This study contributes to the literature by exploring, from a multidisciplinary viewpoint, the emerging discipline of asset management, and by providing a better understanding of the motives for and ...barriers to asset-intensive organisations' adoption of asset management systems (AMS). To this end, the authors conceptualise and explore the core themes of AMS, and link this novel body of knowledge to organisational motives for and barriers to the formal introduction of AMS. The study collects and analyses empirical data from 93 middle/senior managers in Slovenian organisations dealing with engineered assets. The results, obtained by applying partial least squares path modelling, show that internal motives, such as better control of operational processes and continuous improvement, are the main drivers of the introduction of AMS. The potential increase in documentation and bureaucracy, and lack of resources, were identified as the main barriers to AMS implementation. By assessing the factors that facilitate and inhibit formal AMS implementation, the study provides important insights into developing strategies to promote the novel and important discipline of asset management.
AbstractThis study explores the performance regime of different classification algorithms as they are applied to the analysis of asphalt pavement deterioration data. The aim is to examine how ...different algorithms deal with the typically limited and low-quality data sets in the infrastructure asset management domain, and whether better configurations of relevant algorithms help overcome these limitations. Furthermore, the emphasis on choosing the most affordable attributes (e.g., temperature and precipitation levels) makes the results reproducible to smaller municipalities. This analysis used the data of more than 3,000 examples of road sections, which were retrieved from the Long-Term Pavement Performance (LTPP) database. The algorithms examined in this study include two types of decision trees, naïve Bayes classifier, naïve Bayes coupled with kernels, logistic regression, k-nearest neighbors (k-NN), random forest, and gradient boosted trees. The performance of these algorithms is compared, and their weaknesses and strengths are discussed. They were all applied to predict the deterioration of pavement condition index (PCI). Of specific importance is the positive role of ensemble learning. It is shown how using higher efficiencies by using ensemble learning can compensate for data shortcomings. The accuracy of some of the models in predicting the PCI after 3 years exceeded 90%. Suggestions are made to improve the performance of some algorithms. For instance, the naïve Bayes classifier was coupled with kernel estimates to achieve a better accuracy. It is demonstrated that using kernel estimates can increase the accuracy of the naïve Bayes classifier dramatically. Further, the study examines the impact of data segmentation. Data were divided into four different climatic regions. The accuracy of prediction was sufficiently high after segmentation, with the highest accuracy in the dry and nonfreeze zone and the lowest performance in the region with a wet and freezing climate.
The purpose of this research is to describe and analyze the implementation of asset management policies in the market village of Sidorejo Village, Pagelaran District, Malang Regency based on Sidorejo ...Village Regulation Number 01 of 2009 and to analyze the factors that support and hinder the implementation of asset management policies in the market village of Sidorejo Village, Pagelaran Village, Malang Regency based on the Regulation Sidorejo Village Number 01 of 2009 concerning Village Market Management. The method used in this research is descriptive qualitative. The results of this study can be concluded that the implementation of Village Asset Management Policies at the Sidorejo Village Market, Pagelaran District, Malang Regency Based on the Sidorejo Village Regulation Number 01 of 2009 concerning Village Market Management in general can be said to be successful with the condition that there is a real impact from the policy output, namely the Village Head Regulation of Sidorejo Village Number. 4 of 2009.
•Development of an operational lead asset classification system.•Implementation of a hierarchical asset classification system imbedded within an BIM model.•methodology to support the design, ...development and implementation of an AIM.•Transfer of BIM related data directly from the model into a relational database.
Building Information Modelling (BIM) is one of the most significant technological advancements in recent years that has been adopted by the design and construction industry. While BIM adoption is growing, it can be witnessed that adoption is relatively weak within operational and maintenance (O&M) organisations such as Estate and Infrastructure Management, who would ultimately gain the highest value from utilising BIM. While the challenges of BIM adoption are multifaceted, there is a recurring theme of poor data integration between BIM and existing information management systems. There is a clear gap of knowledge on how to structure a BIM model that allows its efficient use in the O&M phase. Furthermore, there is a lack of claritiy on how to exchange information from a BIM model into an Asset Information Model (AIM).
This paper outlines a methodology that enables extraction of BIM-related data directly from a model into a relational database for integration with existing asset management systems. The paper describes the BIM model requirements, development of the extraction platform, database architecture and framework. Furthermore, a case study is presented to demonstrate the methodology. The case study demonstrates that if the BIM model is designed from the start with consideration for the O&M requirements, it can be exploited for development into an AIM. It also shows that a structured approach to object classification within a BIM model supports the efficient exchange of data directly from the BIM model.
•We sample data of mutual funds investing in European stocks.•We build the network basing on the correlation matrix.•We calculate centrality measures.•We introduce different measures of herding.•We ...detect the role of the main centrality measures for herding.
