Project Cluster LIFETIME
Lifetime Engineering of Buildings and Civil Infrastructures
for life time design and management of civil infrastructures and buildings
Project Cluster Lifetime Authors:
Prof. Dr. Asko Sarja, Technical Research Centre, VTT, Finland, Co-ordinator email@example.com
firstname.lastname@example.org Prof. Bamforth, Taywood Ltd, UK
Dr. Caccavelli and Dr. Jean-Luc Chevalier, CSTB, France
Prof. Sevket Durucan, Imperial College of Science Technology and Medicine, Royal School of Mines, UK
Espoo 14. October, 2005
1 Analysis methodology in lifetime engineering ...6
1.1 Classification of generic requirements...6
1.2 Analysis of lifetime functionality and performance ...7
1.2.1 General model for deterioration...7
1.2.2 Determination of model parameters...12
1.2.3 Software ...14
1.3 Analysis of lifetime economy ...16
1.3.2 The need for analysis of lifetime economy...16
1.3.3 Undertaking analysis of lifetime economy ...17
1.3.4 Dealing with uncertainty...18
1.3.5 Dealing with Net Present Value...18
1.4 Analysis of Lifetime Ecology (LCA)...20
1.4.1 Life Cycle Assessment (LCA) ...20
1.4.2 Mining Life Cycle Modelling: Cluster Project LICYMIN ...24
2 Predictive models for deterioration of structures - alternative principles of Lifecon methods..33
2.1 Alternative models ...33
2.2 Statistical degradation models [Lifecon D3.2] ...33
2.3 RILEM TC 130 CSL models [Lifecon D2.1] ...34
2.4 Reference structure models [Lifecon D2.2]...34
2.5 Environmental load parameters [Lifecon D4.1] ...35
2.6 Quantitative classification of environmental loads [Lifecon D4.2]...36
2.7 GIS-based quantification of environmental load parameters [Lifecon D4.3]...37
3 Multiple criteria decision making for selecting investment...39
3.1 Principles and methods of multiple criteria decision making...39
3.1.1 The Aim of the Methods ...39
3.1.2 Multi Attribute Decision Aid (MADA) ...39
3.1.3 Quality Function Deployment (QFD)...41
3.1.4 Risk Analysis ...41
3.2 Decision making tool for multiple criteria decision making...43
3.2.1 Method ...43
3.2.2 INVESTIMMO: A multicriteria investment decision-making tool...45
4 Predictive and integrated life cycle management system ...51
4.2 Methods and procedures ...53
4.2.1 Optimisation and Decision-Making Methodology ...53
4.2.2 Reliability Based Systematics...56
4.3 Management system...58
5 LCCP model design and user manual ...62
5.1 The general framework for the LCCP model...62
5.2 WLC model components ...64
5.3 Dealing with uncertainties ...65
5.4 Software applications...65
APPENDIX: Terms and Definitions of the Lifecon LMS...72
Buildings and civil and industrial infrastructures are the longest lasting and most important products of our societies. However, traditionally most of the considerations and methods in design and management have been focused on a quite short term, and long term economy and performance of structures have been taken into account quite implicitly only.
There is a clear need for a uniform European approach for assessing, validating and operating Civil Infrastructures and Buildings with full consideration of lifetime usability (functionality and technical performance in use], safety, health and comfort), economic and ecological sustainability.
This kind of holistic approach raises needs for a wide co-operation of several experts from research and practice. A European consortium of outstanding experts from research and practice is ideal for carrying out a creative work for LIFECON objectives. Considering the fragmented European construction industry it is of vital importance that a project addressing these issues is operating on a European level with full participation of the important key actors in the sector.
Lifetime engineering is an innovative idea and a concretisation of this idea for solving the dilemma between a very long-term product and a short-term design, management and maintenance planning.
Lifetime engineering includes:
- Lifetime investment planning and decision making - Integrated lifetime design
- Integrated lifetime management and maintenance planning - Modernisation, reuse, recycling and disposal
- End-of Life Management and
- Environmental monitoring and impact assessment.
The integrated lifetime engineering methodology is aiming at regulating optimisation and guaranteeing the life cycle human conditions, economy, cultural compatibility and ecology with technical performance parameters. With the aid of lifetime engineering we can thus predict and optimise the human conditions (functionality, safety, health and comfort), the monetary (financial) economy and the economy of the nature (ecology). Beside these, also social aspects have to be taken into consideration.
