Modeling and Monitoring of the Dynamic Response of Railroad Bridges using Wireless Smart Sensors

Railroad bridges form an integral part of railway infrastructure in the USA, carrying approximately 40 % of the ton-miles of freight. The US Department of Transportation (DOT) forecasts current rail tonnage to increase up to 88 % by 2035. Within the railway network, a bridge occurs every 1.4 miles of track, on average, making them critical elements. In an effort to accommodate safely the need for increased load carrying capacity, the Federal Railroad Association (FRA) announced a regulation in 2010 that the bridge owners must conduct and report annual inspection of all the bridges. The objective of this research is to develop appropriate modeling and monitoring techniques for railroad bridges toward understanding the dynamic responses under a moving train. To achieve the research objective, the following issues are considered specifically. For modeling, a simple, yet effective, model is developed to capture salient features of the bridge responses under a moving train. A new hybrid model is then proposed, which is a flexible and efficient tool for estimating bridge responses for arbitrary train configurations and speeds. For monitoring, measured field data is used to validate the performance of the numerical model. Further, interpretation of the proposed models showed that those models are efficient tools for predicting response of the bridge, such as fatigue and resonance. Finally, fundamental software, hardware, and algorithm components are developed for providing synchronized sensing for geographically distributed networks, as can be found in railroad bridges. The results of this research successfully demonstrate the potentials of using wirelessly measured data to perform model development and calibration that will lead to better understanding the dynamic responses of railroad bridges and to provide an effective tool for prediction of bridge response for arbitrary train configurations and speeds.National Science Foundation Grant No. CMS-0600433National Science Foundation Grant No. CMMI-0928886National Science Foundation Grant No. OISE-1107526National Science Foundation Grant No. CMMI- 0724172 (NEESR-SD)Federal Railroad Administration BAA 2010-1 projectOpe

