Dr Donya Hajializadeh
Academic and research departmentsDepartment of Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences, Surrey Institute for People-Centred Artificial Intelligence.
Dr Donya Hajializadeh, BEng (Hons), MEng, PhD, CEng MICE, MIEI, EUR ING, MWES, FHEA is a Chartered Engineer, European Engineer, and Senior Lecturer (Associate Professor) in Bridge/Structural Engineering at the University of Surrey, Department of Civil and Environmental Engineering (CEE).
Dr Hajializadeh is a Fellow of the Surrey Institute for People-Centred Artificial Intelligence (AI), CEE Director of Employability, IStructE - University of Surrey Liaison Officer along with other roles and responsibilities across the CEE, University of Surrey, UK and EU institutions and professional bodies.
Dr Hajializadeh was a recipient of the prestigious Marie Curie Early Stage Researcher Scholarship awarded by the European Commission for the duration of her PhD at the University College Dublin (UCD). Following completing her PhD, she spent 3 years in the industry as a Research Engineer at Roughan & O’Donovan Innovative Solutions (RODIS) and as a Lecturer (part-time) at UCD Civil Engineering School. Subsequently, she was appointed Senior Lecturer in Civil/Structural Engineering at Anglia Ruskin University for 3 years prior to joining the University of Surrey.
Donya is an active member of several professional bodies, including the Institution of Civil Engineers (ICE), the Institution of Structural Engineers (IStructE), the Institution of Engineers of Ireland (IEI), the European Federation of National Engineers Associations (FEANI) and the Women’s Engineering Society (WES).
She is a manuscript peer reviewer of ASCE Journal of Bridge Engineering, ICE Bridge Engineering Journal, Scientific Research Publishing (SCIRP) Open Journal of Civil Engineering, International Journal of Building Pathology and Adaptation (emeraldinsight).
Donya’s Research Interests with the industry-focused application:
- Structural health monitoring (SHM)
- Data-driven-based SHM
- Machine learning-based asset management systems
- Deep learning-based damage identification systems
- Bridge Weigh-in-Motion (WIM)
- Bridge traffic load modelling
- Advanced finite element analysis (FEA)
- Risk and reliability assessment of bridges
- Infrastructure interdependencies’ modelling
- Resilience, vulnerability and risk assessment of critical infrastructure
Dr Hajializadeh is keen to supervise and support outstanding candidates who wish to pursue their studies at the PhD level in any of the above research areas or related fields. To discuss PhD research topics, funding opportunities and the application process, please contact Donya directly by email: email@example.com
University roles and responsibilities
- Director of Employability - 2020 onwards
- Deputy Surrey / ICE Scholarship Coordinator - 2019 onwards
- IStructE - University of Surrey Liaison Officer - 2021 onwards
- Fellow of the Surrey Institute for People-Centred Artificial Intelligence (AI) - 2021 onwards
- Member of Athena Swan Self-Assessment Team - 2020 onwards
- Women in Property, Surrey Contact Point - 2019 onwards
- Member of the University Ethics Committee - 2019-20
- Level M Tutor for MSc and MEng students - 2019-20
Affiliations and memberships
- Academic Qualifications Panel
- Papers Awards Judging Panel
- Technical Publications Panel
IStructE - University of Surrey Liaison Officer since 2021
Committee Member of the IStructE Surrey Regional Group since 2019
Committee Member of the ICE East of England - Essex Branch (2016-2019)
Committee Member of the Engineers Ireland - GB Region
In the media
Donya’s Research Interests with the industry-focused application:
- Structural health monitoring (SHM)
- Data-driven-based SHM
- Machine learning-based asset management systems
- Deep learning-based damage identification systems
- Bridge Weigh-in-Motion (WIM)
- Bridge traffic load modelling
- Advanced finite element analysis (FEA)
- Risk and reliability assessment of bridges
- Infrastructure interdependencies’ modelling
- Resilience, vulnerability and risk assessment of critical infrastructure
Dr Hajializadeh is keen to supervise and support outstanding candidates who wish to pursue their studies at the PhD level in any of the above research areas or related fields. To discuss PhD research topics, funding opportunities and application process, please contact Donya directly by email: firstname.lastname@example.org
Principal Investigator (PI) - Developing a measure of network resilience in response to hazardous events, in addition to vulnerability measure. This measure provides a quantitative metric of the robustness, recoverability, rapidity and resourcefulness of the infrastructure network in response to environmental hazards. This project was fully funded by the UK Natural Environment Research Council (NERC) in collaboration with UK industry partners: Transport Scotland, Scottish Water, SSE and Atkins.
The frequency and magnitude of environmental hazards in Scotland and across the UK in general, have increased significantly in the last decade, resulting in widespread failures in Critical Infrastructure (CI) networks. Breakdowns of interdependent and integrated CIs disrupt essential services and result in severe consequences for asset owners and managers, users and the wider community. The common challenge currently faced by asset owners and managers is the lack of a robust resilience-informed business planning and management strategies in response to interdependent assets failures, in particular due to low-probability/ high-impact environmental hazards.
Interdependencies among CIs can cause cascading failures and hence amplify negative consequences due to these failures. This can also affect CIs service restoration rate and consequently reducing their resilience in coping with these hazardous environmental events. As infrastructures are becoming more interdependent at some sectors, there is an increasing need for a better management of these interactions and interdependencies.
To reduce these impacts, an integrated Resilience and Vulnerability-based Decision Support System (RV-DSS) is developed to map interdependent network vulnerable components and to introduce adaptive capacities accordingly.
This is of particular importance as CIs owners and managers are investing substantially on improving the resilience of their assets in response to extreme environmental hazards.
RV-DSS was adopted in a case study of North Argyll, Scotland. The interdependencies in transport, energy and water networks and the failure propagation due to these interdependencies was assessed.
This was the very first time that three infrastructure networks interdependencies have been quantitatively mapped to identify the capabilities and limitations of the interdependent CI systems in withstanding systematic and unpredicted failures.
Principal Investigator (PI) - Developing a decision-making system focused on resilience assessment of interdependent infrastructure networks to study failure propagation in such systems.
Co-Principal Investigator (Co-PI) - Developing a guideline on resilience metrics for infrastructure networks as a function of the network configuration for a variety of infrastructure networks (e.g. Transport or Energy Network).
