__________ QUALITY SPECIFICATIONS FOR ROADWAY BRIDGES, STANDARDIZATION AT A EUROPEAN LEVEL Scientific Report on Short Term Scientific Mission Researcher Anja Vidovic anja.vidovic18@gmail.com Home Institution University of Natural Resources and Life Sciences, Department of Civil Engineering and Natural Hazards, Institute of Structural Engineering, Vienna, Austria http://www.baunat.boku.ac.at/iki/ Host Institution IFSTTAR-French Institute of Science and Technology for Transport, Development and Networks, Champs- sur-Marne, France http://www.sdoa.ifsttar.fr Start Date October 1, 2017 End Date October 31, 2017 Reference Code TU1406_STSM_38354
Transcript
__________
QUALITY SPECIFICATIONS FOR ROADWAY BRIDGES, STANDARDIZATION AT A EUROPEAN LEVEL
Scientific Report on Short Term Scientific Mission
Researcher Anja Vidovic [email protected] Home Institution University of Natural Resources and Life
Sciences, Department of Civil Engineering and Natural Hazards, Institute of Structural Engineering, Vienna, Austria
http://www.baunat.boku.ac.at/iki/
Host Institution IFSTTAR-French Institute of Science and Technology for Transport,
Development and Networks, Champs-sur-Marne, France
http://www.sdoa.ifsttar.fr
Start Date October 1, 2017 End Date October 31, 2017 Reference Code TU1406_STSM_38354
2
CONTENTS
1. Aims and Objectives ............................................................................................................... 3
2. Work carried out ..................................................................................................................... 4
2.1. Development of Indicators Readiness Level (IRL) scale ....................................................... 4
2.2. Definition of Information and Performance Indicators ........................................................... 4
2.3. Literature review and analysis of collected research-based Performance Indicators .............. 4
2.4. Work documentation in the Innovation Subgroup Report ...................................................... 4
3. Main results............................................................................................................................ 5
3.1. Development of levels of maturity (IRL scale) ...................................................................... 5
3.2. Definition and classification of Information and Indicators..................................................... 7
3.3. IRL classification of research-based Performance Indicators .............................................. 10
3.3.1. Research Performance Indicators related to corrosion ................................................... 10
8.1. Confirmation by the host institution on the sucessful execution of the STSM ...................... 16
3
1. AIMS AND OBJECTIVES
Road bridges are key elements in terms of safety and functionality for the whole infrastructure. The ageing and deterioration of the bridges and the increased traffic intensities and loads, make them the bottlenecks of the transport infrastructure. A decision to replace or repair, when and how to repair each individual structure, is common and difficult management issue for asset managers [1]. To be able to effectively identify maintenance needs, evaluation of specific Performance Indicators must be conducted. Either qualitative or quantitative Performance indicators (PIs) are being collected within the scope of COST Action TU1406. Based on the PIs with established Performance Goals (PGs), Quality Control (QC) plans are accomplished. Considering that quantification of the Indicators and Goals in European countries may
consistently differ, the main aim of the Action is to provide standardized quality specifications at the European level. One of the first assignments of the Action was to collect operational Performance Indicators from the participating countries and to analyze the obtained databases [2]. The Innovation Subgroup – leader Dr. André Orcesi and vice-leader Prof. Maria Pina Limongelli – is currently working on the collection and classification of the research-based Performance Indicators, that is indicators of performance not yet utilized for maintenance purposes, but that are being investigated by research groups. They have also introduced the idea of the Indicator Readiness Level (IRL) aimed to classify PIs based on their level of maturity. Firstly, the input from participating European countries regarding the research-based PIs was collected. Each country, through its Country Nominated Person (CNP) has been asked to fill out a form to classify the PIs based on the IRL and to propose a scientific paper where the considered PI is defined. The received research databases were processed and some disomogenities have been pointed out both in terms of definition of the PIs and in terms of classification according to the IRL. In the COST TU1406 Nicosia meeting, on June 30th 2017, Innovation Subgroup has reported the following issues:
• Conflicting Indicators Readiness Levels – the concept of IRL scale was not clear for all participants, resulting in different maturity levels applied to the same Performance Indicators,
• Uncertain definitions of Performance Indicators – input for the computation of the PIs and the output that is to be used in QC checks (i.e. the PI itself) were often not clearly differentiated
• Incomplete database of research-based Performance Indicators – various deterioration mechanisms were not considered.
The carried out Short-Term-Scientific-Mission (STSM) supported the resolving of the aforementioned issues of the Innovation Subgroup. The comprehensive surveying of the collected scientific documents related to Performance Indicators was carried out for the final outcome of a standardized research database at the European level with classification of the PIs aimed to define their maturity as tools for decision-making support.