A Bayesian Network methodology for railway risk, safety and decision support

For railways, risk analysis is carried out to identify hazardous situations and their consequences. Until recently, classical methods such as Fault Tree Analysis (FTA) and Event Tree Analysis (ETA) were applied in modelling the linear and logically deterministic aspects of railway risks, safety and reliability. However, it has been proven that modern railway systems are rather complex, involving multi-dependencies between system variables and uncertainties about these dependencies. For train derailment accidents, for instance, high train speed is a common cause of failure; slip and failure of brake applications are disjoint events; failure dependency exists between the train protection and warning system and driver errors; driver errors are time dependent and there is functional uncertainty in derailment conditions. Failing to incorporate these aspects of a complex system leads to wrong estimations of the risks and safety, and, consequently, to wrong management decisions. Furthermore, a complex railway system integrates various technologies and is operated in an environment where the behaviour and failure modes of the system are difficult to model using probabilistic techniques. Modelling and quantification of the railway risk and safety problems that involve dependencies and uncertainties such as mentioned above are complex tasks. Importance measures are useful in the ranking of components, which are significant with respect to the risk, safety and reliability of a railway system. The computation of importance measures using FTA has limitation for complex railways. ALARP (As Low as Reasonably Possible) risk acceptance criteria are widely accepted as ’\'best practice’’ in the railways. According to the ALARP approach, a tolerable region exists between the regions of intolerable and negligible risks. In the tolerable region, risk is undertaken only if a benefit is desired. In this case, one needs to have additional criteria to identify the socio-economic benefits of adopting a safety measure for railway facilities. The Life Quality Index (LQI) is a rational way of establishing a relation between the financial resources utilized to improve the safety of an engineering system and the potential fatalities that can be avoided by safety improvement. This thesis shows the application of the LQI approach to quantifying the social benefits of a number of safety management plans for a railway facility. We apply Bayesian Networks and influence diagrams, which are extensions of Bayesian Networks, to model and assess the life safety risks associated with railways. Bayesian Networks are directed acyclic probabilistic graphical models that handle the joint distribution of random variables in a compact and flexible way. In influence diagrams, problems of probabilistic inference and decision making – based on utility functions – can be combined and optimized, especially, for systems with many dependencies and uncertainties. The optimal decision, which maximizes the total benefits to society, is obtained. In this thesis, the application of Bayesian Networks to the railway industry is investigated for the purpose of improving modelling and the analysis of risk, safety and reliability in railways. One example application and two real world applications are presented to show the usefulness and suitability of the Bayesian Networks for the quantitative risk assessment and risk-based decision support in reference to railways.:ACKNOWLEDGEMENTS IV ABSTRACT VI ZUSAMMENFASSUNG VIII LIST OF FIGURES XIV LIST OF TABLES XVI CHAPTER 1: Introduction 1 1.1 Need to model and quantify the causes and consequences of hazards on railways 1 1.2 State-of-the art techniques in the railway 2 1.3 Goals and scope of work 4 1.4 Existing work 6 1.5 Outline of the thesis 7 CHAPTER 2: Methods for safety and risk analysis 10 2.1 Introduction 10 2.1.1 Simplified risk analysis 12 2.1.2 Standard risk analysis 12 2.1.3 Model-based risk analysis 12 2.2 Risk Matrix 14 2.2.1 Determine the possible consequences 14 2.2.2 Likelihood of occurrence 15 2.2.3 Risk scoring matrix 15 2.3 Failure Modes & Effect Analysis – FMEA 16 2.3.1 Example application of FMEA 17 2.4 Fault Tree Analysis – FTA 19 2.5 Reliability Block Diagram – RBD 22 2.6 Event Tree Analysis – ETA 24 2.7 Safety Risk Model – SRM 25 2.8 Markov Model – MM 27 2.9 Quantification of expected values 31 2.9.1 Bayesian Analysis – BA 35 2.9.2 Hazard Function – HF 39 2.9.3 Monte Carlo (MC) Simulation 42 2.10 Summary 46 CHAPTER 3: Introduction to Bayesian Networks 48 3.1 Terminology in Bayesian Networks 48 3.2 Construction of Bayesian Networks 49 3.3 Conditional independence in Bayesian Networks 51 3.4 Joint probability distribution in Bayesian Networks 52 3.5 Probabilistic Inference in Bayesian Networks 53 3.6 Probabilistic inference by enumeration 54 3.7 Probabilistic inference by variable elimination 55 3.8 Approximate inference for Bayesian Networks 57 3.9 Dynamic Bayesian Networks 58 3.10 Influence diagrams (IDs) 60 CHAPTER 4: Risk acceptance criteria and safety targets 62 4.1 Introduction 62 4.2 ALARP (As Low As Reasonably Possible) criteria 62 4.3 MEM (Minimum Endogenous Mortality) criterion 63 4.4 MGS (Mindestens Gleiche Sicherheit) criteria 64 4.5 Safety Integrity Levels (SILs) 65 4.6 Importance Measures (IMs) 66 4.7 Life Quality Index (LQI) 68 4.8 Summary 72 CHAPTER 5: Application of Bayesian Networks to complex railways: A study on derailment accidents 73 5.1 Introduction 73 5.2 Fault Tree Analysis for train derailment due to SPAD 74 5.2.1 Computation of importance measures using FTA 75 5.3 Event Tree Analysis (ETA) 78 5.4 Mapping Fault Tree and Event Tree based risk model to Bayesian Networks 79 5.4.1 Computation of importance measures using Bayesian Networks 81 5.5 Risk quantification 82 5.6 Advanced aspects of example application 83 5.6.1 Advanced aspect 1: Common cause failures 83 5.6.2 Advanced aspect 2: Disjoint events 84 5.6.3 Advanced aspect 3: Multistate system and components 84 5.6.4 Advanced aspect 4: Failure dependency 85 5.6.5 Advanced aspect 5: Time dependencies 85 5.6.6 Advanced aspect 6: Functional uncertainty and factual knowledge 85 5.6.7 Advanced aspect 7: Uncertainty in expert knowledge 86 5.6.8 Advanced aspect 8: Simplifications and dependencies in Event Tree Analysis 86 5.7 Implementation of the advanced aspects of the train derailment model using Bayesian Networks. 88 5.8 Results and discussions 92 5.9 Summary 93 CHAPTER 6: Bayesian Networks for risk-informed safety requirements for platform screen doors in railways 94 6.1 Introduction 94 6.2 Components of the risk-informed safety requirement process for Platform Screen Door system in a mega city 97 6.2.1 Define objective and methodology 97 6.2.2 Familiarization of system and information gathering 97 6.2.3 Hazard identification and hazard classification 97 6.2.4 Hazard scenario analysis 98 6.2.5 Probability of occurrence and failure data 99 6.2.6 Quantification of the risks 105 6.2.6.1. Tolerable risks 105 6.2.6.2. Risk exposure 105 6.2.6.3. Risk assessment 106 6.3 Summary 107 CHAPTER 7: Influence diagrams based decision support for railway level crossings 108 7.1 Introduction 108 7.2 Level crossing accidents in railways 109 7.3 A case study of railway level crossing 110 7.4 Characteristics of the railway level crossing under investigation 111 7.5 Life quality index applied to railway level crossing risk problem 115 7.6 Summary 119 CHAPTER 8: Conclusions and outlook 120 8.1 Summary and important contributions 120 8.2 Originality of the work 122 8.3 Outlook 122 BIBLIOGRAPHY 124 APPENDIX 1 13