Technical Co-Lead - Developed and validated a 3D FEM representing the static and dynamic responses of the Boyne Viaduct railway bridge under train load. The aim of the project is to develop solutions for identifying, analysing and remediating critical rail infrastructure. These solutions will be implemented using a decision support tool, which will allow rail infrastructure managers to make rational investment choices based on reliable data.
The aim of the project was to develop solutions for identifying, analysing and remediating critical rail infrastructure. These solutions would be implemented using a decision support tool, which will allow rail infrastructure managers to make rational investment choices based on reliable data.
RODIS, as a partner, was leading the work stream focused on predicting the performance of railway bridges on the basis of information received from both actual and virtual SHM installations, feeding the information to the decision support tool and Life Cycle Analysis. Having this capability will enable Owners/Managers to optimise budgets/resources from the perspectives of minimisation of cost for maximised service life performance. The developed methodologies will also facilitate statistical updating of predicted performance on the basis of information received from both actual and virtual SHM installations and therefore feed to the decision support tool and Life Cycle Analysis.
Design Engineer - Responsible for carrying out ultimate limit state and serviceability limit state checks of piles and piers for a 180-metre beam-and-slab reinforced concrete bridge.
Roughan & O’Donovan acted as an independent design checker on this ambitious project to upgrade the core of Scotland’s motorway network between Glasgow and Edinburgh and was responsible for the detailed check of all disciplines from the stabilisation of historic mine workings through to ITS.
Key elements included: 16km of improvements to the existing motorway network. 12km of new build motorway. Upgrades to 12 existing motorway junctions. New build or modifications of 5 bridges over operational railways. The 2,000 tonnes Braehead Railway Bridge. Several 3 and 4 span steel composite bridges. 70m clear span crossing of the environmentally designated North Calder Water. Extensive mineworkings, subsurface voids and remediation measures. Extensive earthworks including steepened earthworks, soft ground, made ground and contaminated land. Pavement design and assessment. ITS including lane, speed and variable message signals, vehicle detection, CCTV, journey time and Weigh-in-Motion (WIM) monitoring. 5km of new pedestrian and cycle routes.
Design Engineer - Responsible for assessing the possibility of reducing section sizes in the upper sections of the bridge pylon, as part of a value engineering exercise during New Wear Crossing project (Northern Spire Bridge, Sunderland). As a result of detailed analysis, the section sizes were reduced by 16%.
Northern Spire Bridge is 340m long and comprises a 24m-wide deck, supported by a 1,550 tonne, 105m-high A-frame pylon. It carries two lanes of traffic in either direction, with dedicated cycle-ways and footpaths along the full length of the crossing. The bridge stands apart from traditional cable-stayed solutions in that its central pylon was fabricated in one piece off site and transported by sea to the bridge site, and its deck was assembled in two sections alongside the bridge and launched in two separate phases across the river. These innovations provided substantial benefits, in terms of construction and whole-life maintenance costs, to the client, Sunderland City Council.
The decision to pre-fabricate the pylon in a single piece off site while its foundations, which consist of bored cast-in-place composite piles and pilecap, were constructed onsite was ground-breaking. It reduced the programme of on-site works by nine months when compared with the traditional form of pylon construction, which follows a linear programme of piles, pilecap and segmental pylon construction. It also resulted in better quality control, reduced the risk of weather disruption and improved safety.
Assembling the bridge deck in two sections and launching it in two separate phases also produced significant cost savings. 300m of the 340m long bridge deck was launched across the river on temporary supports and associated foundations while the remaining portion - the approach span - was lifted into position by crane afterwards. The deck steelwork (2000t) was prefabricated off-site and delivered in transportable sections for assembly by bolting/ welding. A launching nose and tail frame was installed and the first 172m section of the bridge was launched into position using strand jacks to pull the rear of the assembly forward and low friction rollers to support the bottom flange of the girders.
The 105m high, 1550t steel/concrete pylon was raised into the vertical position using cables. Temporary hinges were provided on top of the concrete ‘tusks’ at the base of the pylon to facilitate the lift. A temporary back mast was fitted to the pylon and hoisted vertically from a barge using 4 number 450t capacity strand jacks. Once the pylon had been raised into position, the second section of the bridge was then launched. The bridge opened to the public in August 2018, and it is already an established landmark in the surrounding area.
Design Engineer - Developed a 3D FEM of the lighthouse subjected to wave loads for a cylindrical structure. Conducted the stability checks, including sliding, overturning and bearing capacity checks.
Technical Lead - The aim of this project was to develop weight limits for HGV’s which were non-regulated under the Irish Law. The focus was on the impact of the specified vehicle configurations on bridge structures and pavements.
Using an Irish Weigh-in-Motion (WIM) database collected in 2014, this recently completed project for the Irish National Road Authority (NRA) was performed to assess the effects of amending the Road Traffic (Construction and Use of Vehicles) Regulations (S.I 5/2003, as amended) in order to include Gross Vehicle Weight (GVW) limit regulations for a number of specific vehicle configurations on general road bridges, masonry arch bridges and pavements. The general road bridge assessment was performed by carrying out statistical extrapolation for characteristic 1000-year load effects for 143 typical bridge configurations. The vehicle types considered included;
- 5-axle rigid vehicle
- 4-axle trucks drawing 2-axle trailers
- 3-axle trucks drawing 3-axle trailers.
For the 5-axle rigid vehicle, RODIS were required to derive a GVW limit as well as a specific configuration as these were not available in the WIM data. For the 4-axle trucks drawing 2-axle trailers the effect of moving from an allowable GVW of 42 tonnes to 46 tonnes was assessed. For the 3-axle trucks drawing 3-axle trailers, the impact of increasing the allowable GVW from 44 tonnes to 46 tonnes was assessed.
Work Package Technical Lead - Responsible for the application of a risk assessment framework and benchmarking against two case studies.
The aim of the RAIN project was to identify critical infrastructure impacted by extreme weather events and minimise the impact of these events on the EU transport, energy and telecommunication infrastructure networks. This is being achieved through the development of early warning systems, decision support tools and engineering solutions to ensure rapid reinstatement of the network. This will improve the reliability of critical infrastructures and reduce disruption for citizens.