4
2. WORK CARRIED OUT
Main assignments performed during the STSM are herein summarized.
2.1. DEVELOPMENT OF INDICATORS READINESS LEVEL (IRL) SCALE
Innovation Subgroup has developed a specific rating system – Indicator Readiness Scale – to be used to rank a certain Indicator for the usage in maintenance plans. During the STSM, as the submitted research-based Performance Indicators have been processed, additional modifications of the IRL scale were
performed, since IRL was not completely applicable to all Performance Indicators, due to their diversity and capability of computation. Final and modified IRL scale is presented in Section 3.1, together with its predecessor, Technology Readiness Level scale, on which IRL is based on.
2.2. DEFINITION OF INFORMATION AND PERFORMANCE INDICATORS
Considering high variety of processes that Performance Indicators can be applied to and consequent need for the different understanding of their Performance Goals, an overall categorization of received information was developed. It became important to understand what the input is for the computation of Performance Indicator, how is it computed and if the output may be applied in QC checks and related decision-making (see Section 3.2).
2.3. LITERATURE REVIEW AND ANALYSIS OF COLLECTED RESEARCH-BASED PERFORMANCE INDICATORS
As soon as the procedure for classification of Performance Indicators and application of IRL scale was defined, literature review had to be performed in order to analyze potential research-based Performance Indicator for its applicability in QC checks and related decision-making. To illustrate the application of IRL scale, Section 3.3 is providing examples of Performance Indicators related to reinforcement corrosion of concrete bridges.
2.4. WORK DOCUMENTATION IN THE INNOVATION SUBGROUP REPORT
While the Performance Indicators were processed, they were documented in the Innovation Subgroup Report. As being eligible to be used in QC checks and related decision-making, these PIs are belonging to the research-based Performance Indicators database.
5
3. MAIN RESULTS
3.1. DEVELOPMENT OF LEVELS OF MATURITY (IRL SCALE)
The ranking of research-based Performance Indicators based on their maturity level takes basis on the scale of Technology Readiness Level (TRL) that was originally developed to classify new technologies in terms of their maturity level. A brief description of the TRL is reported to introduce the concepts that have been used in the definition of the Indicator Readiness Level (IRL).
The Technology Readiness Level (TRL) is a scale that was developed in the 70s by the NASA (National Aeronautics and Space Administration) to assess the stage of development (maturity) of new technologies and to compare different technologies in terms of the maturity level from idea to application [3]. Around 2005 its use became widespread in the international space development community and in several other fields, with some adaptations to suit the different needs. The original drive for the TRL development was “communication and planning” aimed to synchronize the development of the individual technologies needed for the development of one high-tech technological systems. Development of individual technologies should be carefully planned and synchronized to avoid losses in terms of both time and investments. The need to align the maturity levels of different individual components brought to the need of a common scale that can be used as a tool for decision making about the investments on the individual technologies. Recently the use of TRL spread to other fields and the scale was adapted by changing the number of levels and/or grouping levels.
Table 1. Technology Readiness Level (TRL).
Definition according to H2020 work program 2014-2015. General annexes [4]
Description according to EU network for space
[5]
TRL1 Basic principles observed Principles postulated and observed but no experimental proof available.
TRL2 Technology concept formulated
Concept and application have been formulated. Examples are limited to analytic studies.
TRL3 Experimental proof of concept First laboratory tests completed to validate analytical predictions of separate elements of the technology
TRL4 Technology validated in lab Basic technological components are integrated and a small-scale prototype is tested in a laboratory environment (“ugly” prototype).
TRL5 Technology validated in relevant environment
A large-scale prototype is tested in relevant environment.
TRL6 Technology demonstrated in
relevant environment Prototype system tested in relevant environment to demonstrate operations under critical environmental conditions.
TRL7 System prototype demonstration in operational environment
Prototype tested in operational environment.
TRL8 System complete and qualified
Product in its final configuration. Manufacturing issues solved. Evaluation of the system to
determine if it meets design specifications. TRL9 Actual system proven in
operational environment Full commercial application, technology available for consumers.
For example at EU level the TRL is used for decision making on research and development investments, that is to classify research projects in order to assess their eligibility to access funding. In this case, there is a further goal (beside communication and planning) which is the assessment of the eligibility. The definition of the TRL levels has been updated accordingly asking to research project with mid/high TRL
to provide a business plan for future investments. Table 1 reports nine levels currently defined for the TRL. The term “relevant” referred to the environment indicates a simulated environment representative of the “operational” environment that is the one where the technology will operate in real world.
