enhanced data mining was used on textual data of HVAC systems. Accordingly, work packages of deteriorated components were identified, and a 60-day schedule was developed to repair 203 HVAC units. This research shows how AI can assist facility management with respect to condition assessment, rehabilitation planning, and resource allocation. [ABSTRACT FROM AUTHOR]

The article discusses the condition assessment and asset management of Houston's East Water Purification Plant (EWPP), which consists of three surface water treatment plants. The plants were built at different times and have undergone multiple upgrades to meet increasing water demand and regulatory requirements. The city conducted a risk-based assessment of the plant's assets to determine their condition and prioritize rehabilitation or replacement. The article highlights the challenges faced by the plant, such as declining filter performance, and the measures taken to improve water quality and filter run times. The project offers insights that can help other utilities make informed decisions about their aging infrastructure. [Extracted from the article]

Wolf Rock Lighthouse is a Victorian era masonry structure located in an extreme environment facing the fiercest Atlantic storms off the southwest coast of England whose dynamic behaviour has been studied since 2016. Initially, a modal test was used to determine modal parameters; then, in 2017, a monitoring system was installed that has operated intermittently providing response data for a number of characteristic loading events. These events have included wave loads due to storms, a small UK earthquake, helicopters landing on the helideck, and the grounding of a ship on the reef. This is believed to be the most extensive experimental campaign on any structure of this type. This paper briefly describes a unique project involving the characterisation and measurement of dynamic behaviour due to different forms of dynamic loading. [ABSTRACT FROM AUTHOR]

Conventional structural health monitoring (SHM) evaluates the condition of civil structures by analyzing the data acquired by advanced sensors. The requirement of overinvestment in specialized equipment and labor for implementation prevents the traditional SHM from large-scale usage. On the other hand, computer vision techniques offer cost-effective solutions for SHM thanks to its inherent advantage in data acquirement and processing. More importantly, it has been demonstrated that these emerging solutions can produce reliable condition diagnoses for civil structures using pure image data. In this article, a novel transformer-based neural network is proposed for vision-based structural condition assessment which is formulated to a semantic segmentation problem. The network employs Swin Transformer as the backbone and MaskFormer as the overall architecture to recognize components (sleepers, slabs, columns, etc.) and damage (concrete damage, exposed rebar) of structures. Unlike the commonly used fully convolutional networks, the proposed model tackles semantic segmentation as a mask classification rather than a pixel classification problem. To deal with the lack of training data, an image data augmentation method called Copy-Paste is extended and applied for training data generation, resulting in an increase of around 40% data for component segmentation and 71% data for damage segmentation. Experimental validations on the Tokaido railway viaduct dataset show that the proposed approach is very accurate, achieving 97% and 90% mean Intersection Over Union for component and damage segmentation, outperforming the existing methods by a significant margin. The accurate segmentation results can provide meaningful information for downstream SHM tasks. [ABSTRACT FROM AUTHOR]

The present study undertakes a scoping review of research on the methodologies and techniques used for the maintenance and condition assessment of power transformers, which are the main asset in the electrical power transmission sector. It addresses articles on asset management, monitoring and diagnostics, oil analysis, and insulation moisture, with these articles originating from twenty-five countries and being published in journals in the last fifteen years, with more than half of them published in the last five years. The aim of this research is to map the literature linked to the topic in a broader and more exploratory manner and to identify any existing gaps in knowledge. Guidelines such as eligibility criteria, sources of evidence, data charting, and result summaries are described. This study finds that data analysis methodologies related to identifying failures and aiding decision-making can add value to power transformer asset management and this scoping review was the basis for the development of an inedited methodology to aid decision-making regarding investments in the maintenance of power transformers.

Abstract Bridge deck condition assessments are typically conducted through visual inspections and by incorporating traditional contact sensors for Non-Destructive Evaluation techniques such as hammer sounding and chain dragging, which require the keen expertise of trained inspectors. The accuracy of these inspections is proportional to the level of deterioration of the bridge deck, as the ability of the inspectors is correlated to the apparent level of damage. This study aims to improve the accuracy of bridge deck inspection processes by utilizing non-destructive evaluation techniques, including analyzing point cloud data gathered via Light Detection and Ranging (LiDAR) as a geometry-capturing tool for identifying surface irregularities. This research aims to evaluate and quantify the effectiveness and efficiency of LiDAR sensors in contributing to the suite of technologies available to perform bridge deck condition assessment. To achieve this, the research proposes to understand the deterioration pattern of New Jersey bridges, evaluate the results gathered from point cloud data collected on a full-scale bridge deck, and quantify the information gained from deploying LiDAR on operating bridges in New Jersey. Two data processing approaches were chosen to measure the gross and fine dimensions of the evaluated bridge decks, such as the Curvature Extraction and Slope Analysis method, and the Least Square Plane Fitting method, resulting in an accuracy of 97.92% in reference to the results gathered from reports generated through the analysis of state-of-the-art NDE technology data and visual inspection.

