Asset Condition Assessment Research Articles

Customized Approaches for Introducing Road Maintenance Management in I-BIM Environments

Road maintenance management aims to satisfy quality, comfort, and safety requirements for the various assets. To overcome delays and barriers in the widespread adoption of road management systems, the Building Information Modeling (BIM) approach may offer significant advantages as a convenient alternative for road maintenance management. Although existing BIM platforms are not fully equipped for this purpose, defining original modules and scripts can extend their capabilities, allowing for the handling of road condition information and maintenance management. In this context, this paper presents an operative framework designed to leverage BIM benefits for road maintenance management, particularly in terms of virtual inspection, asset condition assessment, and maintenance design. To achieve this, specific original and customized smart objects and routines were coded in I-BIM platforms, tailored to different scales, aims, and detail levels. These smart objects incorporate user-defined extended attributes related to pavement condition and maintenance planning (such as roughness, rutting, structural capacity). In particular, the authors have developed original virtual smart objects in different platforms, serving as “containers” for the survey information. These objects are adapted to display quality levels of the pavement segments in a realistic and user-friendly environment. Additionally, original routines were coded to automatically import survey data from external datasets and associate this information with the appropriate objects. This customized and extended approach, not available in commercial platforms, can effectively support maintenance operators.

Application of Satellite-Based Remote Sensing for the Management of Pavement Infrastructure Assets

The long-term monitoring of transportation infrastructure assets at a lower cost and with short mobilization time is of significant interest to both state and federal transportation agencies in the U.S. Because of the significant improvement in spatial and temporal resolution of synthetic aperture radar (SAR) remote sensing systems and a notable reduction in the cost of data acquisition, SAR has now become a viable method to provide economic and rapid condition assessment of transportation assets. A research study was developed and performed to comprehensively perform the inspection and characterization of a pavement surface based on the amplitude of backscattering of an X-band radar. In situ characterization of the test site was first performed using traditional inertial profilers and aerial photogrammetry with unmanned aerial vehicle (UAV) surveys. The results from these in situ methods were compared with the corrected amplitude of the SAR data, which indicated that the distribution of surface roughness values computed from the inertial profiler, UAV, and SAR exhibited similar probability densities at various segmental lengths considered in this study. This suggested that the problematic areas that are evident during in situ characterization can be delineated and quantified based on the normalized radar cross section of the pavement surface. Overall, the outcome of this research exhibits the potential of SAR for future transportation asset management undertakings, and the systematic framework developed as a part of this research could be of significant interest to engineers and transportation practitioners.

Condition Assessment of Medium Voltage Assets: A Review

Condition Assessment of Medium Voltage Assets: A Review

Real-Time Humidity Monitoring Using Distributed Optical Sensor for Water Asset Condition Assessment

It is well accepted that moisture ingress in concrete reduces durability and life span of water assets. Condition assessment is an important tool to inform decision for maintenance, retrofit or replacement. However, the most significant challenge is to obtain accurate condition information, particularly when the inspection points are physically difficult to access or inaccessible. Therefore, a reliable and cost-effective monitoring (sensor) system, preferably real-time with ability to streaming online, would be a useful management tool, particularly for water utilities. This paper describes an approach to develop a distributed optical fibre humidly sensor for condition assessment and environmental monitoring both inside and outside of infrastructures, such as inside the concrete and surrounding soil. A new polyelectrolyte multilayer (PEM) coating with higher sensitive was evaluated for relative humidity measurement in soil and concrete, respectively. In this study, two simulated conditions, in concrete and soil, were conducted to evaluate the sensing concept with the development of appropriate measuring methodologies including fibre installation and protection. The optical fibre sensor setup in laboratory environment showed that optical sensor can detect and indicate voltage change with the variation of moisture contents in both soil and concrete. The test results indicate a good correlation between high levels of relative humidity/moisture and transmitted optical power. A simple relative humidity (RH) calibration can be used to convert signal to RH in percentage for soil and concrete measurements and the procedure used to imbed the fibre in both samples is effective. Nevertheless, the sensor measures soil humidity (not moisture content); therefore, further investigation is required to identify the consequence for the variation of the measured parameter.