6
This last use of the TRL suggested that a similar scale could be used to assess the eligibility of Performance Indicators for quality checks and related decision making on real asset. This scale has been herein defined as IRL.This scale is meant to serve as a supporting tool for a twofold aim:
• Checking the eligibility of a Performance Indicator for quality check and related decision making on roadway bridges based on its maturity, and
• Selection of research needs on Performance Indicators, i.e. to underpin those indicators on which more research is required in order to bring them to the level of full applicability for quality checks.
The TRL scale from Table 1 has been adapted to take into account the differences between a technology and an Indicator:
• The use of Performance Indicators for quality checks requires the definition of Performance Goals;
• Performance Indicators are also used for ranking purposes beyond Quality Check;
• The computation of Performance Indicators usually requires experimental tests, and
• Performance Indicators are not objects so a “prototype” cannot be defined. Therefore, In order to take into account the previous requirements, the definition of the maturity levels of the IRL (Table 2) has been slightly changed with respect to those relevant to the TRL (Table 1).
Table 2. Indicator Readiness Level (IRL).
IRL1 Basic principles observed The principles underlying the parameter are known
IRL2 Indicator concept formulated The indicator is applied in analytical studies
IRL3 Experimental proof of concept Analytical and experimental studies (indoor) performed on a laboratory scale on specimens to
validate analytical predictions. IRL4 Indicator validated in laboratory Experimental studies are performed in laboratory on
a reduced scale model of the structure/structural element to produce a database for estimation of the indicator.
IRL5 Indicator validated in laboratory in simulated environment
Experimental studies performed in controlled laboratory (or outdoor) on reduced scaled model of
the structure/structural element reproducing real environmental conditions to produce a database for estimation of the indicator.
IRL6 Indicator demonstrated in relevant environment
Experimental studies performed in controlled laboratory (or outdoor) on a full-scale model of the structure/structural element reproducing real
environmental conditions to produce a database for estimation of the indicator.
IRL7 Indicator demonstrated in operational environment
Experimental studies performed on a real structure/structural element and/or application of the indicator for ranking purposes and related decision-making.
IRL8 System complete and qualified Indicator can be used for quality control check purposes and related decision-making. Applicability issues are solved.
IRL9 Actual system proven in operational environment
The indicator is systematically applied for the quality check of a structure/asset and related decision making
7
3.2. DEFINITION AND CLASSIFICATION OF INFORMATION AND INDICATORS
Discrepancy between information that can be retrieved from inspection and testing (namely, Performance Parameters) and Performance Indicators as functions of this information has been recognized. Therefore, to classify properly an each Indicator, it is important to understand developed procedure for screening of received information (Figure 1). Information
Performance Parameters (PP). The Performance Parameters are quantifyiable features that are
used to describe the state of a structure. They can be measurable quantities, functions of observation,
or functions of measures.
a. Measurable Quantities are obtained directly from measures during inspections, laboratory or
on site testings, monitoring systems, etc.
b. Functions of measures. are obtained as a function of a measurable quantity (e.g., interpolation
error is PP as a function of the modal shape).
c. Functions of observations are obtained from codified scales associated to visual inspections
(e.g. inspection score from visual observations).
The TRL in its original form (see Table 1) can be applied to rate the level of maturity of technologies
used to measure the Performance Parameters.
Indicators
Performance Indicators. (PIs) may be computed directly from Performance Parameters (see
Equation (1)), and can be used in quality control checks and related decision-making. Performance
Indicators are usually defined as the difference between the values of the PPs in two different states
of the structure (element), usually a reference state (the original intact, undamaged state) and the
current state. If the value of the performance parameter in the reference state is zero, the value of the
performance parameter and of the performance indicator coincide.
�� � |������ ����| (1)
where is
PI Performance Indicator
PPcurr Performance Parameter in the current state
PPref Performance Parameter in a reference state
Performance Indicators can be applied at the element/structure level or at the network/asset level. In
particular, at the element/structure (in further text just element) level, the PI can be used to check for
instance the compliance of the structural state with assigned limit state or generally with performance
goals, and to support further decisions on possible interventions on the structure. Alternatively, the
Performance Indicators can be used for decision making at network/asset level to rank elements of
the network/asset based on the PI’s value (in further text just network, although the differatiation must
be applied between network and asset level due to the fact that the network is implying existance of
traffic configuration, which does not necessarily have to be a case at the asset level).
The Performance Indicator that is applied at the element level can also be used to rate elements inside
the network level, i.e. these PIs are applicable at both element and network level. If the Performance
Indicator is applied at the network level, it is mostly used for prioritization of the intervention plans, but
it must be realized that the information that the Indicator provides can also be used for a decision at
the structure itself – particularly, if no information regarding the thresholds or limit states not available,
the Indicator can triger further investigations and indicate a need for additional testing on the object,
i.e. at the element level.