The paper investigates herding in mutual funds through a complex networks approach. The detection of significant correlation coefficients constitutes the basis for the construction of the network. Some centrality measures and the assortativity are added as explanatory variables in the regression analysis of two popular indicators of herding, largely applied in finance literature. Cross-Sectional Standard Deviation and Cross-Sectional Absolute Deviation are both considered since they emphasize the bulk and the extreme values of herding. Two dummy variables designed to capture differences in investor behaviour in extreme up or down versus relatively normal markets are considered as independent variables. The results show a clear decrease of herding in stressful periods of the market. Moreover, the prevailing explanatory power of the betweenness is well evidenced, thereby highlighting the role of the network structure. In line with the literature on herding, the results also evidence a flight to safety effect.
AbstractThe massive number of infrastructure intervention activities occurring in cities leads to detrimental social, environmental, and economic impacts on the community. Thus, integrating the asset ...intervention activities is required to minimize the community disruption and maintain an acceptable level of service throughout the assets’ lifecycle. This paper presents an integrated multiobjective asset management system for the road and water infrastructure that enables asset managers to trade off intervention alternatives and compare the outcomes of both conventional and integrated asset management systems. The multiobjective framework considers (1) physical state, (2) lifecycle costs, (3) user costs, and (4) replacement value. It revolves through three core models: (1) a database model that contains detailed asset inventory for the road and water networks; (2) key performance indicator (KPI) computational models for assessing the impact of the intervention plan on the predefined set of KPIs; and (3) an optimization model that relies on a combination of metaheuristics, dynamic programming, and goal optimization to schedule the intervention activities throughout the planning horizon. The system is applied to road and water networks in Kelowna, British Columbia, and the results show 33 and 50% savings in lifecycle costs and user costs, respectively. Moreover, it shows the potential ability to scale the framework to include other infrastructure such as sewer, electricity, gas, and telecom, provided that the information can be shared among these entities.
The time has come for renewed emphasis on the life cycle management of the physical aspects of transportation infrastructure. The urgency for this new direction is underscored by the fact that the ...physical transportation network at most countries constitutes the most valuable publicly owned infrastructure and efforts must be made to keep it resilient to possible threats of man-made or natural disasters over its service life so that the movement of people and goods can continue uninterrupted to serve the economy and maintain the quality of life. The concept of transportation asset management (TAM) is a systematic process based on multiple disciplines (engineering, finance, operations research and economics), to make cost-effective repair and replacement decisions geared towards a sustained state of good repair over the infrastructure life cycle. This paper first argues for the continued application of asset management principles for transportation infrastructure in the new millennium. The paper then discusses the development cycle of transportation infrastructure as a prelude to a discussion of the key functions of an asset management system. The paper proceeds to identify the components (asset types) and elements of asset management (that is, the tasks that are carried out by an asset manager in an agency). The challenges and opportunities of asset management in the new millennium are then discussed. These include, among others, the specter of climate change, infrastructure resilience, sustainable development of transportations assets, the emerging era of autonomous vehicles and smart cities, and the consideration of transportation assets as a holistic system-of-systems. These issues are addressed in the context of the availability of big data and advances in analytical techniques and computing power.
Pavement asset management system (PAMS) assists agencies and decision makers to maintain deteriorating pavement assets with optimized budget allocation. The recent developments in pavement condition ...data collection and processing have significant effect on estimating remaining service life and selecting optimum maintenance strategies. Further, image processing (IP) and artificial intelligence (AI) tools have improved the overall performance of PAMS by helping analyze big data emanating from distress surveys. The objective of this review paper was to collect and report several current state-of-the-art developments in PAMS and the associated embedded processes, majorly focused on data collection procedures, analytical techniques, decision making tools, and processing methods. The shift from manual condition surveys to automated pavement condition surveys has profusely improved data collection rate. The wide-range of data collection methods, manual, automated vehicles, and cost-effective methods followed across the globe were reviewed. Further, the chronological development in data analysis, specifically, distress evaluation, homogeneous sectioning for selection of maintenance strategies, and prioritization and optimization of maintenance strategies were discussed while emphasizing the application of IP and AI in enhancing the efficacy of PAMS. In addition, this paper provided a narrative account of the interdisciplinary research and multi-scale developments that recognize the value-addition of cutting-edge technologies in AI and computer vision.
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•Documented chronological developments in Pavement Asset Management Systems•Reviewed pavement condition state-of-the-art data collection technologies•Collected image processing and machine learning applications in PAMS data analysis•Discussed utilization of various heuristic techniques in effective decision making•Collation of literature indicative of vast scope to use AI in automating PAMS
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