Cluster:”Life time design and management of civil infrastructures and buildings, LIFETIME”, consisted of the following five projects:
1. INVESTIMMO: A Decision Making Tool for Long-Term Efficient Investment Strategies in Housing Maintenance and Refurbishment. Co-ordinator: Dr. Dominique Caccavelli, CSTB, France
2. EUROLIFEFORM: A probabilistic approach for predicting the life cycle cost and performance of buildings and civil infrastructure. Co-ordinator: Prof. Phil Bamforth, Taylor Woodrow, UK 3. LIFECON: Life Cycle Management of Concrete Infrastructures for improved sustainability.
Co-ordinator: Prof. Dr. Asko Sarja, VTT Building and Transport, Finland
4. LICYMIN: Life Cycle Environmental Impact in Mining. Co-ordinator: Prof. Dr. Sevket Durucan, ICSTM, UK
5. CONLIFE: Life-time prediction of high performance concrete with respect to durability. Co- ordinator: Prof. Dr. Setzer, Prof. Dr. Max J. Setzer, Universität Essen (DE)
The Cluster Lifetime had a common management, which was carried out by a Management Group.
The Management Group consisted of the following five co-ordinators of the “LIFETIME” projects:
Prof. Dr. Asko Sarja, Technical Research Centre, VTT, Finland, Co-ordinator Prof. Bamforth, Taywood Ltd, UK
Dr. Caccavelli and Dr. Jean-Luc Chevalier, CSTB, France
Prof. Sevket Durucan, Imperial College of Science Technology and Medicine, Royal School of Mines, UK
Prof. Dr. Max J. Setzer, Technical University Ruhr-Essen, Germany The objectives of the Cluster “LIFETIME” have been defined as follows:
- to integrate the knowledge of partners of these five projects for advancing the work and results of all projects
- to co-operate in similar tasks in order to avoid parallel overlapping work and parallel results - to produce an integrated and generic “European Guide for Life Time Design and
Management of Civil Infrastructures and Buildings” in order to contribute the development process in practical application of Life Time Design and Management on different sub areas and by different owners.
- To integrate the Information Networks through linking between these five projects
- To create and deliver a continuously updated database of produced models for later European exploitation
This report: European Guide for Life Time Design and Management of Civil Infrastructures and Buildings is the main deliverable of the Cluster Lifetime. It has been produced in co-operation between the co-ordinators and other central persons of the Cluster projects. Final writing work has been carried out in the Thematic Network Lifetime, which has included all co-ordinators of the Cluster projects as Principal Contractors, and has worked under co-ordination of Professor Asko Sarja.
The full results of the five Lifetime Cluster projects and of the Thematic Network Lifetime are available from the websites, which are listed in the list of references of this report.
Espoo, 15. August, 2005 Co-ordinator
Professor Asko Sarja
Analysis methodology in lifetime engineering
Classification of generic requirements Contributor: Asko Sarja
The objective of the integrated and predictive lifetime management is to achieve optimised and controlled lifetime quality of buildings and civil infrastructures in relation to the generic requirements. This objective can be achieved with a performance-based methodology, applying generic limit state approach.
The lifetime quality means the capability of the structures to fulfil the multiple requirements of the users, owners and society in an optimised way during the entire design or planning period (usually 50 to 100 years). The multiple generic requirements are presented in Table 0.1.
Table 0.1 Generic classified requirements of structures and buildings.
1. Human requirements
• functionality in use
2. Economic requirements
• investment economy
• construction economy
• lifetime economy in:
- operation - maintenance - repair
- rehabilitation - renewal - demolition
- recovery and reuse - recycling of materials - disposal
3. Cultural requirements
• building traditions
• life style
• business culture
• architectural styles and trends
4. Ecological requirements
• raw materials economy
• energy economy
• environmental burdens economy
• waste economy
Analysis of lifetime functionality and performance Contributors: Dominique Caccavelli and Manuel Bauer
The deterioration of building elements is a normal consequence of the ageing process. However, a number of parameters like the quality of construction, the climatic conditions, or the lack of maintenance, can greatly influence this process. Considering the fact that the operational costs of a building will grow with time and that problems get worse unless some actions are taken, the necessity for maintaining, refurbishing or upgrading actions becomes evident. Such actions mainly concern the physical and functional building elements, as well as energy consumption and indoor environment quality.