Within the project, RODIS was developing a quantitative risk assessment framework, based on a Bayesian approach and benchmarking the method against two case studies, which were based on actual flooding events that occurred in Italy and Finland respectively. This framework was being used to assess the benefits of providing resilient infrastructure from an economic, societal and security point of view and considers the effects of climate change and the positive/negative impacts of implementing mitigation measures. Key factors influencing the risk such as; population, GDP, Education level, warning systems in place and maintenance plans, along with consequences such as; travel cost, cost of repairs and/or replacement of infrastructure and costs of labour, are all considered in the framework. The influence of climate change on the risks has been considered in terms of the intensity and likelihood of extreme weather events, with thresholds having been defined for the considered extreme weather event types. The unique aspect of the framework lies in its ability to quantify the dependencies and interdependencies involved in cause-effect-chains.
Technical Co-Lead - Responsible for reliability assessment of bridge with and without WIM data considering growth in traffic. The primary objective of Re-Gen was to provide Road Owners/Managers with best practice tools and methodologies for risk assessment of critical infrastructure elements such as bridges, retaining structures and steep embankments. The Re-Gen project seeks to adopt a network-wide probabilistic risk-based approach to optimise lifecycle performance of the infrastructure, within the context of evolving traffic demands and climate change effects. The proposed framework will consider the different types of risk faced by national road administrations such as safety risk, financial risk, operational risk, commercial risk and reputational risk.
In order to develop the set of risk management and decision tools, work streams were focusing on the areas of; Prediction of Deterioration Considering Climate change; Traffic Effect Forecasting and Risk profiling (i.e. investigating the probabilities of road infrastructure failure and the consequences). Due to significant expertise and knowledge of Weigh-in-Motion (WIM) and load modelling within RODIS, in addition to acting a project coordinator, RODIS was responsible for the activity related to Traffic Growth Forecasting. As part of this work, RODIS provided a comprehensive overview of existing guidelines for collecting WIM data, recommended a procedure for quality assurance of WIM data and for cleaning of erroneous data and provided examples of good practice of using WIM data for bridge applications.
Subsequently, RODIS assessed the implications of Traffic Growth on the Eurocode alpha-factors used in Site-Specific Bridge Assessment. These alpha factors are used to scale down the notional loads from the Eurocode load model for new bridges to undertake assessments of existing bridges. Using complex statistical processes and sophisticated analysis, long run simulations were carried out for a range of load effects, spans and rates of growth to determine the extent to which growth in traffic influences the alpha-factor.
It is important to note that for design or routine assessment of small bridges, a load model such as the Eurocode LM1, sometimes scaled using alpha-factors, is generally sufficient. However, for larger or strategically located bridges, a Reliability Analysis may be required to obtain a more accurate measure of its true safety to account for uncertainties inherent in, for example, material properties, workmanship, element dimensions, traffic load, environmental conditions and the deterioration process. Consequently, the effects of traffic growth were then included in a probabilistic framework, which considered reliability, from which we provided guidelines for probabilistic modelling of resistance and load modelling, considering traffic growth.
Re-Gen was funded through the CEDR Transnational Road Research Programme Call 2013 “Ageing Infrastructure” with funding provided by the road administration of Denmark, Germany, Ireland, Netherlands, Slovenia and the UK. The programme is being managed by the NRA, Ireland, on behalf of CEDR.
Technical Co-Lead - Responsible for bridge assessment under the long run traffic simulation.
In Ireland, vehicles which do not comply with the weight or dimension limits specified in the regulations must apply for a permit before they are legally allowed to travel on the roads. As part of the processing for issuing these permits, it is important that due consideration is given to the bridges which they will traverse on their journey. Commissioned by the Irish National Roads Authority (NRA), RODIS has developed a software tool which can be used to enable local authorities and law enforcement agencies to administer and manage the issuing of these permits on the Irish Major Inter-Urban (MIU) Network. The software has been developed to be user-friendly so that it can be used by technically competent engineering staff of the local authority that do not necessarily possess any specialist knowledge of bridge analysis algorithms and optimisation procedures.
The aim of this project was to develop a simple and efficient process for verifying the load carrying capacity of structures on the Irish MIU Network, considering the codified abnormal vehicle loadings which they have been designed to carry (e.g., HB loading per BD37/01 or LM3 loading per the Eurocodes) and the actual abnormal vehicles which may feasibly be anticipated on these routes.
As part of this project, RODIS carried out:
- A detailed statistical analysis of UK Weigh-in-Motion (WIM) records.
- A detailed statistical analysis of the Eirspan database (database of bridges in Ireland).
- A detailed statistical analysis of Irish abnormal vehicle permit records.
- Developed a suite of abnormal vehicle ‘silhouettes’.
- Developed a software tool which can be used when issuing permits for abnormal vehicles.
- Classified a ‘Green Motorway Network’ in Ireland, a network on which the studied types of -abnormal vehicles up to 100 tonnes are likely to be issued a permit.
Technical Lead - Responsible for bridge virtual monitoring and fatigue assessment of steel bridge using the long-run simulation of traffic.
- Developed Structural Health Monitoring (SHM) software (never developed before) for steel bridges that utilises the Bridge Weigh-in-Motion (B-WIM) concept to virtually monitor those parts of the bridge that are not monitored directly.
- Designed fatigue damage accumulation software to calculate damage at all points on the bridge over its lifetime.
The aim of the project was to improve bridge structural health monitoring technologies as follows:
- Develop a method of using existing road bridges to weigh vehicles in motion with a step change increase in accuracy.
- Develop a novel method of monitoring steel road bridges, susceptible to fatigue damage.
- Develop an accurate and cost-effective method of weighing trains in motion.
- Develop an improved method of monitoring railway bridges.
During the project, RODIS used its expertise in Structural Health Monitoring (SHM), Traffic Loading and Finite Element Analysis (FEA) techniques, to develop a virtual monitoring concept to assist in evaluating the fatigue life of steel cable-stayed bridges. This concept allows for all parts of the bridge that are not monitored directly using sensors to be 'virtually' monitored through simulation using a Finite Element Analysis. As a result, the fatigue damage calculation can be carried out for any location on the bridge, without the requirement for the installation of sensors at the specific location.
To test the accuracy of the virtual monitoring system, a cable-stayed steel box girder bridge near Rotterdam in the Netherlands was instrumented with two monitoring systems: (i) a Bridge WIM (B-WIM) system on the section of the bridge over the road and (ii) a more traditional monitoring system based on strain gauges on the section of the bridge over the canal and inside the pylon. Due to RODIS expertise in Weigh-in-Motion (WIM) Systems, it presided over the installation and implementation of both the Bridge Weigh-in-Motion (B-WIM) system and the more traditional system. This system not only measures the actual traffic load but the data is also used to calibrate a detailed FE model of the bridge generated by RODIS.
