Assessment and forecast of the culvert’s performance within a road infrastructure management system. Literature review

During the 21st century, within road infrastructure management there is a strong enforcement on preserving assets and prevent roadway collapses. As a result, public agencies have to implement periodic inspections and asset condition assessments. As pavements and bridges also culverts management play a special role in roadway safety, because they prevent roadbed erosion. The scope of this investigation is the assessment and forecast of culverts performance regarding rating condition and network reliability forecast. In addition, it intends to analyze hazards influence in the culvert serviceability, modelling the hazards actions on the infrastructure. In this paper, is performed the literature review of studies done during the past decade comparing advantages and limitations. Five main subjects are identified in the development of a culvert management system, since the inventory and inspection framework, to forecasting models and risk assessment. Moreover, it will determine the correlation between subjects and will find gaps for improvement.(undefined

Railway Research

This book focuses on selected research problems of contemporary railways. The first chapter is devoted to the prediction of railways development in the nearest future. The second chapter discusses safety and security problems in general, precisely from the system point of view. In the third chapter, both the general approach and a particular case study of a critical incident with regard to railway safety are presented. In the fourth chapter, the question of railway infrastructure studies is presented, which is devoted to track superstructure. In the fifth chapter, the modern system for the technical condition monitoring of railway tracks is discussed. The compact on-board sensing device is presented. The last chapter focuses on modeling railway vehicle dynamics using numerical simulation, where the dynamical models are exploited

Analysis of selected acceleration signals measurements obtained during supervised service conditions – study of hitherto approach

The subject matter of the paper is an analysis of chosen results of acceleration signals measurements obtained from the prototype of the Rail Vehicle and Rail Track Monitoring System. This prototype of the monitoring system measures acceleration signals on designated elements of the electric multiple unit (EMU). These elements comprise components such as: bogie frames, wheels and bodies of railway vehicles. The analysis was prepared on the basis of rail vehicle journeys on sample sections of the Polish National Railways (PKP Polskie Linie Kolejowe S.A.) network. The products of measurements were converted to values of specific diagnostic parameters (statistical parameters), e.g. an amplitude (zero-peak), a root mean square, a kurtosis coefficient, an interquartile range. Comparing the values of diagnostic parameters with their permissible values allows the monitoring of distinctive dynamic behaviors of rail vehicles and track condition, as well as the temperature of the bearings of rail vehicle wheelsets. It also allows the determining of the condition of rail vehicle structure. The permissible values of certain diagnostics parameters could not be obtained from the literature. Therefore, this paper in part presents a way of obtaining these permissible values. The main intention of the analysis described here is to determine the usability of various diagnostic parameters and to identify the course of further research related to condition monitoring and diagnostics of rail vehicles and tracks

Mathematical modelling as an element of planning rail transport strategies

Effective planning and optimization of rail transport operations depends on effective and reliable forecasting of demand. The results of transport performance forecasts usually differ from measured values because the mathematical models used are inadequate. In response to this applicative need, we report the results of a study whose goal was to develop, on the basis of historical data, an effective mathematical model of rail passenger transport performance that would allow to make reliable forecasts of future demand for this service. Several models dedicated to this type of empirical data were proposed and selection criteria were established. The models used in the study are: the seasonal naive model, the Exponential Smoothing (ETS) model, the exponential smoothing state space model with Box–Cox transformation, ARMA errors, trigonometric trend and seasonal components (TBATS) model, and the AutoRegressive Integrated Moving Average (ARIMA) model. The proposed time series identification and forecasting methods are dedicated to the processing of time series data with trend and seasonality. Then, the best model was identified and its accuracy and effectiveness were assessed. It was noticed that investigated time series is characterized by strong seasonality and an upward trend. This information is important for planning a development strategy for rail passenger transport, because it shows that additional investments and engagement in the development of both transport infrastructure and superstructure are required to meet the existing demand. Finally, a forecast of transport performance in sequential periods of time was presented. Such forecast may significantly improve the system of scheduling train journeys and determining the level of demand for rolling stock depending on the season and the annual rise in passenger numbers, increasing the effectiveness of management of rail transport

Intelligent Systems Supporting the Use of Energy Systems and Other Complex Technical Objects, Modeling, Testing and Analysis of Their Reliability in the Operation Process

The book focuses on a novel application of Intelligent Systems for supporting the operation and maintenance of power systems or other technical facilities within wind farms. Indicating a different perception of the reliability of wind farm facilities led to the possibility of extending the operation lifetime and operational readiness of wind farm equipment. Additionally, the presented approach provides a basis for extending its application to the testing and analysis of other technical facilities

Reliability Improvement On Feasibility Study For Selection Of Infrastructure Projects Using Data Mining And Machine Learning