The next diagram shows the possible time evolution of an external coating. The « y » axis represents the deterioration state measured with a discrete code. A “low quality” coating will have a short life (zone in red), and a “high quality” coating will have a long life (zone in green). These zones depend clearly not only on the manufacturing and the materials of the element (ex: synthetic or mineral coating) but also on the maintenance (ex: cleaning, painting) and the environment (ex:
facade exposed to wind and rain).
Without any important change in the element’s environment, the following deterioration process will occur in a delimited zone.
Figure 0.1 Possible time evolution of a building element. Illustration of the “Quality” concept
General model for deterioration
In INVESTIMMO, a predictive model describing the deterioration process of building elements was developed. This model is based on EPIQR, a European diagnosis methodology and software for apartment building refurbishment that was developed in the framework of a previous research programme financed by the European Commission, DG XII (JOR3-CT96- 0044).
The EPIQR method considers 49 elements in a building. Four codes (a, b, c, d) describe the state of deterioration of the elements. A code a means a new or excellent condition element; in d code, the performance of the element is no more assured at the end of its life. b and c are intermediate states.
These codes have been defined accurately for all elements and can be observed during a building survey. A deterioration model should be able to describe the deterioration code of an element for the following years.
Figure 0.2 EPIQR deterioration codes. Example: surroundings, element 1 (French version)
The deterioration process can vary from one to another building. In a building sampling, the time when a deterioration code can be observed delimits intervals: after some time, no more a can be observed. Following this reasoning, one can define probability curves for an element of a building to be in a, b, c or d codes at a given time.
In the next figure, probability curves, built with about 300 INVESTIMMO surveyed buildings are shown. One can see that the element 03 (façade) of a building can be in deterioration code a (blue curve) after 20 years with a probability of 0.6 and in deterioration code b (light blue curve) with a probability of 0.4. After 80 years, the probability to be in d (red curve) code is almost 1.
Figure 0.3 Probability curves calculated for element 03 (facade). Code a (dark blue), code b (light blue), code c (yellow), code d (red)
These probability curves can also be plotted on a single cumulative diagram (Figure 0.4):
Figure 0.4 Cumulative curves calculated for element 03 (facade). Code a (dark blue), code b (light blue), code c (yellow), code d (red)
The y-axis of the diagram can be interpreted as the building quality space (0≤q≤1). If q=0, the element is high quality with a long life span. It will be in d code only after 75 years (beginning of the red zone when y=0). If q=1, the element is low quality, and will be in d code only after 30 years in this example (beginning of the red zone when y=1). The model makes the assumption that the quality of the element remains constant during time.
The knowledge of the quality of an element or at least an interval of quality defines its future evolution of deterioration.
Quality interval based on survey data
During the building survey 1 the following information was collected for each element and type:
- The deterioration code (a, b, c, d)
- The age of the element (duration from last refurbishment action).
This information is used to define the quality interval of the building element.
Figure 0.5 Quality interval estimation
In the above example, the age of the element is of 40 years and the code is b. It defines a value 0,51≤ q ≤0,89: it means that this element is a medium/high quality one.
Quality interval based on other information
Another possibility to fix the quality is to fix the influent parameters (country, type, …) this refinement leads to a narrower zone on the graph.
The user can also fix the quality interval if he has detailed information about an element.
In the model prediction, the three quality intervals (user, country and based on survey information) are merged to calculate a resulting quality interval (see Figure 0.6)
1 In the framework of the INVESTIMMO project, a total of 349 building audits was performed in seven European countries (France, Germany, Italy, Switzerland, Denmark, Poland and Hellas.) The aim of these surveys was to collect the necessary data in order to:
• Analyze the influence of various factors on the building deterioration and to develop correlations between these factors and the deterioration process,
• Construct a European database on building element deterioration,
• Prepare a set of Guidelines on building’s deterioration.
Figure 0.6 Resulting model quality
If no overlap exists (conflict), then the survey observation has priority.
Calculation of future deterioration codes
By means of this graphic, one can build the predictive degradation model of an element. The changes between states are expressed in terms of intervals. These intervals represent the uncertainty of the prediction (Figure 0.7).
Figure 0.7 Time passage estimation for element 03 (facade)
The time passage from a to b, from b to c and from c to d can then be estimated on the above diagram.