It is important to recognize the difference between computing the Indicator and applying it in QC
checks, at either element or network level. In the notations of the Indicators, the level is applied to
the computing aspect.
Furthermore, it is possible to classify the PIs (computed both at the element and at the network level)
with regard to their forecast capability:
8
a. Diagnostic Performance Indicator (DPI_E, DPI_N). DPI give information that allow decision-
making based only on the current structural condition.
b. Prognostic Performance Indicator (PPI_E, PPI_N). PPI require the definition of a model of the
future behaviour and give information that allow decision-making based on both the current
conditions and the forecasted future conditions.
In addition to aforementioned classification, every Performance Indicator is divided into following categories:
1. Indicators related to load effect side, such as those related to load bearing capacity
2. Indicators related to resistance side, for instance in consideration of various degradation
mechanisms, such as corrosion mechanism, loss of stiffness etc.
3. Indicators related to probabilistic (reliability) analysis, in which comparison between load
and resistance is performed, namely these are General Purpose Indicators.
The maturity level of Performance Indicators is rated according to Table 2 – Indicator Readiness Level.
For the purposes of better understanding of the classification, let’s consider the phenomenon of corrosion and variation of modal frequency. Corrosion is to be considered as observation (detected by capturing rust or other corrosion products during the visual inspection of a bridge). Carbonation depth and chloride content are the correspondent Performance Parameters (measurable) during the initiation phase. The value of the carbonation depth
or chloride content in the reference state (original) are ideally zero, therefore in this case the value of the Performance Parameter and of the Performance Indicator coincide. Both the indicators give information about the current state of the structure thus they are Diagnostic Performance Indicators. Based on the value of the DPI it could be estimated for example that depassivation limit state has been reached and that propagation period, i.e. corrosion, may start or has already started. This gives information about the current state of the structure and may allow a decision on possible reactive intervention measures. Alternatively, if the DPI reveals that depassivation limit state is not yet reached or that it is but without any occurrence of corrosion, proactive intervention measures can be taken. Furthermore, in order to determine the speed at which the reinforcement deteriorates, it possible to investigate the corrosion rate, that is, in the same manner as the forestated depassivation parameters, measurable PP that is considered as DPI since the difference in current corrosion rate and the initial one indicates the propagation of corrosion. Finally, if one would use developed analytical and/or numerical models, which are then functions of measures, all these PPs become Prognostic Performance Indicators, that are predicting the future deterioration levels. For instance, remaining service life can be estimated based on mathematical models and reliability analysis, determining the service life according to either the level of initation of corrosion (depassivation limit state – carbonation depth, chloride content) or development of the propagation period (corrosion rate). Considering that for these input PPs limit values are defined in the literature and stuctural codes, these indicators can be used at the element level. PPs and PIs related to corrosion belong to the group of Indicators related to the degradation mechanism and are computed
and applied in the maintenance plans at the element level. Application of the levels of maturity to corrosion related Performance Indicators is provided in 3.3.1. In order to illustrate the difference in application in maintenance plans between the element and network level, one may consider the example of the loss of stiffness, for instance modal frequency, since it is the most common dynamic parameter used in damage detection. Modal frequency is computed at the element level and does not have defined threshold so Performance Parameter and Indicator will not in this case coincide. Now, it is rather considered that PI is the variation of modal frequencies. Regarding the Eq. (1), this would mean that PI is the difference between two values of modal frequency, from the current state to the reference one, and exactly this difference implying worsening of the condition, i.e. decrease of the stiffness. Variation of modal frequencies can accordingly be used to prioritize intervention plans of various elements in the network, or can triger further examinations at the element level.
9
Figure 1 Procedure for classification of Performance Indicators
10
3.3. IRL CLASSIFICATION OF RESEARCH-BASED PERFORMANCE INDICATORS
Herein only the examples related to reinforcement corrosion mechanicsm are presented. It must be noted that all processed Performance Indicators are documented in the Innovation Subgroup official report. For an each Indicator, table with 3 columns is presented, where the first column is reffering to the IRL scale, second column is defining if the maturity level is reached (yes-Y or no-N), and finally is provided an explanation how the certain level is reached. The achievemnt of a certain level is documented by publications of resutls in the technical reports or scientific papers.