Following verification of the virtual monitoring system, the fatigue damage calculation was carried out. Using the traffic information from the B-WIM system, RODIS undertook ‘scenario modelling’ which enabled many years of traffic to be simulated from only a few months of measurements. The simulated traffic was used in the fatigue software developed to calculate the accumulated damage at all points on the bridge during its lifetime.
Technical Co-Lead - Responsible for rail infrastructure fatigue assessment.
The project vision was to provide a framework for rail infrastructure operators to ensure safe, reliable and efficient operation of ageing European railway networks. This was achieved through the development of effective monitoring and remediation strategies to incorporate in a life cycle assessment of existing rail networks.
RODIS, played a key role in the project, leading the work stream aimed at developing models which greatly improve the ability of track owners to predict the future condition of the infrastructure. It developed a probability-based assessment framework which encompassed not only railway bridges, but all aspects of railway infrastructure including tracks subject to settlement and slope stability. By incorporating stochastic procedures in structural assessment, the uncertainty associated with physical properties can be accurately modelled.
In addition, by considering the failure probability of a range of structures, infrastructure owners are better equipped to prioritize those repairs which are shown to be necessary. The framework was complimented with a sample assessment of the Nieporet railway bridge in Poland, which enabled sensor, inspection and NDT data (Structural Health Monitoring information) to be incorporated into the models.
As part of the project, sustainable technologies were developed for effective rehabilitation and strengthening of "older" existing railway infrastructure. These were verified using on-site application and modelling. Software tools were also developed to allow infrastructure owners to design cost-effective and environmentally friendly rehabilitation solutions for railway infrastructure.
The potential financial benefits to bridge owners are apparent from the results, which suggested that costly bridge repair/rehabilitation can be avoided. SMARTRAIL was an EU FP7 funded project under the Transport-2011 call.
Indicators of esteem
Ranked 7th in the Overall Degree GPA among 120 students of the School of Civil Engineering at Sharif University of Technology and awarded Exemption from Master's Degree National Entrance Exam as an 'Exceptionally Talented Student'.
Recipient of the Marie Curie Early Stage Researcher Scholarship, awarded by the European Commission for the duration of PhD at the University College Dublin (UCD).
Nominated by ARU students for the ‘Outstanding Personal Tutor Award’ for two consecutive academic years (2016–17 and 2017–18).
Manuscript peer reviewer for the following international journals:
Journal of Bridge Engineering – The American Society of Civil Engineering (ASCE)
Bridge Engineering Journal – The Institution of Civil Engineers (ICE)
Open Journal of Civil Engineering – Scientific Research Publishing (SCIRP)
International Journal of Building Pathology and Adaptation (emeraldinsight)
Postgraduate research supervision
- Transport Infrastructure Consequences of Failure Assessment Under Natural Hazards. Mr Chia Sadik. (Starting Sep. 2019 - Co-Supervisor)
- Dynamic Characterisation of Tall RC Buildings Using Ambient Vibration Measurement (AVM) Techniques. Mr Michael Millgate. (Starting July 2020 - Co-supervisor)
- Developing a Novel Visual-Based Traffic Stream Identification System. Mr Ahmed Al-Oballi. (Starting Sep. 2021 - Principal Supervisor)
- Dynamic Train Bridge Interaction Effects on Fatigue of Metallic Railway Bridges. Mr Khosru Rahman. (Starting Jan. 2022 - Co-Supervisor)
- Developing Data-driven Damage Identification Systems for Bridges. Mr Severious Iduh. (Starting April 2022 - Principal Supervisor)
Dr Hajializadeh is keen to supervise and support outstanding candidates who wish to pursue their studies at the PhD level. To discuss PhD research topics, funding opportunities and the application process, please contact Donya directly by email: email@example.com
- ENG2103: Structural Analysis 1
- ENG1076: Structural Design 1
- ENG1066: Solid Mechanics
- ENG1073: Fluid Mechanics
- ENGM054: Earthquake Engineering
- ENGM264: Infrastructure Systems, Interdependencies and Resilience (Module Contributor)
This study describes the development and testing of a railway bridge weigh-in-motion (RB-WIM) system. The traditional bridge WIM (B-WIM) system developed for road bridges was extended here to calculate the weights of railway carriages. The system was tested using the measured response from a test bridge in Poland, and the accuracy of the system was assessed using statically-weighed trains. To accommodate variable velocity of the trains, the standard B-WIM algorithm, which assumes a constant velocity during the passage of a vehicle, was adjusted and the algorithm revised accordingly. The results showed that the vast majority of the calculated carriage weights fell within ±5% of their true, statically-weighed values. The sensitivity of the method to the calibration methods was then assessed using regression models, trained by different combinations of calibration trains.
The common challenge currently faced by critical infrastructure (CI) asset owners and operators is the lack of an integrated and robust resilience-informed business planning and management approach in response to interdependent assets’ failures, in particular due to low-probability/high-impact environmental hazards. Interdependencies among CI can cause cascading failures and hence, amplify impacts due to these failures. This can also affect CI’s service restoration rate and consequently, reducing their resilience in coping with these hazardous events. As infrastructures are becoming more interdependent in some sectors, there is an increasing need for better management of the interactions and interdependencies. To reduce these impacts, an integrated resilience and vulnerability- informed Decision Support System (DSS) is required to identify interdependent network’s vulnerable components and introduce adaptive capacities accordingly. This is of particular importance given ever growing investments in asset management across different sectors in order to improve the resilience of the networks in response to extreme environmental hazards. This study presents a novel framework for building a resilience and vulnerability-informed decision support system (RV-DSS). This framework provides potential means of communicating challenges induced due to interdependencies and quantifies benefits of considering interdependencies in streamlining intervention strategies for systems. It also proposes a measure of network resilience in response to hazardous events, in addition to the commonly used measures of vulnerability for assessment of the network performance. The framework can be used in initiating the interdependency-based communications among different CI network owners and managers, leading to shared knowledge and common understanding of their connected assets, hidden failure propagation mechanisms and collective recovery process. The application of the framework is then demonstrated using a case study in North Argyll, Scotland. It is quantitatively demonstrated that although infrastructures with a higher level of interdependency, can impose the network to higher vulnerability, it provides a greater opportunity for an integrated recovery
The recent extreme weather events in Europe and around the world have raised issues about the organization and management of critical infrastructure. There is uncertainty and a lack of information on how infrastructure should be managed when subject to these extreme events. The existence of chaos and uncertainty in these situations can result in disruptions to transport, power outages and in the most extreme instances, loss of life. The 7th Framework RAIN (Risk Analysis of Infrastructure Networks in response to extreme weather) project is addressing these issues, involving partners from Ireland, Belgium, Germany, Finland, Italy, Netherlands, Slovenia and Spain. The objective of the RAIN project is to provide an operational analysis framework to minimize the impact of major weather events in the EU. This paper summarizes the work that will be performed in one of the work packages of the RAIN project. This work package will examine the impact of critical infrastructure failure on society, security issues and the economy. Based on a risk analysis framework, a means of quantifying the level of risk will be established. The risk procedure will be benchmarked against case studies conducted on critical transport and operational tactical connections. The project outputs will contribute to the process of knowledge management used in the protection of Critical Infrastructure and will provide a basis for the development of decision support systems.