On this graph:
- txy_min means minimal time of passage from x to y, i.e. a pessimistic point of view (lowest quality),
- txy_max means maximal time of passage from x to y, i.e. an optimistic point of view (highest quality).
Results are plotted in the next figure (the time is set to zero at the observation).
Figure 0.8 Time passage interval for element 03 (facade)
In a more detailed and probabilistic approach, one can calculate the conditional probability to be at a current state at any time, knowing the deterioration code at a certain time.
Determination of model parameters
The probability curves have been built using survey data (about 300 couples of code and age examples).
Survey data distributions
The survey data give directly the age distribution of the deterioration code for the building population. For example, the probability to have a building (in fact an element of a building) with an age y between x and x+∆x in the code k in a building population is given by:
yk[x,x+ ∆x]= nk[x,x+ ∆x]
k = a, b, c or d
nk[x,x+ ∆x] is the number of code a buildings aged between x and x+∆x and
Nk is the total number of code k buildings
These distributions have been fitted using the beta function and an automatic fitting procedure. This function has been chosen after studying different functions (Gauss, Rayleigh, Weibull) because it is a non-symmetric function, with various shape possibilities and only 4 parameters.
Figure 0.9 Code distributions for element 39 (windows), manual fitting with a Rayleigh function
The fit considers all types for a single element. If the analysis shows different behaviours between types, than the types are separated (four curves for each type). Ideally, all types should be considered separately but the number of examples of each type is too small to lead a relevant statistical analysis.
Probability calculation and cumulative curves
At a given age x, the probability for a building element to be in deterioration code k is given by:
The cumulative curves are derived from the probability curves:
fb(x)= pa(x)+ pb(x)
fc(x)= pa(x)+ pb(x)+ pc(x)
fd(x)=pa(x)+ pb(x)+pc(x)+ pd(x)=1
Figure 0.10 Distribution yk(x) (up), probability pk(x) (middle) and cumulative curves fk(x) (down) Software
The model predicting the evolution of building element degradation has been implemented in a visual basic program. This software has been tested on simple case studies to show that the output is rational. The program has a simple and efficient interface (Figure 0.11).
Figure 0.11 Model implementation in visual basic
Analysis of lifetime economy Contributor: Phil Bamfort
Whole Life Costing is becoming an increasingly common feature in the procurement of built assets.
The Whole Life Cost includes
• Capital costs for construction
• The costs for planned preventative maintenance, reactive maintenance and repairs
• Life cycle costs for replacement of elements
• The operational costs of the asset
• End of life costs which may include demolition or refurbishment
There is much debate about the specific term to be used in defining the lifetime cost of an asset with Whole Life Cost and Life Cycle Costs both being used. In practice in the UK, Life Cycle Costs refer specifically to the replacement costs of specific elements while Whole Life Costs refer to the total cost including the items listed above.
The need for analysis of lifetime economy
A principal reason for the growing interest in WLC is that Governments are demanding better value rather than lowest capital costs and require that tenders include not only the cost of building but also the expected lifetime costs for planned preventative maintenance, replacement and repairs.
Furthermore, procurement methods such as Private Finance Initiative (PFI), Public Private Partnership (PPP) and Build, Own, Operate, Transfer (BOOT) are becoming more popular. Such projects require that the developer maintains responsibility for the asset for a defined contract period (typically 25-30 years) and during that time has contractual responsibility for ensuring that the asset continues to meet its functional requirements by maintaining acceptable serviceability levels. At the end of the contract period the asset is then handed over to the client in a condition that meets the client’s requirements for a defined residual life.
Enlightened clients are also increasingly recognising the benefit of sustainable construction, not only in relation to their own strategic development but also with regard to public opinion, branding and benefits associated with planning applications.
Whole life costing may be used at all the important decision stages in the procurement, construction, use and finally the disposal of the asset, for example:
• the initial investment appraisal or decision whether or not to build or acquire by purchase or lease
• the assessment of feasibility of alternative construction solutions, including replacement and/or maintenance over the life of the asset
• outline design
• detailed design (including choice of components and services)
• tender appraisal
• assessment of variations during the course of construction
• hand-over and final account
• assessment of the effectiveness of the construction (post-occupancy evaluation)
At each decision stage of a project, as additional and more reliable information becomes available, the calculation of the life cycle costs may be refined, to provide increased accuracy of predictions of the life cycle costs of the project. The aim must be to achieve recognition of the optimum whole life cost of the asset, balancing the optimum value, the minimum overall life cycle cost and the maximum functionality.