Influence of an integrated value-based asset condition assessment in built asset management

Purpose Built asset management processes require a long transition period to collect, edit and update asset conditions information from existing data sets. This paper aims to explore and explain whether and how digital technologies, including asset information model (AIM), Internet of Things (IoT) and blockchain, can enhance asset conditions assessment and lead to better asset management. Design/methodology/approach Mixed methods are applied to achieve the research objective with a focus in universities. The questionnaire aims to test whether the integration of AIM, IoT and blockchain can enhance asset condition assessment (ACA). Descriptive statistical analysis was applied to the quantitative data. The mean, median, mode, standard deviation, variance, skewness and range of the data group were calculated. Semi-structured interviews were designed to answer how the integration of AIM, IoT and blockchain can enhance the ACA. Quantitative data was analysed to define and explain the essential factors for each sub-hypothesis. Meanwhile, to strengthen the evaluation of the research hypothesis, the researcher also obtained secondary data from the literature review. Findings The research shows that the integration of AIM, IoT and blockchain strongly influences asset conditions assessment. The integration of AIM, IoT and blockchain can improve the asset monitoring and diagnostics through its life cycle and in different aspects, including financial, physical, functional and sustainability. Moreover, the integration of AIM, IoT and blockchain can enhance cross-functional collaboration to avoid misunderstandings, various barriers and enhance trust, communication and collaboration between the team members. Finally, costs and risk could be reduced, and performance could be increased during the ACA. Practical implications The contribution of this study indicated that the integration of AIM, IoT and blockchain application in asset assessment could increase the efficiency, accuracy, stability and flexibility of asset assessment to ensure the reliability of assets and lead to a high-efficiency working environment. More importantly, a key performance indicator for ACA based on the asset information, technology and people experience could be developed gradually. Originality/value This study can break the gap between transdisciplinary knowledge to improve the integration of people, technology (AIM, IoT and blockchain) and process value-based ACA in built asset management within universities.

Using image analysis to quantify defects and prioritize repairs in built-up roofs

PurposeThis paper aims to use convolutional neural networks (CNNs) to provide an objective approach to classify deteriorated building assets according to the type and extent of damage. This research supports automated inspection of buildings and focuses on roofing elements as one of the most critical and externally distressed elements in buildings.Design/methodology/approachIn this paper, 5,000+ images of deteriorated roofs from several buildings were collected to design a CNN system that automatically identifies and sizes roofing defects. Experimenting with different CNN formulations, the best accuracy is achieved using two-stage CNNs. The first-stage CNN classifies images into defect/no defect, while the second stage classifies the defected images according to the damage type. Based on the image classification, optimization is used to prioritize roof repairs by maximizing the return from limited rehabilitation funds.FindingsThe developed CNNs reached 95% and 97% accuracy for the first and second phases, respectively, which is higher than achieved in previous literature efforts. Using the proposed model to automate inspection and condition assessment activities proved to be faster than conventional methods. Repair/replace strategy for a case study of 21 campus buildings based on their condition and budgetary constraints was suggested.Research limitations/implicationsFuture research includes testing different data acquisition technologies (e.g. infrared imaging), performing severity-based classification and integrating with BIM for defect localization.Originality/valueThis study provides an objective approach to automate asset condition assessment and improve funding decisions using a combination of image analysis and optimization techniques. The proposed approach is applicable toward other asset types and components.

Probabilistic Bridge Deterioration Prediction, Applied Analytics, and Predictive Modeling