Earthen architecture holds deep historical, cultural, and ecological value, forming an essential component of our global cultural heritage. However, these structures face numerous threats, including climate change, socio-economic shifts, and, in many cases, neglection, which accelerate their deterioration. This study introduces a photogrammetry-based methodology adapted for the digital documentation and preservation of earthen architecture within the context of developing countries. We focus on the Ex-Hotel Oasis Rouge in Timimoun, an iconic earthen building in southwestern Algeria and the current headquarters of CAPTERRE (Algerian Centre for Earthen Built Cultural Heritage). This paper details our approach to using photogrammetry to capture both the interior and exterior of the building, produce detailed orthophotos for archiving the unique earthen bas-reliefs, and conduct a condition assessment. Our findings demonstrate the effectiveness of photogrammetry as a cost-effective tool for heritage documentation, highlighting its potential to assist in the ongoing preservation and informed restoration of earthen architecture.

Concrete surface cracks serve as early indicators of potential structural threats. Visual inspection, a commonly used and versatile concrete condition assessment technique, is employed to assess concrete degradation by observing signs of damage on the surface level. However, the method tends to be qualitative and needs to be more comprehensive in providing accurate information regarding the extent of damage and its evolution, notwithstanding its time-consuming and environment-sensitive nature. As such, the integration of image analysis techniques with artificial intelligence (AI) has been increasingly proven efficient as a tool to capture damage signs on concrete surfaces. However, to improve the performance of automated crack detection, it is imperative to intensively train a machine learning model, and questions remain regarding the required image quality and image collection methodology needed to ensure the model's accuracy and reliability in damage quantitative analysis. This study aims to establish a procedure for image acquisition and processing through the application of an image-based measurement approach to explore the capabilities of concrete surface damage diagnosis. Digitizing crack intensity measurements were found to be feasible; however, larger datasets are required. Due to the anisotropic behavior of the damage, the model's ability to capture crack directionality was developed, presenting no statistically significant differences between the observed and predicted values used in this study with correlation coefficients of 0.79 and 0.82. [ABSTRACT FROM AUTHOR]

The present work offers a comprehensive overview of methods related to condition assessment of bridges through Structural Health Monitoring (SHM) procedures, with a particular interest on aspects of seismic assessment. Established techniques pertaining to different levels of the SHM hierarchy, reflecting increasing detail and complexity, are first outlined. A significant portion of this review work is then devoted to the overview of computational intelligence schemes across various aspects of bridge condition assessment, including sensor placement and health tracking. The paper concludes with illustrative examples of two long-span suspension bridges, in which several instrumentation aspects and assessments of seismic response issues are discussed. [ABSTRACT FROM AUTHOR]

Although doubly fed induction generators (DFIG) are widely used, difficulties in early fault detection and severity assessment for inter-turn short-circuit (ITSC) faults are highly prominent. In this manuscript, a novel incipient fault detection and state assessment method based on external leakage flux sensing and modified multiscale poincare plots (MMSPP) is proposed. An external leakage flux sensor is placed on the axial-end position of the generator to monitor the presence and evolution of ITSC faults. Multiscale poincare mapping is a novel nonlinear tool that is further developed and modified using the normal cumulative distribution function and multiscale computing methods to capture the behavior evolution and changes in the generator's external leakage flux signals. The healthy indicator is based on the analysis of elliptical orbit features extracted from MMSPP, established by support vector data description with parameter optimization. The effectiveness of the proposed method was implemented and verified under an experimental environment on a 100 kW DFIG platform to detect incipient inter-turn short circuits (mainly considering the two-turn ITSC) and evaluate the performance degradation (three- to eight-turn ITSC) with 0%, 50%, and 100% different load conditions. The experimental results showed the viability of the approach and fault indicator for incipient fault detection and condition assessment of the wind generator's low inter-turn insulation faults and for relative quantification of ITSC fault severity. [ABSTRACT FROM AUTHOR]