Undertaking analysis of lifetime economy
Analysis of lifetime economy may be undertaken at various levels. At project inception, strategic decisions are required to determine whether it a project is economically viable. As the process proceeds through concept and detailed design, the amount of information that is needed increases substantially to enable various design options to be evaluated and selected. However, post construction, the focus is less on individual components than on the performance of the systems or spaces created (Figure 0.12).
In an ideal world, a complete database of life cycle information would be available for all of the available building materials and components, and an internationally accepted (and possibly standardised) method for applying these data would be available. In the real world however the data are fragmented, if available at all, and various processes are used to establish the performance, cost and societal and environmental impacts over the life of a built asset.
It is important therefore that whatever data are used they are suitably qualified. The factors used in the ISO 15686 approach for estimating service life provide some guidance requiring the user to apply factors for levels of quality, design and execution (these factors impacting upon the as-built quality) and the conditions of exposure, use and maintenance in service.
Clients brief - feasibility Concept design Detailed design
Strategic level Construction
Residual cost or value
Figure 0.12 Information requirements through the life of an asset
The real skill in WLC lays not so much in the mechanics of the process. The availability of spreadsheets has made it possible for almost anyone with basic mathematical skills and computer literacy to produce a WLC model at a simplistic level and WLC calculators and comparators abound. The required output is usually a prediction of annual costs and a cost profile over the life of the asset leading to the total WLC (Figure 0.13 and 1.14).
0 20 40 60 80 100 120 140
1 6 11 16 21 26
0 100 200 300 400 500 600
0 5 10 15 20 25 30 Years
Cumulative cost negitive cash
flow Estimated cost schedule positive cash flow
Figure 0.13 Predicted annual cost Figure 0.14 Cumulative cost profile
The importance of the cost profile is most apparent for PFI type contracts within which the client expects to pay a fixed annual fee (perhaps linked to inflation). Hence the contractor must be able to spread the costs in a way that is both acceptable to the client and which minimises negative cash flow.
The real difficulty with WLC, particularly at the design level, is in knowing what numbers to put in the appropriate boxes. When are interventions necessary? How much will be spent on PPM and at what rate? And how much will be spent replacing elements when they cease to fulfil the function for which they were designed? The first step in the process of Whole Cycle Costing is, therefore, to predict when events or interventions occur. Costs are then attached to these interventions and a WLC profile may be estimated. So the analysis of lifetime economy starts with the analysis of lifetime functionality and performance (Section 1.2). Without the ability to predict, with some degree of reliability, the performance of the asset and its component parts the process of WLC cannot even begin. ISO 15686 provides an approach to estimation of service life which, although relatively simplistic, at least provides guidance on those factors that are significant.
Dealing with uncertainty
When dealing with an abundance of data, much of which is fraught with uncertainty, the only sure thing is that the predicted performance and hence the predicted cost profile will not be matched in reality. In order to make reasoned decisions about how the risk should be managed we need to know by how much the predicted performance, and hence costs may vary from the estimate. Hence the decision making process must necessarily include an acknowledgement of the uncertainties in both the predictive process and the input data.
Dealing with Net Present Value
A central feature of WLC is the application of Net Present Value. This involves converting future costs to present day value to take account of interest rates and inflation. However, even at modest discount rates, the NPV reduces rapidly, hence making Capital investment for long-term performance unattractive to the developer in simple cost terms (Figure 0.15). For example, at a discount rate of only 4%, the NPV is less than 50% of the cost at 20 years, and ay higher discount rates the NPV reduces even further. On purely economic grounds this makes it more attractive to spend less now and more later.
0 20 40 60 80 100
0 20 40 60
Figure 0.15 The change in Net Present Values with time, expressed as a percentage of current cost Furthermore, the uncertainties associated with predicting changes in future interest and inflation rates can be greater than those attached to predictions of service life. Care should be taken, therefore, when applying discount rates within a WLC calculation.
• Analysis of Lifetime Ecology (LCA) Contributors: Sevket Durucan and Anna Korre
The LIFETIME cluster project LICYMIN focused on life cycle impact assessment (LCIA) of a minerals production operation and developed an LCIA model (LICYMIN) for the minerals industry. This section describes the concept and the methodology involved in Life Cycle Assessment (LCA) as an environmental management tool for all forms of materials and industries and its specific application in the cluster project LICYMIN.