Designing a new bridge structure or designing the rehabilitation of an existing bridge is a time-consuming process. It is important for bridge owners to proactively assess their structures to make sure that by the time rehabilitation or replacement is required, the design phase elements of such a project, including necessary permits, are ready. Assessing future bridge structure condition also helps the owner to evaluate a variety of design choices or options based on the risk imposed by each alternative. Federal regulations (MAP-21) have required state transportation agencies to implement asset management performance measures. Asset management performance strategies are arguably principal in guiding transportation infrastructure investments. Predicting future asset condition helps businesses and engineers to allocate and utilize the available resources in a direction that keeps the existing bridge inventory in the best possible condition, as well as serves the public. Prediction of bridge future condition also assists engineers and bridge owners in making decisions based upon quality information and well-defined and considered objectives [1]. The core element of any bridge management system is the database containing physical condition–rating data obtained through regular inspection and maintenance activities over a significant amount of time. Consequently, asset management relies on having an accurate inventory and condition assessment of assets in real time. One significant problem lies in the stochastic nature of traffic that transpires over time. This research focused on using the Markov process to predict future bridge condition state based on bridge inspection data inputs. Markov chain modeling was selected for this study because bridge condition ratings have a stochastic nature that requires an appropriate deterioration prediction method. The goal was to create a computational tool to predict bridge and highway degradation and assess such a structure’s long- term performance. As a proposed update to the National Bridge Inspection Standard, this study offers two methods for making changes to bridge inspection intervals. Both ways consider risk a significant contributing factor to the deciding criteria, for which this paper presents methodology to perform such a risk calculation..

Computer vision-based generating and updating of the public transit bus stop inventories

An updated asset inventory enables public transit agencies to make informed decisions on the maintenance and improvement of their physical assets. Conventional asset inventory surveys mainly rely on manual site visits and subsequent analysis, which are time consuming and expensive. Many research projects developed methods to automate condition assessment of civil infrastructure assets, such as road surfaces, structures, and sewage systems; however, research on the automated detection and condition assessment of public transit infrastructure is very limited. This research aims to contribute to addressing this gap by introducing an automated computer vision-based system to detect main assets in transit bus stops and update asset inventories using video frames captured by on-board cameras on operating buses. This system uses existing hardware systems on public buses to gather required data and then uses Deep Convolutional Neural Networks (DCNNs) to recognize public transit assets. In addition, a related method was proposed to process manually collected images for semi-automated asset inventory updating. The experimental results showed more than 95% detection rates in videos, which demonstrate potentials for practical applications.

An approach for Building Defects Detection using VGG16

Customers are increasingly looking for fast and efficient ways to frequently check and inspect the condition of their buildings so essential repairs and maintenance can be done in a timely manner. Act quickly before they become too dangerous and costly. Traditional methods for this type of work commonly comprise engaging inspection officers to undertake a condition assessment which involves a lengthy site inspection to produce a systematic recording of the building defects, including cost estimates of immediate and project the long-term costs of renewal, repair and maintenance of the building. Current asset condition assessment procedures are extensively time-consuming and expensive and pose health and safety threats to inspection officers, particularly at height and roof levels which are difficult to access. This project aims toward an automated detection and localization of key building defects, e.g., moss, cracks, deterioration and stains, from images using Convolution Neural Networks. The proposed model was built on a pre-trained CNN classifier of VGG-16, with class activation mapping (CAM) using a squeeze net for object localization. The challenges and limitations of the model in real-life applications have been identified. The proposed neural network model has proved that it can accurately detect and localize building defects. Key Words:Object Detection, Convolutional Neural Network, Deep Learning, VGG16

Asset condition assessment model for healthcare facilities

Asset management of healthcare facilities utilizes technical and administrative strategies to extend the asset life cycle. This research aims to develop an asset condition assessment (ACA) model for a healthcare facility to predict and assess critical asset ratings considering various factors, especially patient risk space groups, and asset redundancy. Two modelling techniques were used to develop the ACA model. First, the Analytic Hierarchy Process (AHP) was used to calculate the weighting criteria and ranking the factors. Second, the AHP and regression analysis techniques were combined into a single model to evaluate healthcare facilities' asset condition assessment. The integrated model was validated with 88.32% average validity percent (AVP). The model was determined to be robust, and its results satisfactory. Finally, this research developed a numerical asset condition assessment (ACA) critical rating scale, which can guide capital renewals leaders in predicting and managing healthcare facilities' critical infrastructure needs. Ultimately, this tool can be used to improve the asset management of healthcare facilities, which is one of the nation's largest infrastructure sectors.