3.3.1. RESEARCH PERFORMANCE INDICATORS RELATED TO CORROSION
Already introduced Performance Indicators in the previous Section are those related to reinforcement corrosion, often studied and usually determined during the bridge inspections, considering that corrosion is the most frequent deterioration mechanism in reinforced concrete structures. Service life of these structures can be divided in initiation and propagation phase. During the first phase, corrosion is induced
by either chloride ingress or carbonation process. If the structure is exposed to aggressive environment, such are those structures that are located close to the seawater or exposed to de-icing salts, chlorides penetrate into concrete and start inducing corrosion. Alternatively, during the carbonation process, carbon dioxide penetrates the surface of the concrete and reacts with alkaline components in the cement paste, for the final result of reduction of pH value. The initiation phase is therefore characterized by these two processes, until the depassivation limit state is reached. Furthermore, when surface film of ferric oxide on the reinforcement is broken or depassivated, propagation phase starts. Steel corrosion itself is an electrochemical process that includes dissolution of iron and formation of corrosion products, such as rust. In order to capture the corrosion progress, corrosion rate is investigated, that is in addition referring to corrosion current density and electrical potential. Altogether, important PPs for corrosion process are undoubtedly carbonation depth, chloride content at the reinforcing steel and corrosion rate. They are measurable parameters that have well defined testing procedures and critical limits. As the change in these parameters, from the initial healthy state to the current one, indicates the level of deterioration, they are considered as Diagnostic Performance Indicators at the element level and when the prediction models are applied to same Performance Parameters, they evolve to Prognostic Performance Indicators. This Section reports IRL scales applied to an each corrosion related Indicator.
Chloride content at the reinforcing steel Chloride content (PP) is the total amount of chloride ion in concrete, including bound in the solid phases and free chlorides in the pore solution, where the corresponding Diagnostic Performance Indicator is the difference between the total amount of chloride in concrete at current and reference state.
Table 3 IRL applied to chloride content (measure � DPI_E)
IRL Level achieved
Explanation
1 Y
The concentration of chloride in the concrete consists of the free and
bound chloride ions in the concrete. The critical free chloride concentration in pore solution in contact with the rebar surface causes depassivation of the steel rebar leading to its corrosion.
2 Y It is possible to calculate change of the chloride content in concrete by various developed mathematical models, such as those from [6].
3 Y It is possible to perform laboratory tests on concrete specimens, in order to determine chloride concentration in hardened concrete.
4 Y It is possible to perform laboratory tests on reduced scale models, in order to determine chloride concentration in hardened concrete.
5 Y
It is possible to perform experimental studies on a reduced scale model of the structure in real environment. In reference [7] experimental studies were carried out in real marine environment on reduced scale model of the structure that lasted up to 15 years, in order to analyze the parameters that affect the concentration of chlorides.
6 Y It is possible to determine chloride content on a full-scale model of the structure/element in real environment.
11
7 Y It is possible to measure chloride concentration in concrete on real bridge/element. Determination of chlorides on case studies after 13 years [8], and 14, 20, 25 years of exposure are recorded [7].
8 Y
Determination of chloride content in hardened concrete is a well-established procedure and prescribed in EN 14629 [9]. The most frequent threshold, i.e. critical chloride concentration, used is equal to 0.4% by weight of cement (see also maximum chloride contents provided in EN 206 [10]), while the threshold for concentration of only free chlorides is 7 kg per m3 of pore solution [11]. With these developed testing procedures and defined goals, there are no issues in applying the indicator in QC checks and related decision-making.
9 Y Chloride content measurements are regularly used in QC checks and intervention plans [8], [7].
During the bridge inspections, investigation of chloride concentration is regularly performed test; wherefore DPI reaches the highest level of maturity. If the model for the prediction of remaining service life for chloride-induced corrosion is applied to chloride content estimation, the same now becomes PPI at the element level (Table 4). Moreover, researchers have also developed 3D numerical models for prediction of depassivation time of reinforcement modelling chloride ingress through concrete. Again, chloride content becomes PPI computed at the element level and its IRL is presented in Table 5. Considering from the innovation aspect, these prediction models present developments at the research level, while the PP itself is an operational parameter.
Table 4 IRL applied to chloride content – prediction model for remaining service life (function of a measure � PPI_E)
IRL Level achieved
Explanation
1 Y
Assessment of the chloride-induced corrosion is essential to predict the remaining service life of the structure and its limit states. Increase of chloride concentration near rebar leads to corrosion and decrease of bridge load carrying capacity and its service life.
2 Y It is possible to obtain the relationship between the chloride content, concrete parameters and service life [6], [12].
3 Y It is possible to perform laboratory tests on concrete specimens, in order to determine chloride concentration in hardened concrete, necessary for estimation of the indicator.
4 Y It is possible to perform laboratory tests on reduced scale model of the structure/element in order to determine chloride concentration in hardened concrete, required for the estimation of the indicator.
5 Y
It is possible to perform experimental studies on reduced scale model of the structure/element in real environment, in order to determine chloride concentration in hardened concrete, required for the estimation of the indicator.