Reliability Modeling and Improvement of Critical Infrastructures: Theory, Simulation, and Computational Methods

This dissertation presents a framework for developing data-driven tools to model and improve the performance of Interconnected Critical Infrastructures (ICIs) in multiple contexts. The importance of ICIs for daily human activities and the large volumes of data in continuous generation in modern industries grant relevance to research efforts in this direction. Chapter 2 focuses on the impact of disruptions in Multimodal Transportation Networks, which I explored from an application perspective. The outlined research directions propose exploring the combination of simulation for decision-making with data-driven optimization paradigms to create tools that may provide stakeholders with optimal policies for a wide array of scenarios and conditions. The flexibility of the developed simulation models, in combination with cutting-edge technologies, such as Deep Reinforcement Learning (DRL), sets the foundation for promising research efforts on the performance, analysis, and optimization of Inland Waterway Transportation Systems. Chapter 3 explores data-driven models for condition monitoring and prognostics, with a focus on using Deep Learning (DL) to predict the Remaining Useful Life of turbofan engines based on sequential sensor measurements. A myriad of approaches exist for this type of problems, and the main contribution for future efforts might be centered around combining this type of data-driven methods with simulation tools and computational methods in the context of network resilience optimization. Chapter 4 revolves around developing data-driven methods for estimating all-terminal reliability of networks with arbitrary structures and outlines research directions for data-driven surrogate models. Furthermore, the use of DRL for network design optimization and maximizing all-terminal network reliability is presented. This poses a promising research venue that has been extended to network reliability problems involving dynamic decision-making on allocating new resources, maintaining and/or improving the edges already in the network, or repairing failed edges due to aging. The outlined research presents various data-driven tools developed to collaborate in the context of modeling and improvement for Critical Infrastructures. Multiple research venues have been intertwined by combining various paradigms and methods to achieve this goal. The final product is a line of research focused on reliability estimation, design optimization, and prognostics and health management for ICIs, by combining computational methods and theory

Coordinated Transit Response Planning and Operations Support Tools for Mitigating Impacts of All-Hazard Emergency Events

This report summarizes current computer simulation capabilities and the availability of near-real-time data sources allowing for a novel approach of analyzing and determining optimized responses during disruptions of complex multi-agency transit system. The authors integrated a number of technologies and data sources to detect disruptive transit system performance issues, analyze the impact on overall system-wide performance, and statistically apply the likely traveler choices and responses. The analysis of unaffected transit resources and the provision of temporary resources are then analyzed and optimized to minimize overall impact of the initiating event