Structural condition diagnostics provides the basis for decision making regarding the possibility of continued safe operation, necessary reinforcement, repair work, and in extreme cases, dismantling of the structure. The most reliable results concerning the condition and strength of materials are provided by destructive testing. However, these tests are very time-consuming, costly, and difficult to perform on in-service facilities. In addition, they involve the need to obtain the consent of the occupants of the premises and subsequent renovations. This article focuses on presenting an opportunity to reduce the number of destructive tests necessary to reliably assess the condition of large-panel structures, which constitute a significant housing stock in Europe. Based on tests carried out on a real building, the risk factors associated with obtaining reliable results by non-destructive methods were determined. Areas where destructive testing is necessary were identified. In addition, reference was made to standard recommendations and guidelines from a reputable research institution. Practical guidelines were formulated regarding the diagnostics of large-panel structures, resulting in a reduction in the number of destructive tests required. [ABSTRACT FROM AUTHOR]

This paper presents a model-based probabilistic structural identification that uses the deflection of prestressed concrete bridges (PSCBs) as observational information to perform Bayesian inference on the state variables associated with creep, structural rigidity, dead loads, shrinkage and prestress. The creep development is modeled as a stochastic process, and the structural rigidity is modeled as a stochastic field using the Karhunen-Loeve transform. By incorporating the stochastic process/field into the inference frame, detailed information on structural states can be extracted. Considering the high dimension of the state variables, their posterior distributions are derived by the Hamiltonian Markov chain Monte Carlo (HMCMC) algorithm. As an illustrative example, two sets of deflection measurement of a case bridge are used to update the state variables in a sequential manner. Bayesian inference can calibrate the state variables, while the uncertainties associated with the state variables can be reduced. A K-means analysis can reveal the typical modes in the joint posterior distribution of the state variables, corresponding to the typical failure modes in the attributive analysis of the excessive deflection. The updated state variables are used in the probabilistic condition assessment associated with the deflection evolution. [ABSTRACT FROM AUTHOR]

The Proportional Hazards Model (PHM) under a Condition-Based Maintenance (CBM) policy is used by asset-intensive industries to predict failure rate, reliability function, and maintenance decisions based on vital covariates data. Cox's partial likelihood optimization is a method to assess the weight of time and conditions into the hazard rate; however, parameter estimation with diverse covariates problem could have multiple and feasible solutions. Therefore, the boundary assessment and the initial value strategy are critical matters to consider. This paper analyzes innovative non/semi-parametric approaches to address this problem. Specifically, we incorporate IPCRidge for defining boundaries and use Gradient Boosting and Random Forest for estimating seed values for covariates weighting. When applied to a real case study, the integration of data scaling streamlines the handling of condition data with diverse orders of magnitude and units. This enhancement simplifies the modeling process and ensures a more comprehensive and accurate underlying data analysis. Finally, the proposed method shows an innovative path for assessing condition weights and Weibull parameters with data-driven approaches and advanced algorithms, increasing the robustness of non-convex log-likelihood optimization, and strengthening the PHM model with multiple covariates by easing its interpretation for predictive maintenance purposes. [ABSTRACT FROM AUTHOR]

Pipeline integrity and safety depend on the detection and prediction of stress corrosion cracking (SCC) and other defects. In oil and gas pipeline systems, a variety of corrosion-monitoring techniques are used. The observed data exhibit characteristics of nonlinearity, multidimensionality, and noise. Hence, data-driven modeling techniques have been widely utilized. To accomplish intelligent corrosion prediction and enhance corrosion control, machine learning (ML)-based approaches have been developed. Some published papers related to SCC have discussed ML techniques and their applications, but none of the works has shown the real ability of ML to detect or predict SCC in energy pipelines, though fewer researchers have tested their models to prove them under controlled environments in laboratories, which is completely different from real work environments in the field. Looking at the current research status, the authors believe that there is a need to explore the best technologies and modeling approaches and to identify clear gaps; a critical review is, therefore, required. The objective of this study is to assess the current status of machine learning's applications in SCC detection, identify current research gaps, and indicate future directions from a scientific research and application point of view. This review will highlight the limitations and challenges of employing machine learning for SCC prediction and also discuss the importance of incorporating domain knowledge and expert inputs to enhance the accuracy and reliability of predictions. Finally, a framework is proposed to demonstrate the process of the application of ML to condition assessments of energy pipelines. [ABSTRACT FROM AUTHOR]

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