6 Y
It is possible to perform experimental studies on a full-scale model of a structure/element in real environment, in order to determine chloride concentration in hardened concrete that is required for estimation of the indicator.
7 Y It is possible to perform experimental studies drilled out from a real
bridge or a component [7], [8], [12].
8 N It is possible to use estimation of the service life in related decision-making, but applicability issues still exist.
9 N Consultant companies have already used obtained results for updating service life estimation, but it is not yet systematically applied in QC and related decision-making.
Table 5 IRL applied to chloride content – 3D numerical prediction model for remaining service life (function of a measure � PPI_E)
IRL Level achieved
Explanation
1 Y The concentration of chloride in the concrete consists of the free and bound chloride ions in the concrete. The critical free chloride
12
concentration in pore solution in contact with the rebar surface causes depassivation of the steel rebar leading to its corrosion.
2 Y
Chloride concentration in concrete can be calculated using 3D chemo-hygro-thermo mechanical numerical model (3D CHTM model). The model is adapted to simulate the transport of chlorides in concrete, in both real and laboratory environment. In addition, model is developed to simulate implications of reinforcement corrosion on both new and damaged concrete structure/element [11].
3 Y It is possible to perform laboratory tests on concrete specimens, in order to determine chloride concentration in hardened concrete [13].
4 Y It is possible to perform laboratory tests on reduced scale models, in order to determine chloride concentration in hardened concrete.
5 Y
It is possible to perform experimental studies on a reduced scale model of the structure in real environment. In reference [7] experimental studies were carried out in real marine environment on reduced scale
model of the structure that lasted up to 15 years, in order to analyze the parameters that affect the concentration of chlorides.
6 Y It is possible to determine chloride content on a full-scale model of the
structure/element in real environment.
7 Y
It is possible to measure chloride concentration in concrete on real
bridge/element of a bridge. Determination of chlorides on case studies after 13 years [8], and 14, 20, 25 years of exposure are recorded [7].
8 N
Results obtained from the bridges are in good agreement with the numerical results. 3D CHTM numerical model could be used in QC checks and related decision-making, but issues still exist (such as computational time of the model).
9 N Indicator is not used for QC checks and related decision-making.
Carbonation depth Carbonation depth (PP) is the depth of the concrete carbonated surface layer. Increase in the carbonation depth indicates that depassivation, i.e. carbonation induced corrosion, could occur. Therefore, it is a DPI at the element level (Table 6) where its corresponding Performance Goal is not to reach the rebar front, i.e. concrete cover. It could also be possible to apply models for prediction of remaining service life to carbonation process, similarly as to chloride content, and then this PP would be PPI at the element level.
Table 6 IRL applied to carbonation depth (measure � DPI_E)
IRL Level
achieved Explanation
1 Y Carbonation process leads to reduction of the pH-value to less than 9, i.e. to depassivation limit state and corrosion of reinforcing bars.
2 Y It is possible to determine carbonation depth by various developed mathematical models, such as those from [6].
3 Y
Determination of carbonation depth is carried out by the phenolphthalein method [14] and the procedure can be carried out from the specimen in the lab to the investigation on the real structure. Since the evolution of carbonation depth in normal environment is slow, an accelerated carbonation chamber can be used in laboratories [15].
4 Y It is possible to determine carbonation depth from a reduced scale model of a bridge/component (indoor).
5 Y It is possible to determine carbonation depth from a reduced scale model of a bridge/component (outdoor).
6 Y It is possible to determine carbonation depth on full-scale model of a real bridge/component (outdoor).
7 Y It is possible to determine carbonation depth on a real bridge/component.
8 Y Carbonation depth can be used in QC checks and related decision-making.
9 Y Carbonation depth is already systematically used in QC checks and related decision-making.
13
In the same manner as the chloride content, carbonation depth is already applied at the operational level and regularly tested parameter, reaching therefore IRL 9.
Corrosion rate Assessment of the corrosion rate is essential for prediction of remaining service life of the structure and its limit states. Corrosion rate is referring to corrosion current density, due to linear relationship between the corrosion rate and corrosion current, and indirectly to electric potential on the rebar surface and in pore solution in concrete. Higher corrosion rate indicates more rapid degradation, which is, by definition, DPI at the element level (Table 7). It is possible to predict corrosion rate using already presented 3D numerical model that was previously predicting depassivation time of reinforcement, and afterwards was developed to model the propagation phase of the reinforcement corrosion [16] – and in this case corrosion rate is regarded as PPI (Table 8).
Table 7 IRL applied to corrosion rate (measure � DPI_E)
IRL Level
achieved Explanation
1 Y
Corrosion rate is corrosion current density in the corrosion unit on the cathodic and anodic part of the steel rebar during electrochemical processes of steel corrosion in concrete. Current density of 0.05 A/m2-1 A/m2 is equivalent to the reduction of the reinforcement diameter for
approximately 0.1-2 mm/year.