Robotic Crop Interaction in Agriculture for Soft Fruit Harvesting

Autonomous tree crop harvesting has been a seemingly attainable, but elusive, robotics goal for the past several decades. Limiting grower reliance on uncertain seasonal labour is an economic driver of this, but the ability of robotic systems to treat each plant individually also has environmental benefits, such as reduced emissions and fertiliser use. Over the same time period, effective grasping and manipulation (G&M) solutions to warehouse product handling, and more general robotic interaction, have been demonstrated. Despite research progress in general robotic interaction and harvesting of some specific crop types, a commercially successful robotic harvester has yet to be demonstrated. Most crop varieties, including soft-skinned fruit, have not yet been addressed. Soft fruit, such as plums, present problems for many of the techniques employed for their more robust relatives and require special focus when developing autonomous harvesters. Adapting existing robotics tools and techniques to new fruit types, including soft skinned varieties, is not well explored. This thesis aims to bridge that gap by examining the challenges of autonomous crop interaction for the harvesting of soft fruit. Aspects which are known to be challenging include mixed obstacle planning with both hard and soft obstacles present, poor outdoor sensing conditions, and the lack of proven picking motion strategies. Positioning an actuator for harvesting requires solving these problems and others specific to soft skinned fruit. Doing so effectively means addressing these in the sensing, planning and actuation areas of a robotic system. Such areas are also highly interdependent for grasping and manipulation tasks, so solutions need to be developed at the system level. In this thesis, soft robotics actuators, with simplifying assumptions about hard obstacle planes, are used to solve mixed obstacle planning. Persistent target tracking and filtering is used to overcome challenging object detection conditions, while multiple stages of object detection are applied to refine these initial position estimates. Several picking motions are developed and tested for plums, with varying degrees of effectiveness. These various techniques are integrated into a prototype system which is validated in lab testing and extensive field trials on a commercial plum crop. Key contributions of this thesis include I. The examination of grasping & manipulation tools, algorithms, techniques and challenges for harvesting soft skinned fruit II. Design, development and field-trial evaluation of a harvester prototype to validate these concepts in practice, with specific design studies of the gripper type, object detector architecture and picking motion for this III. Investigation of specific G&M module improvements including: o Application of the autocovariance least squares (ALS) method to noise covariance matrix estimation for visual servoing tasks, where both simulated and real experiments demonstrated a 30% improvement in state estimation error using this technique. o Theory and experimentation showing that a single range measurement is sufficient for disambiguating scene scale in monocular depth estimation for some datasets. o Preliminary investigations of stochastic object completion and sampling for grasping, active perception for visual servoing based harvesting, and multi-stage fruit localisation from RGB-Depth data. Several field trials were carried out with the plum harvesting prototype. Testing on an unmodified commercial plum crop, in all weather conditions, showed promising results with a harvest success rate of 42%. While a significant gap between prototype performance and commercial viability remains, the use of soft robotics with carefully chosen sensing and planning approaches allows for robust grasping & manipulation under challenging conditions, with both hard and soft obstacles