Life Cycle Assessment (LCA)
Life cycle assessment is a “cradle-to-grave” approach for assessing industrial systems. “Cradle-to- grave” begins with the gathering of raw materials from the earth to create the product and ends at the point when all materials are returned to the earth. LCA evaluates all stages of a product’s life from the perspective that they are interdependent, meaning that one operation leads to the next.
LCA enables the estimation of the cumulative environmental impacts resulting from all stages in the product life cycle, often including impacts not considered in more traditional analyses (e.g., raw material extraction, material transportation, ultimate product disposal, etc.).
By including the impacts throughout the product life cycle, LCA provides a comprehensive view of the environmental aspects of the product or process and a more accurate picture of the true environmental trade-offs in product selection. Specifically, LCA is a technique to assess the environmental aspects and potential impacts associated with a product, process, or service, by:
• compiling an inventory of relevant energy and material inputs and environmental releases;
• evaluating the potential environmental impacts associated with identified inputs and releases;
• interpreting the results to help you make a more informed decision.
Figure 0.16 LCA Stages
LCA is a technique for assessing all the inputs and outputs of a product, process, or service (Life Cycle Inventory); assessing the associated wastes, human health and ecological burdens (Impact Assessment); and interpreting and communicating the results of the assessment (Life Cycle
Interpretation) throughout the life cycle of the products or processes under review. The term “life cycle” refers to the major activities in the course of the product’s life-span from its manufacture, use, maintenance, and final disposal; including the raw material acquisition required to manufacture the product. Figure 0.16 illustrates the possible life cycle stages that can be considered in an LCA and the typical inputs/outputs measured.
The LCA process is a systematic, phased approach and consists of four components: goal definition and scoping, inventory analysis, impact assessment, and interpretation as illustrated in Figure 0.17:
1. Goal Definition and Scoping - Define and describe the product, process or activity. Establish the context in which the assessment is to be made and identify the boundaries and
environmental effects to be reviewed for the assessment.
2. Inventory Analysis - Identify and quantify energy, water and materials usage and
environmental releases (e.g., air emissions, solid waste disposal, and wastewater discharge).
3. Impact Assessment - Assess the human and ecological effects of energy, water, and material usage and the environmental releases identified in the inventory analysis.
4. Interpretation - Evaluate the results of the inventory analysis and impact assessment to select the preferred product, process or service with a clear understanding of the uncertainty and the assumptions used to generate the results.
Figure 0.17 LCA framework
Goal definition and scoping is the phase of the LCA process that defines the purpose and method of including life cycle environmental impacts into the decision-making process. In this phase, the following items must be determined: the type of information that is needed to add value to the decision-making process, how accurate the results must be to add value, and how the results should be interpreted and displayed in order to be meaningful and usable.
The LCA process can be used to determine the potential environmental impacts from any product, process, or service. The goal definition and scoping of the LCA project will determine the time and resources needed. The defined goal and scope will guide the entire process to ensure that the most meaningful results are obtained. Every decision made throughout the goal definition and scoping phase impacts either how the study will be conducted, or the relevance of the final results. The following section identifies the decisions that must be made at the beginning of the LCA study and the impact of these decisions on the LCA process.
A life cycle inventory (LCI) is a process of quantifying energy and raw material requirements, atmospheric emissions, waterborne emissions, solid wastes, and other releases for the entire life cycle of a product, process, or activity.
In the life cycle inventory phase of an LCA, all relevant data is collected and organized. Without an LCI, no basis exists to evaluate comparative environmental impacts or potential improvements. The level of accuracy and detail of the data collected is reflected throughout the remainder of the LCA process. Life cycle inventory analyses can be used in various ways. They can assist an organization in comparing products or processes and considering environmental factors in material selection. In addition, inventory analyses can be used in policy-making, by helping the government develop regulations regarding resource use and environmental emissions.
An inventory analysis produces a list containing the quantities of pollutants released to the environment and the amount of energy and material consumed. The results can be segregated by life cycle stage, by media (air, water, land), by specific processes, or any combination thereof.