2 Y It can be indirectly expressed as electric potential on the rebar surface and in pore solution in concrete.
3 Y It is possible to perform laboratory tests on small concrete specimens in order to determine corrosion current density and electrical potential.
4 Y It is possible to perform laboratory tests on a reduced scale model of a bridge/element.
5 Y It is possible to perform experimental studies on a reduced scale model of a bridge/element outdoor.
6 Y It is possible to perform experimental studies on a full-scale model of a bridge/element outdoor.
7 Y
It is possible to perform experimental studies on a real bridge/element. Real case studies are reported in [7], using half-cell potentials. Determination of the macro-cell electric potential can be carried out according to [17].
8 Y Assessment of the corrosion rate, i.e. corrosion current density and electrical potential can be used in decision-making.
9 Y Indicator is already systematically used in QC checks and related
decision-making.
Table 8 IRL applied to corrosion rate – 3D numerical prediction model for remaining service life (function of a measure � PPI_E)
IRL Level
achieved Explanation
1 Y
Corrosion rate is corrosion current density in the corrosion unit on the
cathodic and anodic part of the steel rebar during electrochemical processes of steel corrosion in concrete. It can be indirectly expressed as electric potential on the rebar surface and in pore solution in concrete. Higher corrosion rate indicates more rapid degradation.
2 Y Corrosion rate can be calculated using 3D CHTM numerical model after depassivation of reinforcement in concrete [16]
3 Y It is possible to perform laboratory tests on small concrete specimens in order to determine corrosion current density and electrical potential.
4 Y It is possible to perform laboratory tests on a reduced scale model of a bridge/element in order to estimate the parameters required for determination of the indicator.
5 Y It is possible to perform experimental studies on a reduced scale model of a bridge/element outdoor in order to estimate the parameters required for determination of the indicator.
14
6 Y It is possible to perform experimental studies on a full-scale model of a bridge/element outdoor in order to estimate the parameters required for determination of the indicator.
7 Y It is possible to perform experimental studies on a real bridge/component. Real case studies are reported in [7], using half-cell potentials and the procedure defined in [17].
8 N Assessment of the corrosion rate, i.e. corrosion current density and electrical potential can be used in decision-making. Applicability issues of 3D numerical model still exist.
9 N Indicator is not used for QC checks and related decision-making.
Accompanying the modeling of the corrosion rate with 3D model, the expansion of the corrosion products (such as rust) can be modeled by 1D-contact corrosion elements on the reinforcement concrete contact surface [18]. Considering that corrosion products on the elements of the reinforced concrete structures are recorded during the visual inspections, this model with the addition of the corrosion propagation products is also reaching IRL 7, and evolves from function of measure and observation to PPI at the element level.
3.4. CONCLUSIONS
Regarding the research needs pointed out during the Nicosia COST TU1406 Action meeting, following issues have been solved during the STSM: concept of IRL scale had been clarified in more detail and definitions of Performance Indicators were checked and updated, giving a possibility to apply the levels of maturity in a proper manner. The problem of still incomplete research-based Performance Indicators database partially remains, since the collected information are mostly those that are of particular research interest to the participants of the COST TU1406 Action. Hence, it is acknowledged that there are various Indicators at the research stage that have not yet been documented and should be included in the future steps. Then the developed database itself could serve for improvement of existing performance assessment methods within bridge management systems at the European level.
4. FUTURE COLLABORATION
Many ideas and knowledge were exchanged during my STSM and I believe that I have developed good relationship with Dr. André Orcesi and Prof. Maria Pina Limongelli. The future collaboration is already secured thought the work connected with the current needs of the Innovation Subgroup, such as finishing of the Subgroup’s Report, on which I have continued working also after my STSM.
5. FORESEEN PUBLICATIONS/ARTICLES
Conference paper titled “Bridge Performance Indicators at the research level: an overview” is to be submitted to the upcoming 40th IABSE Symposium, which will take place in Nantes on September 19-21, 2018. The paper covers the results obtained during the STSM, i.e. definition and classification of Performance Indicators and categorization based on their maturity level is therein described.
6. ADDITIONAL COMMENTS
As a PhD student at University of Natural Resources and Life Sciences in Vienna, I am working on a research project, where final objective is to develop life-cycle bridge management tool for railway concrete bridges. Since I am notably focused on maintenance and decision-making support for concrete bridges, I am also working with some of here collected and presented Performance Indicators. Therefore, I very much appreciate the fact that I had the opportunity to enhance and gain additional experience in this field, in particular learn new techniques and novel IRL scale that could be used for rating of the Indicators within decision-making procedure. Undoubtedly, my knowledge was enriched and it was great to meet experts in this field of engineering and to share experiences with them. I would like to thank to entire COST TU1406 Management Committee for providing me this opportunity. In addition, my thanks go to the host institution and exceptional hosts Prof. Maria Pina Limongelli and Dr.