The recent expansion of pervasive computing technology has contributed with novel means to pursue human activities in urban space. The urban dynamics unveiled by these means generate an enormous amount of data. These data are mainly endowed by portable and radio-frequency devices, transportation systems, video surveillance, satellites, unmanned aerial vehicles, and social networking services. This has opened a new avenue of opportunities, to understand and predict urban dynamics in detail, and plan various real-time services and applications in response to that. Over the last decade, certain aspects of the crowd, e.g., mobility, sentimental, size estimation and behavioral, have been analyzed in detail and the outcomes have been reported. This paper mainly conducted an extensive survey on various data sources used for different urban applications, the state-of-the-art on urban data generation techniques and associated processing methods in order to demonstrate their merits and capabilities. Then, available open-access crowd data sets for urban event detection are provided along with relevant application programming interfaces. In addition, an outlook on a support system for urban application is provided which fuses data from all the available pervasive technology sources and finally, some open challenges and promising research directions are outlined. Â© 2018 IEEE.
A framework is presented for the assessment of the safety of a bridge deck under actual traffic loading using an enhanced Monte Carlo method which attempts to reduce computational cost while preserving the advantages of more conventional, computationally intensive, simulation. To generate the bridge loading scenarios, an extensiveWeigh-in-Motion (WIM) database is used to calibrate a sophisticated simulation model of two-directional traffic. Traffic and vehicle characteristics are generated from statistical distributions derived from measured traffic data. Two examples are used in this study to assess the usefulness and accuracy of the enhanced method. In the first, a simple example is used for which the exact theoretical probability of failure is available. Hence, the error in estimation can be assessed directly. In the second, 'long-run' simulations are used to generate a very large database of load effects from which very accurate estimates can be deduced of lifetime maximum effects. Â© 2013 Taylor & Francis Group, London.
In recent years, a variety of extreme weather events, including droughts, rain induced landslides, river floods, winter storms, wildfire, and hurricanes, have threatened and damaged many different regions across Europe and worldwide. These events can have devastating impact on critical infrastructure systems. The 7th Framework RAIN project will address these issues, involving partners from Ireland, Belgium, Germany, Finland, Italy, Netherlands, Slovenia and Spain. In this project, the impact of critical infrastructure failure on society, on security issues and on the economy will be examined. Based on the impacts of the failures, quantifiable benefits (from a societal, security and economic standpoint) of providing resilient infrastructure will be identified. In this project, a means of quantifying the level of risk will be established, first due to single land transport mode failures, and second due to selected multi-mode-interdependent failure scenarios (e.g. failure of power stations result in failure of electrical train lines). This paper introduces the RAIN project and its goal of developing a methodology to create an advanced risk assessment procedure, including a probabilistic based approach, to derive a measurable indicator of risk.
A framework is presented for the assessment of the safety of a bridge deck, taking account of its 2-dimensional nature. Random field analysis is proposed to determine the spatial distribution of resistance probability but is not implemented in this paper. Monte Carlo simulation, calibrated using Weigh-in-Motion traffic data, is used to determine lifetime maximum distributions of bending moment throughout the bridge deck. The 2-dimensional approach is shown, for the example considered, to give much lower probabilities of failure than the alternative approach of considering points one by one.
Infrastructure networks do not exist in isolation. Rather they are interconnected to other infrastructures and, as technological development increases, so too does the linkage between networks. Interdependencies among Critical Infrastructure (CI) can cause cascading failures and hence amplify negative consequences due to these failures. This can also affect CI’s service restoration rate and consequently reducing their resilience in coping with these hazardous environmental events. For example, failure of the water drain and sewer system due to 2002 Glasgow flooding affected many homes and closed many main roads and stations such as the A82 and A8 roads, Buchanan Street subway station and Dalmarnock through to Exhibition Centre stations on the Argyle Line. As infrastructures are becoming more interdependent at some sectors, there is an increasing demand for more effective management of these interactions and interdependencies. This paper provides details of a quantitative metric for the robustness, recoverability, rapidity and resourcefulness of the interdependent infrastructure network in response to hazardous event. By generating a quantitative measure of network resilience, considering infrastructure interdependencies, the most severe failure scenarios and their spatial impacts can be identified and mapped. This can lead to prioritise future business planning strategies for CI asset owners and managers. To illustrate the application of the proposed approach, a case study in North Argyll, Scotland is analysed and presented in this paper.
In this study a structural health monitoring (SHM) system is combined with bridge weigh-in-motion (B-WIM) measurements of the actual traffic loading on a bridge to carry out a fatigue damage calculation. The SHM system uses the ‘virtual monitoring’ concept, where all parts of the bridge that are not monitored directly using sensors, are ‘virtually’ monitored using the load information and a calibrated finite element (FE) model of the bridge. Besides providing the actual traffic loading on the bridge, the measurements are used to calibrate the SHM system and to update the FE model of the bridge. The newly developed virtual monitoring concept then uses the calibrated FE model of the bridge to calculate stress ranges and hence to monitor fatigue at locations on the bridge not directly monitored. The combination of a validated numerical model of the bridge with the actual site-specific traffic loading allows a more accurate prediction of the cumulative fatigue damage at the time of measurement and facilitates studies on the implications of traffic growth. To test the accuracy of the virtual monitoring system, a steel bridge with a cable-stayed span in the Netherlands was used for testing. © 2017, Canadian Science Publishing. All rights reserved.