Indirect impact of landslide hazards on transportation infrastructure

This thesis examines the indirect impact of natural hazards on infrastructure networks. It addresses several key themes and issues for hazard assessment, network modelling and risk assessment using the case study of landslides impacting the national road network in Scotland, United Kingdom. The research follows four distinct stages. First, a landslide susceptibility model is developed using a database of landslide occurrences, spatial data sets and logistic regression. The model outputs indicate the terrain characteristics that are associated with increased landslide potential, including critical slope angles and south westerly aspects associated with increased rates of solar irradiance and precipitation. The results identify the hillslopes and road segments that are most prone to disruption by landslides and these indicate that 40 % (1,700 / 4,300 km) of Scotland s motorways and arterial roads (i.e. strategic road network) are susceptible to landslides and this is above previous assessments. Second, a novel user-equilibrium traffic model is developed using UK Census origin-destination tables. The traffic model calculates the additional travel time and cost (i.e. indirect impacts) caused by network disruptions due to landslide events. The model is applied to calculate the impact of historic scenarios and for sets of plausible landslide events generated using the landslide susceptibility model. Impact assessments for historic scenarios are 29 to 83 % greater than previous, including £1.2 million of indirect impacts over 15 days of disruption at the A83 Rest and Be Thankful landslide October 2007. The model results indicate that the average impact of landslides is £64 k per day of disruption, and up to £130 k per day on the most critical road segments in Scotland. In addition to identifying critical road segments with both high impact and high susceptibility to landslides, the study indicates that the impact of landslides is concentrated away from urban centres to the central and north-west regions of Scotland that are heavily reliant on road and haulage-based industries such as seasonal tourism, agriculture and craft distilling. The third research element is the development of landslide initiation thresholds using weather radar data. The thresholds classify the rainfall conditions that are most commonly associated with landslide occurrence in Scotland, improving knowledge of the physical initiation processes and their likelihood. The thresholds are developed using a novel optimal-point threshold selection technique, high resolution radar and new rain variables that provide spatio-temporally normalised thresholds. The thresholds highlight the role of the 12-day antecedent hydrological condition of soils as a precursory factor in controlling the rain conditions that trigger landslides. The new results also support the observation that landslides occur more frequently in the UK during the early autumn and winter seasons when sequences or clustering of multiple cyclonic-storm systems is common in periods lasting 5 to 15 days. Fourth, the three previous elements are combined to evaluate the landslide hazard of the strategic road segments and a prototype risk assessment model is produced - a catastrophe model. The catastrophe model calculates the annual average loss and aggregated exceedance probability of losses due to the indirect impact of landslides in Scotland. Beyond application to cost-benefit analyses for landslide mitigation efforts, the catastrophe model framework is applicable to the study of other natural hazards (e.g. flooding), combinations of hazards, and other infrastructure networks

HETEROGENEOUS UNCERTAINTY QUANTIFICATION FOR RELIABILITY-BASED DESIGN OPTIMIZATION

Uncertainty is inherent to real-world engineering systems, and reliability analysis aims at quantitatively measuring the probability that engineering systems successfully perform the intended functionalities under various sources of uncertainties. In this dissertation, heterogeneous uncertainties including input variation, data uncertainty, simulation model uncertainty, and time-dependent uncertainty have been taken into account in reliability analysis and reliability-based design optimization (RBDO). The input variation inherently exists in practical engineering system and can be characterized by statistical modeling methods. Data uncertainty occurs when surrogate models are constructed to replace the simulations or experiments based on a set of training data, while simulation model uncertainty is introduced when high-fidelity simulation models are built through idealizations and simplifications of real physical processes or systems. Time-dependent uncertainty is involved when considering system or component aging and deterioration. Ensuring a high level of system reliability is one of the critical targets for engineering design, and this dissertation studies effective reliability analysis and reliability-based design optimization (RBDO) techniques to address the challenges of heterogeneous uncertainties. First of all, a novel reliability analysis method is proposed to deal with input randomness and time-dependent uncertainty. An ensemble learning framework is designed by integrating the Long short-term memory (LSTM) and feedforward neural network. Time-series data is utilized to construct a surrogate model for capturing the time-dependent responses with respect to input variables and stochastic processes. Moreover, a RBDO framework with Kriging technique is presented to address the time-dependent uncertainty in design optimization. Limit state functions are transformed into time-independent domain by converting the stochastic processes and time parameter to random variables, and Kriging surrogate models are then built and enhanced by a design-driven adaptive sampling scheme to accurately identify potential instantaneous failure events. Secondly, an equivalent reliability index (ERI) method is proposed for handling both input variations and surrogate model uncertainty in RBDO. To account for the surrogate model uncertainty, a Gaussian mixture model is constructed based on Gaussian process model predictions. To propagate both input variations and surrogate model uncertainty into reliability analysis, the statistical moments of the GMM is utilized for calculating an equivalent reliability index. The sensitivity of ERI with respect to design variables is analytically derived to facilitate the surrogate model-based product design process, lead to reliable optimum solutions. Thirdly, different effective methods are developed to handle the simulation model uncertainty as well as the surrogate model uncertainty. An active resource allocation framework is proposed for accurate reliability analysis using both simulation and experimental data, where a two-phase updating strategy is developed for reducing the computational costs. The framework is further extended for RBDO problems, where multi-fidelity design algorithm is presented to ensure accurate optimum designs while minimizing the computational costs. To account for both the bias terms and unknown parameters in the simulation model, Bayesian inference method is adopted for building a validated surrogate model, and a Bayesian-based mixture modeling method is developed to ensure reliable system designs with the consideration of heterogeneous uncertainties