Deep Learning for Detecting Building Defects Using Convolutional Neural Networks.

Clients are increasingly looking for fast and effective means to quickly and frequently survey and communicate the condition of their buildings so that essential repairs and maintenance work can be done in a proactive and timely manner before it becomes too dangerous and expensive. Traditional methods for this type of work commonly comprise of engaging building surveyors to undertake a condition assessment which involves a lengthy site inspection to produce a systematic recording of the physical condition of the building elements, including cost estimates of immediate and projected long-term costs of renewal, repair and maintenance of the building. Current asset condition assessment procedures are extensively time consuming, laborious, and expensive and pose health and safety threats to surveyors, particularly at height and roof levels which are difficult to access. This paper aims at evaluating the application of convolutional neural networks (CNN) towards an automated detection and localisation of key building defects, e.g., mould, deterioration, and stain, from images. The proposed model is based on pre-trained CNN classifier of VGG-16 (later compaired with ResNet-50, and Inception models), with class activation mapping (CAM) for object localisation. The challenges and limitations of the model in real-life applications have been identified. The proposed model has proven to be robust and able to accurately detect and localise building defects. The approach is being developed with the potential to scale-up and further advance to support automated detection of defects and deterioration of buildings in real-time using mobile devices and drones.

KNOWLEDGE ENABLED TECHNOLOGY SELECTION FOR SEWER CONDITION ASSESSMENT

To improve the quality of life of communities, local government provides municipal services, including the sewer collection, effectively and efficiently employing asset management best practices. The condition assessment of assets (including the sewer collection system) is one of the core elements of implementing effective asset management across the utility organizations. Availability of sophisticated and complex condition assessment technologies, lack of common understanding about them and related high cost associated with sewer condition assessment requires the technologies and related knowledge to be explicitly defined in a neutral format-the ontology, to support the development of applications for technology selection. An ontology of condition assessment technologies for sewer network, CATS_Onto was developed following a seven-step approach at two levels of abstraction: meta-model and detailed ontology. This paper presents the development and verification of the meta-model. The proposed ontology will be used to develop a tool for the selection of the most appropriate technology for sewer condition assessment. The knowledge representation was verified while validation is underway.

ASSET CONDITION ASSESSMENT MODEL FRAMEWORK FOR HEALTHCARE FACILITIES

The poor healthcare facilities performance costs the USA tens billions of dollars each year. There are several factors affect the critical assets of healthcare facilities such as the physical condition of the facility, infection prevention, life safety, and hospital revenue loss. The objective of this research is to develop an asset condition assessment model for healthcare facilities. The model identifies several factors, especially the condition, capacity, and code compliance of a facility's infrastructure. The asset condition assessment model has been developed based on the mechanical, electrical and plumping systems and evaluates the asset condition based on its age, life expectancy, recent condition assessment and energy efficiency among other factors. The data was collected from different hospitals in Indiana. This model has been developed using the Regression analysis technique. The model has been validated through analytical and mathematical methods. Results showed average validity at 88.32%. The developed model will help the healthcare facilities managers to prioritize the budget allocation and to reduce the impact of consequences of asset failure, which will improve the healthcare infrastructure performance.

Geospatial Sciences and Space Law: Legal Aspects of Earth Observation, Remote Sensing and Geoscientific Ground Investigations in Africa

Geospatial sciences play crucial roles in and have effects on the socioeconomic, political and security fortunes of states. Earth observation, remote sensing and geoscientific ground investigation increasingly occupy vantage positions in the legal order of states, particularly in evidential terms and in the verification of facts under international law. How then do these aspects of space law and space sciences affect contemporary Africa and the commercial fortunes, as well as international relations among some African states? What impact do they have in relation to: (a) international boundaries disputes and demarcation activities; (b) management and the preservation of the African heritage; (c) disaster and conservation management? The paper will test the hypothesis that it is crucial for the development of the continent especially in the areas mentioned above that states should sustain and increase investment in the following areas: archaeological prospection, condition assessment of heritage assets; Geographic Information System (GIS) analysis of spatial settlement patterns in modern landscapes and assessment of natural or human-induced threats to conservation.