15
André Orcesi. I very much appreciate your detailed supervision and time invested into discussions related to my assignments.
7. REFERENCES
[1] M. Limongelli and A. Orcesi, "A proposal for classification of key performance indicators for road bridges," Vancouver, 2017.
[2] A. Strauss, A. Mandic Ivankovic, J. Matos and J. Casas, "WG1 Technical Report: Performance Indicators for Roadway Bridges of Cost Action TU1406," 2016.
[3] EARTO, "The TRL Scale as a Research & Innovation Policy Tool, EARTO Recommendations," 30 April 2014. [Online]. Available:
[4] European Commission, "HORIZON 2020 Work Programme 2014-2015," 22 July 2014. [Online]. Available: http://ec.europa.eu/research/participants/data/ref/h2020/wp/2014_2015/main/h2020-
wp1415-intro_en.pdf.
[5] COSMOS Space | NCP Network, "COSMOS 2020, the EU funded network of National Contact Points (NCPs) for Space," [Online]. Available: http://ncp-space.net/.
[6] The International Federation for Structural Concrete (fib), "fib Bulletin 34: Model Code for Service Life Design," 2006.
[7] M. Kuster Maric, Service life prediction of reinforced concrete bridges exposed to chlorides (PhD Thesis), University of Zagreb, Croatia, 2013.
[8] M. Kuster Maric, J. Ozbolt, G. Balabanic, A. Mandic Ivankovic and D. Zaric, "Service life predicton of concrete structures in maritime environment - case study: Maslenica Motorway Bridge.," in Proceedings of the 1st International Conference on Construction Materials for Sustainable Future, Zadar, Croatia, 2017.
[9] European Committee for Standardization, "EN 14629 - Products and systems for the protection and repair of concrete structures. Test methods. Determination of chloride content in hardened concrete," 2007.
[10] European Committee for Standardization, "EN 206: Concrete - Part 1: Specification, performance, production and conformity," 2005.
[11] J. Ozbolt, G. Balabanic, G. Periskic and M. Kuster, "Modelling the effect of damage on transport processes in concrete," Construction and Building Materials, vol. 24, pp. 1638-1648, 2010.
[12] K. Gode and A. Paeglitis, "Concrete bridge deterioration caused by de-icing salts in high traffic intensity road environment in Latvia," The Baltic Journal of Road and Bridge Engineering, pp. 200-207, 2014.
[13] L. Marsavina, K. Audenaert, G. De Schutter, N. Faur and D. Marsavina, "Experimental and numerical determination of the chloride penetration in cracked concrete," Construction and Building Materials, vol. 23, pp. 264-274, 2009.
[14] European Committee for Standardization, "EN 14630 - Products and systems for the protection and repair of concrete structures. Test methods. Determination of carbonation depth in hardened concrete by the phenolphthalein method," 2006.
[15] V. G. Papadakis, C. G. Vayenas and M. N. Fardis, "Experimental investigation and mathematical modeling of the concrete carbonation problem," Chemical Engineering Science, vol. 5, no. 6, pp. 1333-1338, 1991.
[16] J. Ozbolt, G. Balabanic and M. Kuster, "3D Numerical modelling of steel corrosion in concrete structures," Corrosion Science , vol. 53, pp. 4166-4177, 2011.
[17] ASTM Subcommittee G01.14, "ASTM 876: Standard Test Method for Corrosion Potentials of Uncoated Reinforcing Steel in Concrete," 2007.
[18] J. Ozbolt, F. Orsanic, M. Kuster and G. Balabanic, "Modelling bond resistance of corroded steel reinforcement," in Cairns JW, Plizzari G (eds) Bond in concrete 2012-general aspects of bond, Publisher creations, 2012.
16
8. ANNEXES
8.1. CONFIRMATION BY THE HOST INSTITUTION ON THE SUCESSFUL EXECUTION OF THE STSM
![Index []...2003/01/28 · IRL Jack O'Leary 04/11/1997 IRL Peter Lynch 26/11/1997 IRL Darragh McElhinney 2000 IRL Fearghal Curtin 14/07/1998 IRL Charlie O'Donovan 06/08/1999 IRL Barry](https://static.documents.pub/doc/80x56/5e9ad3bf924b7b6d1915a79a/index-20030128-irl-jack-oleary-04111997-irl-peter-lynch-26111997.jpg)