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201

The impact of human errors on the performance to failure of concrete bridges

Programa doutoral em Engenharia CivilO colapso de pontes que tiveram lugar em todo o mundo nos últimos 50 anos destacou o erro humano como a principal causa do colapso de pontes. Dadas as implicações financeiras, sociais e psicológicas de tais eventos indesejados, a contribuição do erro humano no colapso de pontes deve ser investigada com o objetivo de compreender como é que a robustez e a segurança estrutural das pontes são afetadas pelos mesmos. A deterioração das pontes, leva à redução das margens de segurança, expondo muitas vezes deficiências causadas por erros de projeto e construção, realçando a importância do desenvolvimento de procedimentos de avaliação estrutural mais abrangentes, tendo em conta numerosas fontes de incertezas. Apesar destes factos conhecidos existem poucos trabalhos disponíveis investigando questões tão relevantes. Neste sentido este trabalho aborda a identificação dos erros humanos em suas inúmeras formas, ou seja, erros de projeto e erros de construção, de acordo com opiniões de especialistas e eventos de colapso de pontes registados. Diferentes erros representam diferentes ameaças à segurança estrutural; como tal o risco relativo dos erros também é investigado. O real impacto dos erros humanos na segurança estrutural é investigado através de três pontes de betão armado, considerando a probabilidade de falha perante um conjunto de incertezas como principal indicador de desempenho. Tal investigação é realizada em duas etapas, uma onde os erros de projeto e construção são introduzidos em cenários onde se entende que eles estão presentes e outra onde a possibilidade de ocorrência de erros de construção é investigada considerando a probabilidade do erro humano e a magnitude do erro. Ocorrências únicas e múltiplas de erros também são discutidas. Modelos de elementos finitos, considerada para fins de análise estrutural não linear, e modelos substitutos são introduzidos como a base das múltiplas análises de fiabilidade estrutural realizadas. Finalmente, a previsão da vida útil de pontes considerando a corrosão induzida por carbonatação e a redução da vida útil das pontes causada por erros de construção são questões também abordadas.The collapse of bridges that have taken place worldwide in the last 50 years has highlighted human error as the main cause of the collapse of bridges. Given the financial, social and phycological implications of such hazardous events, human errors' contribution to the collapse of bridges must be investigated, aiming to understand how their robustness and structural safety are affected. The ageing of bridges leads to safety margin reductions that often expose deficiencies caused by design and construction errors, underling the importance of developing more comprehensive frameworks that consider numerous sources of uncertainty for structural safety assessment purposes. Despite these facts and known needs, few works facing such relevant concerns are available. Accordingly, human errors are identified in their numerous forms, i.e., design errors and construction errors, according to expert opinions and real-world bridge collapse events. Different errors represent different threats to structural safety; thus, their relative risk is also investigated. The actual impact of human errors on structural safety is investigated through one reinforced and two prestressed concrete bridges, using their probability of failure, given a group of uncertainties, as the main performance indicator. Such investigation is performed on two fronts, one where design and construction errors are introduced under scenarios where they are understood to be present, and another where the possibility of occurrence of construction errors is investigated considering probabilistic models to describe human error probabilities and error magnitudes. Single and multiple occurrences of errors are also discussed. Finite element modelling, considered for non-linear structural analysis purposes, and surrogate models are introduced as the backbone of the multiple structural reliability analysis performed. Finally, the service life prediction of bridges considering carbonation-induced corrosion and the service life reduction of bridges due to construction errors are carefully addressed.This work was partially financed by (i) national funds through FCT - Foundation for Science and Technology, under grant agreement “PD/ BD/143003/2018” attributed to the PhD Candidate through the iRail Doctoral program; and (ii) FCT/MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under reference UIDB/04029/2020