Analysis of building and its components condition assessment case study of dormitory buildings

Condition Assessment of assets is one of stages in assets management system that supports effective and efficient improvement and maintenance strategy. The objective of this paper is to develop a condition assessment model based on important components that build an asset. A building asset hierarchy is proposed in which four main categories that build spaces inside building is the principle element of evaluation. The Physical component in which selected as the variable of this research, based on Regulation of the Minister of Public Works of Indonesia no.24 in 2008 about building maintenance guidelines. Data are collected via questionnaires from experts to ranking and assign relative weights as model’s attribute using Confirmatory Factor Analysis (CFA) in Structural Equation Modeling (SEM) techniques. Multi attribute utility theory (MAUT) is used to calculate entire building condition based on rank and relative weight of selected components. This research model is applied to a case study dormitory of Universitas Gadjah Mada, located in Yogyakarta. Result of the research is condition of the entire building based on components that build spaces inside that building. This result of this research will assist owners and facility managers in select effective and efficient improvement and maintenance strategy for the building.

Gaussian Process Regression and Classification for Probabilistic Damage Assessment of Spatially Distributed Systems

We illustrate how methods for non-parametric regression and classification based on Gaussian Processes can be adapted for inferring the condition state of infrastructure components under spatially distributed stressors. When the stressor is modeled by a random field, observations collected in one location can reduce the uncertainty about the stressor intensity also in other locations. Exact inference is possible when the field is Gaussian and observations are perfect or affected by Gaussian noise. However, often the available observations are binary, as those related to the failure or survival of components, and indicate whether the local field is above or below a threshold whose value may also be uncertain. While no efficient scheme for exact inference is available in that setting, we can perform efficient approximate inference when the field is Gaussian and so is the uncertainty on the threshold value. The mathematical formulation for this problem is analogous to that of classification in machine learning, that can be based on latent Gaussian processes. We show how to formulate the problem and how to adapt deterministic methods, as Laplace’s method and Expectation Propagation, and methods based on random number generation, as Monte Carlo uniform sampling and importance sampling, to perform approximate inference. Our illustrative application is the condition assessment of assets exposed to a seismic event. Under specific assumptions, the seismic demand can be modeled as a Gaussian random field, and measures about the demand and about the survival and failure of assets can be processed globally, to update the risk assessment. Specifically, we evaluate methods for approximate inference and discuss their merits.

Smart Water Network Modeling for Sustainable and Resilient Infrastructure

Urbanization can significantly increase the load on aging, inefficient and already strained sewer infrastructure, resulting in catastrophic pipe failure, unwanted spillage, property damage, and serious threat to public health. Urbanization can also dramatically alter the natural water cycle, resulting in diminished water quality, increased frequency and severity of flooding, channel erosion and destruction of aquatic habitat. Recent advances in smart water network (SWN) modeling technology have played a crucial and growing role in addressing these challenges. SWN technology has equipped practicing engineers with a comprehensive set of analytical decision making tools designed to help them preserve structural integrity, manage and reduce the risks of sewer overflow and urban runoff, improve resiliency and keep their urban drainage networks operating well into the future. These advances propel routine conveyance system analysis from basic planning and design to two-dimensional surface/subsurface flow modeling, real-time operation and control, analytical risk-based asset integrity and condition assessment, and optimal selection and placement of green infrastructure based on cost and effectiveness. SWN is providing critically needed support to federal, state, and local agencies and watershed practitioners — not only in optimizing their integrated water management and adaptation strategies, but in ensuring sustainable drainage, addressing environmental quality restoration and protection needs in urban and developing areas, and improving communities’ resiliency. It is also within the grasp of utilities of all sizes, but they need to seize the opportunity.