Currently, the available decision support systems (DSS) rely on risk/vulnerability measures while interdependencies and their resilience in response to extreme environmental hazards are overlooked. Interdependencies among Critical Infrastructure (CI) can cause cascading failures and hence amplify negative consequences due to these failures. This can also affect CI’s service restoration rate and consequently reducing their resilience in coping with these hazardous environmental events. As infrastructures are becoming more interdependent at some sectors, there is an increasing need for better management of these interactions and interdependencies. Conventional infrastructure management techniques aim to provide a high degree of reliability in design process and risk analysis is the commonly used technique in assessing the response to disastrous threats. However, there are limitations to risk assessment in which not all risks can be quantified due to the existence of emerging and unobserved threats and highly improbable events with high degree of uncertainty are dealt with poorly. To manage the infrastructure interdependencies and their interactions in response to disastrous events, in addition to holistic risk/vulnerability mitigation approach; there is a need for resilience-informed management system. This will establish the key components of existing CI network and will assess the sensitivity of these components to disastrous events and their capacity in coping with such events. Resilience informed management systems put the main emphasis on systems’ recoverability, resourcefulness and rapidity, in addition to its robustness. Unlike the available DSSs, RV-DSS provides a measure of network resilience in response to hazardous events, in addition to vulnerability measure. This measure provides a quantitative metric of the robustness, recoverability, rapidity and resourcefulness of the infrastructure network in response to environmental hazards. This paper presents the application of RV-DSS on a transport network case study, quantifying failure propagation due to inherent infrastructure interdependencies and streamlining strategic planning (e.g., winter preparedness in road and rail networks; use of smart technologies in structural health monitoring) by focusing on risk zones of infrastructure networks, and improve the resilience of infrastructure systems in response to low probability/high impact hazardous events.
Bridge weigh-in-motion (B-WIM) is a method by which the axle weights of a vehicle travelling at full highway speed can be determined using a bridge instrumented with sensors. This paper looks at the history of B-WIM, beginning with early work on weigh-in-motion (WIM) technologies in the 1960s leading to its invention by Fred Moses and George Goble in the USA in the mid 1970s. Research initiatives in Australia and Europe have focused on improving B-WIM accuracy. The moving force identification (MFI) method models the dynamic fluctuation of axle forces on the bridge and holds particular promise. B-WIM accuracy depends on bridge site conditions as well as the particular data processing algorithm. The accuracy classifications of several B-WIM installations reported in the literature are summarised in this paper. Current accuracy levels are sufficient for selecting vehicles to be weighed using static scales, but insufficient for direct enforcement. Copyright Â© 2014 Inderscience Enterprises Ltd.
Conventional bridge weigh-in-motion (BWIM) uses a bridge influence line to find the axle weights of passing vehicles that minimize the sum of squares of differences between theoretical and measured responses. An alternative approach, probabilistic bridge weigh-in-motion (pBWIM), is proposed here. The pBWIM approach uses a probabilistic influence line and seeks to find the most probable axle weights, given the measurements. The inferred axle weights are those with the greatest probability amongst all possible combinations of values. The measurement sensors used in pBWIM are similar to BWIM, containing free-of-axle detector sensors to calculate axle spacings and vehicle speed and weighing sensors to record deformations of the bridge. The pBWIM concept is tested here using a numerical model and a bridge in Slovenia. In a simulation, 200 randomly generated 2-axle trucks pass over a 6 mlong simply supported beam. The bending moment at mid-span is used to find the axle weights. In the field tests, 77 pre-weighed trucks traveled over an integral slab bridge and the strain response in the soffit at mid-span was recorded. Results show that pBWIM has good potential to improve the accuracy of BWIM. Â© 2018, Canadian Science Publishing. All rights reserved.
Probabilistic analysis of traffic loading on a bridge traditionally involves an extrapolation from measured or simulated load effects to a characteristic maximum value. In recent years, long run simulations, whereby thousands of years of traffic are simulated, have allowed researchers to gain new insights into the nature of the traffic scenarios that govern at the limit state. For example, mobile cranes and low-loaders, sometimes accompanied by a common articulated truck, have been shown to govern in most cases. In this paper, the extreme loading scenarios identified in the long-run simulation are applied to a non-linear, two-dimensional (2D) plate finite element model. For the first time, the loading scenarios that govern in 2D nonlinear analyses are found and compared to those that govern for 2D linear and one-dimensional (1D) linear and nonlinear analyses. Results show that, for an isotropic slab, the governing loading scenarios are similar to those that govern in simple 1D (beam) models. Furthermore, there are only slight differences in the critical positions of the vehicles. It is also evident that the load effects causing failure in the 2D linear elastic plate models are significantly lower, i.e., 2D linear elastic analysis is more conservative than both 2D nonlinear and 1D linear and nonlinear analyses. Â©, 2015, National Research Council of Canada. All Rights Reserved.
Changes in the likelihood and severity of extreme weather events and climate-related disasters can result in the failure of critical infrastructure (CI) elements and networks. A Quantitative risk assessment of such failures is a core part of risk management protocols. In the RAIN project, funded by 7th Framework Programme, a systematic risk analysis framework is being developed which quantifies risks due to extreme events by explicitly considering the impacts of extreme weather events on critical infrastructure. In this paper, an overview of developed advanced risk assessment procedure quantifying multi-mode risks and the techniques required to assess the interaction between different hazardous events and various critical infrastructure systems is provided. In this study it is assumed that multi-risk scenarios refer to two main components: a. multi-hazard (i.e. the potential for one or more secondary hazard triggered a by primary hazard event) and b. multi-vulnerability (i.e., potential for failure propagation in critical infrastructure network(s)) scenarios. Descriptions of various multi-hazard and multi-vulnerability scenarios are provided and the approach required to quantify risk arising from each scenario is outlined and the application is illustrated for flash flooding case study in North of Italy.