With the progressive development of infrastructure construction, conventional analytical methods such as correlation index, quantifying factors, and peer review are no longer satisfactory in support for decision-making of implementing an infrastructure project in the age of big data. This study proposes using a mathematical model named Fuzzy-Neural Comprehensive Evaluation Model (FNCEM) to improve the reliability of the feasibility study of infrastructure projects by using data mining and machine learning. Specifically, the data collection on time-series data, including traffic videos (278 Gigabytes) and historical weather data, uses transportation cameras and online searching, respectively. Meanwhile, the researcher sent out a questionnaire for the collection of the public opinions upon the influencing factors that an infrastructure project may have. Then, this model implements the backpropagation Artificial Neural Network (BP-ANN) algorithm to simulate traffic flows and generate outputs as partial quantitative references for evaluation. The traffic simulation outputs used as partial inputs to the Analytic Hierarchy Process (AHP) based Fuzzy logic module of the system for the determination of the minimum traffic flows that a construction scheme in corresponding feasibility study should meet. This study bases on a real scenario of constructing a railway-crossing facility in a college town. The research results indicated that BP-ANN was well applied to simulate 15-minute small-scale pedestrian and vehicle flow with minimum overall logarithmic mean squared errors (Log-MSE) of 3.80 and 5.09, respectively. Also, AHP-based Fuzzy evaluation significantly decreased the evaluation subjectivity of selecting construction schemes by 62.5%. It concluded that the FNCEM model has strong potentials of enriching the methodology of conducting a feasibility study of the infrastructure project

Probabilistic and Fuzzy Approaches for Estimating the Life Cycle Costs of Buildings

The Life cycle cost (LCC) method makes it possible for the whole life performance of buildings and other structures to be optimized. The introduction of the idea of thinking in terms of a building life cycle resulted in the need to use appropriate tools and techniques for assessing and analyzing costs throughout the life cycle of the building. Traditionally, estimates of LCC have been calculated based on historical analysis of data and have used deterministic models. The concepts of probability theory can also be applied to life cycle costing, treating the costs and timings as a stochastic process. If any subjectivity is introduced into the estimates, then the uncertainty cannot be handled using the probability theory alone. The theory of fuzzy sets is a valuable tool for handling such uncertainties. In this Special Issue, a collection of 11 contributions provide an updated overview of the approaches for estimating the life cycle cost of buildings

Modern Information Systems

The development of modern information systems is a demanding task. New technologies and tools are designed, implemented and presented in the market on a daily bases. User needs change dramatically fast and the IT industry copes to reach the level of efficiency and adaptability for its systems in order to be competitive and up-to-date. Thus, the realization of modern information systems with great characteristics and functionalities implemented for specific areas of interest is a fact of our modern and demanding digital society and this is the main scope of this book. Therefore, this book aims to present a number of innovative and recently developed information systems. It is titled "Modern Information Systems" and includes 8 chapters. This book may assist researchers on studying the innovative functions of modern systems in various areas like health, telematics, knowledge management, etc. It can also assist young students in capturing the new research tendencies of the information systems' development

A Systematic Literature Review of Drone Utility in Railway Condition Monitoring

Raj Bridgelall is the program director for the Upper Great Plains Transportation Institute (UGPTI) Center for Surface Mobility Applications & Real-time Simulation environments (SMARTSeSM).Drones have recently become a new tool in railway inspection and monitoring (RIM) worldwide, but there is still a lack of information about the specific benefits and costs. This study conducts a systematic literature review (SLR) of the applications, opportunities, and challenges of using drones for RIM. The SLR technique yielded 47 articles filtered from 7,900 publications from 2014 to 2022. The SLR found that key motivations for using drones in RIM are to reduce costs, improve safety, save time, improve mobility, increase flexibility, and enhance reliability. Nearly all the applications fit into the categories of defect identification, situation assessment, rail network mapping, infrastructure asset monitoring, track condition monitoring, and obstruction detection. The authors assessed the open technical, safety, and regulatory challenges. The authors also contributed a cost analysis framework, identified factors that affect drone performance in RIM, and offered implications for new theories, management, and impacts to society.The authors conducted this work with support from North Dakota State University and the Mountain-Plains Consortium, a University Transportation Center funded by the U.S. Department of Transportation.https://www.ugpti.org/about/staff/viewbio.php?id=7

MULTIGRAPH IS: Part 1. A FORMAL DESCRIPTION OF RAILWAY INFRASTRUCTURE FOR THE DIGITAL TWIN OF THE ETCS APPLICATION

The European Railway Agency has formulated assumptions for a target model of rail transport. Its important premise is digitalization to support the communication and transport services that the railways will make available to the public in the future. Part of the digitalization process is the digital description of the railway infrastructure in a formalized form to allow algorithmic processing. The formal description of infrastructure is not a new issue. However, attempts made so far have not resulted in a permanent definition of a generally accessible formalism allowing for a coherent representation of the physical railway infrastructure in a digital form. This paper presents the results of work carried out within the research project Digital Railway-The Digital Twin of the ETCS Application-Virtual Prototyping and Simulation of Operational Scenarios