Multi-class US traffic signs 3D recognition and localization via image-based point cloud model using color candidate extraction and texture-based recognition

Continuous condition monitoring and inspection of traffic signs are essential to ensure that safety and performance criteria are met. The use of 3D point cloud modeling by the construction industry has been significantly increased in recent years especially for recording the as-is conditions of facilities. The high-precision and dense 3D point clouds generated by photogrammetry can facilitate the process of asset condition assessment. This paper presents an automated computer-vision based method that detects, classifies, and localizes traffic signs via street-level image-based 3D point cloud models. The proposed pipeline integrates 3D object detection algorithm. An improved Structure-from-Motion (SfM) procedure is developed to create a 3D point cloud of roadway assets from the street level imagery. In order to assist with accurate 3D recognition and localization by color and texture features extraction, an automated process of point cloud cleaning and noise removal is proposed. Using camera pose information from SfM, the points within the bounding box of detected traffic signs are then projected into the cleaned point cloud by using the triangulation method (linear and non-linear) and the 3D points corresponding to the traffic sign in question are labeled and visualized in 3D. The proposed framework is validated using real-life data, which represent the most common types of traffic signs. The robustness of the proposed pipeline is evaluated by analyzing the accuracy in detection of traffic signs as well as the accuracy in localization in 3D point cloud model. The results promise to better and more accurate visualize the location of the traffic signs with respect to other roadway assets in 3D environment.

Hi-SPEQ – Developing the Technical and Quality Requirements for High-speed Condition Surveys of Road Networks

High speed surveys have become a key source of information to support condition assessment and management of pavement assets. These surveys can be applied network wide to obtain data on the surface condition and structural robustness of the pavement. The success of high speed surveys is demonstrated by the growth in the survey industry and the wide range of measurement equipment that has become available. However, these advances bring challenges to road administrations in determining the most appropriate survey to specify for their networks, in selecting the equipment, and in ensuring that the condition parameters delivered will be suitable to support asset management decisions. Although standards have been developed for some of the measurements provided by high speed systems, the focus tends to be highly technical, often not considering the wide ranging needs of routine network level implementation. The HiSPEQ project has the objective of developing guidance and advice to help road administrations to understand high speed road survey equipment, and to help them in specifying the survey requirements, quality regimes and processing procedures. The project has considered high speed survey data that can be used to assess pavement structural robustness. It has investigated the measurement of pavement shape, visual condition, deflection and structure. Via review of current specifications both within and outside the EU, and direct consultation with practitioners and administrations, the project has proposed a set of core requirements for high-speed surveys of pavement surface and structural condition. These requirements have been used to propose survey specification templates that road administrations could use when developing survey requirements for their own networks. Guidance has been developed to accompany the specifications, to assist road administrations understand the requirements and the implications of different levels of resolution and accuracy on the use of the data. The project has also proposed a set of quality assurance processes to consider when specifying condition surveys.

An ontology-supported asset information integrator system in infrastructure management

Purpose – The purpose of this paper is to develop and apply an ontology-supported asset information integrator system (AIIS) in the domain of infrastructure management. The two objectives are: first, to describe how different ontologies developed as part of this research support the design of message templates (MTs) that were implemented in the AIIS; and second, to explain the development and application of the prototype system for tangible capital asset (TCA) reporting. Design/methodology/approach – The proposed system was developed in the MS SharePoint platform using a four-step methodology: create a web site and library; review and modify MTs; design and configure workflows; and add functionalities. Findings – First, the architecture, methodology, and evaluation of the two ontologies: Transaction Domain Ontology and Tangible Capital Asset Ontology, developed as part of this research work were briefly introduced to describe how both the ontologies supported the design of MTs that were implemented in the AIIS. Second, the AIIS was successfully developed and applied in the domain of infrastructure management for the Asset Inventory and Condition Assessment Reporting. Practical implications – The development of the AIIS would enable industry experts to exchange the tangible capital information. The built-in search engine and history services would help the experts to search a transaction and track the transaction history. The real-time visualisation of the data would help in decision making. Originality/value – Infrastructure agencies use diversified information systems to manage infrastructure systems. Due to propriety nature of the information systems, the TCA data generated is heterogeneous and inconsistent, which make it difficult to exchange with other organisations. Also, the existing applications focus on processing and managing the TCA data for a variety of tasks; however, lack to support data exchange with other organisations. This emphasises the gap that requires the development of an ontology-supported collaboration system in the domain of infrastructure asset management.