Bridge structures are key components of highway infrastructure and their safety is clearly of great importance. Safety assessment of highway bridges requires accurate prediction of the extreme load effects, taking account of spatial variability through the bridge width and length. This concept of spatial variability i s also known as random field analysis. Reliability - based bridge assessment permits the inclusion of uncertainty in all parameters and models associated with the deterioration process. Random field analysis takes account of the probability that two points n ear each other on a bridge will have correlated properties. This method incorporates spatial variability which results in a more accurate reliability assessm ent. This paper presents an integrated model for spatial reliability analysis of reinforced concre te bridges that considers both the bridge capacity and traffic load. A sophisticated simulation model of two - directional traffic is used to determine accurate annual maximum distributions of load effect. To generate the bridge loading scenarios, an extensive Weigh-in-Motion (WIM) database, from five European countries, is used. For this, statistical distributions for vehicle weights, inter - vehicle gaps and other characteristics are derived from the measurements, and are used as the basis for a Monte Carlo simulation of traffic. Results are presented for bidirectional traffic, with one lane in each direction, with a total flow of approximately 2000 trucks per day.
Resistance and loads are often correlated in time and space. The paper assesses the influence of these correlations on structural reliability/probability of failure for a typical two-lane reinforced concrete (RC) slab bridge under realistic traffic loading. Spatial variables for structural resistance are cover and concrete compressive strength, which in turn affect the strength and chloride-induced corrosion of RC elements. Random variables include pit depth and model error. Correlation of weights between trucks in adjacent lanes and inter-vehicle gaps are also included and are calibrated against weigh-in-motion data. Reliability analysis of deteriorating bridges needs to incorporate uncertainties associated with parameters governing the deterioration process and loading. One of the major unanswered questions in the work carried out to date is the influence of spatial variability of load and resistance on failure probability. Spatial variability research carried out to date has been mainly focused on predicting the remaining lifetime of a corroding structure and spatial variability of material, dimensional and environmental properties. A major shortcoming in the work carried out to date is the lack of an allowance for the spatial variability of applied traffic loads. In this article, a two-dimensional (2D) random field is developed where load effects and time-dependent structural resistance are calculated for each segment in the field. The 2D spatial time-dependent reliability analysis of an RC slab bridge found that a spatially correlated resistance results in only a small increase in probability of failure. Despite the fact that load effect at points along the length of a bridge is strongly correlated, the combined influence of correlation in load and resistance on probability of failure is small. Â© 2015 Informa UK Limited, trading as Taylor & Francis Group.
This paper reviews a range of statistical approaches to illustrate the influence of data quality and quantity on the probabilistic modelling of traffic load effects. It also aims to demonstrate the importance of long-run simulations in calculating characteristic traffic load effects. The popular methods of Peaks Over Threshold and Generalised Extreme Value are considered but also other methods including the Box-Cox approach, fitting to a Normal distribution and the Rice formula. For these five methods, curves are fitted to the tails of the daily maximum data. Bayesian Updating and Predictive Likelihood are also assessed, which require the entire data for fittings. The accuracy of each method in calculating 75-year characteristic values and probability of failure, using different quantities of data, is assessed. The nature of the problem is first introduced by a simple numerical example with a known theoretical answer. It is then extended to more realistic problems, where long-run simulations are used to provide benchmark results, against which each method is compared. Increasing the number of data in the sample results in higher accuracy of approximations but it is not able to completely eliminate the uncertainty associated with the extrapolation. Results also show that the accuracy of estimations of characteristic value and probabilities of failure are more a function of data quality than extrapolation technique. This highlights the importance of long-run simulations as a means of reducing the errors associated with the extrapolation process. Â© 2015 Elsevier Ltd.
To perform effectively, a Bridge Weigh-in-Motion (B-WIM) system requires accurate information on the location and speed of all axles on the bridge. In recent years, axle detection is by sensors under the bridge – so called Free-of-Axle-Detector or Nothing-On-Road (NOR) B-WIM. As axles pass over an axle detecting strain sensor, there is a peak in strain which can be detected by the data acquisition system. This approach works well for some bridges but there are challenges for beam-and-slab bridges where the beams are deep, a common form of construction in Alabama. The slabs in such bridges are therefore generally used for axle detection but the peaks in the slab strains are quite sensitive to the transverse position of the wheels over the beam. This paper describes a study into axle detection which tests alternative strategies for a range of bridge types and spans.
Critical infrastructures (CIs), provide essential services to the society. As infrastructures are becoming more interdependent, there is an increasing need for better management of their interactions and interdependencies. Interdependencies among CI can cause cascading failures and, hence, amplify negative consequences due to these failures. This can also affect CIs' service restoration rate and consequently reduce their resilience in coping with these hazardous events. The common challenge currently faced by CI asset owners is the lack of robust resilience-informed business planning and management strategies in response to interdependent assets' failures due to low-probability/high-impact hazards. This is of particular importance as CI owners and managers are investing more on improving the resilience of their assets in response to extreme environmental hazards. This study has approached CIs nexus from the interdependency management point of view. It has developed an integrated resilience assessment framework to identify and map interdependency-induced vulnerabilities in critical infrastructure networks. This framework can potentially support effective management of the interdependencies in CI networks. The findings have been reflected in mapping the connection between the changes in resilience due to interdependency-induced failures and the cost of intervention scenarios, providing means of exploring shared intervention strategies.
Random Field theory has emerged in recent years to model the statistical correlation of resistance in concrete structures and to determine its influence on the probability of structural failure. A major shortcoming in the work carried out to date is the spatial variability and corresponding correlation associated with applied traffic loads. In this paper the influence of spatial correlation of both traffic load and resistance is considered in the context of bridge safety assessment. The current study, explores, the nature of the problem by three theoretical examples. As a general trend, examples show that while traffic loads are weakly correlated, load effects are strongly correlated as the same heavy vehicle often causes extremes of load effect in different parts of the bridge which is due to the transverse sharing of load (measured here using a load sharing factor). It is found that the strength of correlation of load effect depends greatly on the load sharing factor which is treated in a simple way in many studies. In a more sophisticated beam-and-slab bridge example, load sharing factors are derived from a finite element analysis to assess transverse load sharing, and are shown to vary by girder number, girder segment and by load location. Despite the fact that load effect at points along the length of a bridge is strongly correlated, the combined influence of correlation in load and resistance on probability of failure is small. Â© 2015 Elsevier B.V.
HL-93, the current bridge traffic load model used in the United States is examined here. Weigh-in-motion (WIM) data from 17 sites in 16 states containing 74 million truck records are used to assess the level of consistency in the characteristic load effects (LEs) implied by the HL-93 model. The LEs of positive and negative bending moments and shear force are considered on single- and two-lane same-direction slab and girder bridges with a range of spans. It is found that the ratio of WIM-implied LE to HL-93 LE varies considerably from one LE to another. An alternative model is proposed that achieves improvements in consistency in this ratio for the LEs examined, especially for the single-lane case. The proposed model consists of a uniformly distributed load whose intensity varies with bridge length. Â© 2014 American Society of Civil Engineers.
The recent extreme weather events in UK, Europe and around the world have raised issues about the organization and management of critical infrastructure subjected to any crisis. Lack of information on infrastructure management when subject to these disastrous events, along with embedded uncertainties of these disastrous events, will lead to disruptions to all connected critical infrastructure. Recently there has been a change in ideology where instead of relying on the ability to reduce risks induced by disastrous events, it is economically desirable to manage infrastructure network based on their resilience and their capacity in coping with these disruptions. This paper summarizes the work that is being implemented in developing a resilience-informed decision making system. Resilience-informed management system will provide means of quantifying the level of system bounce-back ability to all hazard scenarios in addition to their vulnerability. This study will eventually provide means of optimizing design and investment strategies for critical infrastructure owners and managers.
In a traditional deterministic assessment, a dynamic amplification factor (DAF) is applied to the static loading in order to account for dynamics. The codified DAF values are appropriately conservative in order to consider the wide range of structures and load effects to which they are applied. In the current analysis, a site specific assessment dynamic ratio (ADR) is calculated from direct measurement on an eighty year old steel truss railway bridge. The ADR is defined as the ratio of characteristic total stress to the characteristic static stress. The application of an ADR is a relatively new concept which has rarely been considered for railway bridges. An assessment performed on the bridge in question showed a decrease in the dynamic allowance when considering the site specific ADR, corresponding to a twenty-six percent decrease in the calculated stress. The measurements available were also used to derive a robust stochastic model for dynamic allowance which considered the correlation between DAF and stress level. The developed model was applied to a probabilistic assessment and resulted in a nine percent increase in reliability. Â© Civil-Comp Press, 2016.
With the increasing cost of energy the need to provide energy efficient buildings continues to grow. In 2003 the EU introduced the Energy Performance of Buildings Directive and this was enforced by all member states by 2006. The need to continually improve thermal performance has lead to member states implementing their own national initiatives, and from next year the National Standards Authority of Ireland will specify that all certified sandwich panel products comply with the incoming building regulations. The incoming building regulations stipulate that all sandwich panels achieve a U-value of 0.15 W/m2K, a reduction from the current value of 0.25 W/m2K. This is a significant challenge and requires that there be no significant heat loss through the panel. This paper presents the results of a collaborative project with a sandwich panel manufacturer whereby the thermal performance of a number of concrete panels was assessed. Each sandwich panel contained an inner concrete wythe of 150mm thickness, a 120mm layer of phenolic foam insulation and a 90mm thick outer layer of concrete. For structural reasons it is necessary to use connectors between the inner and outer concrete wythes, but these connectors have the potential to allow heat loss. In this study 2 connector types were used: 1 manufactured using FRP, the other with stainless steel. A control (non-structural) panel was manufactured containing no connectors. The thermal performance of each panel was assessed through experimental hot-box testing to determine U-values. This was complemented by a series of images taken using a thermal camera to show areas of heat loss. In addition the U-values were also determined using a theoretical numerical approach and a thermal finite element analysis (using MSC Patran) was conducted to determine the heat flux through the panel. The results showed that the connector type has a significant influence on the thermal performance of the sandwich panels, and that those containing steel connectors were not capable of providing the required U-value. The relative performance of the various panel types was consistent between analysis methods, as the finite element, the numerical and experimental approaches were in agreement. In addition, the heat losses observed through the thermal imaging camera were consistent with the heat losses predicted by the finite element analysis. It is proposed then that the use of numerical and finite element approaches has a valuable role in the design of thermally efficient sandwich panels. The experimental testing required is time consuming and requires significant effort. The analysis approach described above will make the design process more efficient and facilitate the construction of energy efficient buildings.
Assessment of highway bridge safety requires a prediction of the probability of occurrence of extreme load effects during the remaining life of the structure. While the assessment of the strength of an existing bridge is relatively well understood, the traffic loading it is subject to, has received less attention in the literature. The recorded traffic data are often limited to a number of days or weeks due to the cost of data collection. Studies in the literature have used many different methods to predict the lifetime maximum bridge load effect using a small amount of data, including fitting block maximum results to a Weibull distribution and raising maximum daily or maximum weekly distributions to an appropriate power. Two examples are used in this study to show the importance of the quantity of data in predicting the lifetime maximum distribution. In the first, a simple example is used for which the exact theoretical probabilities are available. Hence, the errors in estimations can be assessed directly. In the second, ‘long-run’ simulations are used to generate a very large database of load effects from which very accurate estimates can be deduced of lifetime maximum effects. Results are presented for bidirectional traffic, with one lane in each direction, based on Weigh-in-Motion data from the Netherlands.
This paper considers the problem of assessing traffic loading on road bridges. A database of European WIM data is used to determine accurate annual maximum distributions of load effect. These in turn are used to find the probability of failure for a number of load effects. Using the probability of failure as the benchmark, traditional measures of safety - factor of safety and reliability index - are reviewed. Both are found to give inconsistent results, i.e., a given factor of safety or reliability index actually corresponds to a range of different probabilities of failure. Â© 2012 Taylor & Francis Group.
To assess the safety of an existing bridge, the traffic loads to which it may be subjected in its lifetime need to be accurately quantified. In this paper the 75 year characteristic maximum traffic load effects are found using a carefully calibrated traffic load simulation model. To generate the bridge loading scenarios, an extensive weigh in motion (WIM) database, from three different European countries, is used. Statistical distributions for vehicle weights, inter-vehicle gaps and other characteristics are derived from the measurements, and are used as the basis for Monte Carlo simulations of traffic representing many years. An advantage of this “long-run” simulation approach is that it provides information on typical extreme traffic loading scenarios. This makes possible a series of nonlinear finite element analyses of a reinforced concrete bridge to determine the response to typical characteristic maximum loadings. Results of the nonlinear analyses are compared to the corresponding results using Eurocode and AASHTO load models.
Promoting the resilience of critical infrastructure, when subjected to different hazardous events, is vital. However, applying inappropriate and/or imprecise resilience metrics or quantification techniques could increase the costs of resilience enhancement and reduce its effectiveness in critical infrastructure. This paper develops a method to evaluate and compare different resilience quantification techniques, in relation to different system failure states, in order to measure their effectiveness