In 1993, EPA published a guidance document entitled Life-Cycle Assessment: Inventory Guidelines and Principles. In 1995, EPA published Guidelines for Assessing the Quality of Life-Cycle Inventory Analysis. The combination of these two guidance documents provides the framework for performing an inventory analysis and assessing the quality of the data used and the results. The two documents define the following steps of a life cycle inventory:
• develop a flow diagram of the processes being evaluated
• develop a data collection plan
• collect data
• evaluate and report results
Life Cycle Impact Assessment (LCIA) is the third phase of Life Cycle Assessment as described in ISO 14042 and further outlined with examples in ISO TR 14047. The purpose of LCIA is to assess a product system’s Life Cycle Inventory to better understand its environmental significance. It also provides information for the interpretation phase.
The LCIA phase provides a system-wide perspective of environmental and resource issues for product system. It assigns Life Cycle Inventory results via characterization to impact categories.
Characterization of emissions, resources extractions and land use means the aggregation by adequate factors of different types of substances or other interventions in a selected number of environmental issues, or "impact categories" such as resource depletion, climate change, acidification or human toxicity. For each impact category the indicators are selected and the category indicator results are calculated. The collection of these results provides information on the environmental impact of the resource use and emissions associated with the product system. The general framework of the LCIA phase is composed of several mandatory elements that convert LCI results to indicator results. In addition, there are optional elements. The following steps comprise a life cycle impact assessment:
1. Selection and Definition of Impact Categories - identifying relevant environmental impact categories (e.g., global warming, acidification, terrestrial toxicity).
2. Classification - assigning LCI results to the impact categories (e.g., classifying CO2 emissions to global warming).
3. Characterisation - modelling LCI impacts within impact categories using science-based conversion factors. (e.g., modelling the potential impact of CO2 and methane on global warming).
4. Normalisation - expressing potential impacts in ways that can be compared (e.g. comparing the global warming impact of CO2 and methane for the two options).
5. Grouping - sorting or ranking the indicators (e.g. sorting the indicators by location: local, regional, and global).
6. Weighting - emphasising the most important potential impacts.
7. Evaluating and Reporting LCIA Results - gaining a better understanding of the reliability of the LCIA results.
The impacts of the different categories have consequences on the environment and human welfare on different spatial scales. This has nothing to do with the importance of the categories, but with a need of spatial differentiation within the fate and exposure for some impact categories. Since economic processes are spread worldwide, local impacts have a global extension as well. The climate change and the stratospheric ozone depletion are phenomena that affect the whole planet. In principle, this holds true also for the extraction of abiotic and biotic resources. However, not all regions of the world have the same need of all resources. Acidification, nitrifications and photochemical oxidant formation are generally caused by pollutants whose residence time in the atmosphere permits a continental dispersion. The impact categories human and ecotoxicity can be considered to have a regional dimension. Depending on the characteristics of the pollutant and the medium where it is emitted, fate can be considered continental or local. Finally the impacts caused by photo-oxidant formation and land use are totally dependent on the local situation, meteorological conditions and land characteristics. The need for spatial differentiation in the fate and exposure analysis in different impact categories is illustrated in Figure 0.18.
Figure 0.18 Impact categories considered in LCIA
The use of models is necessary to derive the characterisation factors. The applicability of the characterization factors depends on the accuracy, validity and characteristics of the models used.
For most LCA studies no models are needed because existing impact categories, indicators and characterization factors will be selected from available sources. As can be seen in Figure 0.19 models reflect the cause-effect chain (environmental mechanism) of an impact category by describing the relationship between the LCI results, indicators and if possible category endpoint(s), i.e. the receptors that are damaged. For each impact category, the following procedure is proposed in ISO 14042:
Identification of the category endpoint(s).
Definition of the indicator for given category endpoint(s).
Identification of appropriate LCI results that can be assigned to the impact category, taking into account the chosen indicator and identified category endpoint(s).
Identification of the model and the characterization factors.
This procedure facilitates an adequate inventory analysis and the identification of the scientific and technical validity, assumptions, value choices and the degree of accuracy of the model. The resulting indicators may vary in precision among impact categories due to the differences between the model and the corresponding environmental mechanism. The use of simplifying assumptions and available scientific knowledge influences the accuracy of the indicators.
Figure 0.19 The indicator concept according to ISO 14042
Life cycle interpretation is a systematic technique to identify, quantify, check, and evaluate information from the results of the life cycle inventory (LCI) and the life cycle impact assessment (LCIA), and communicate them effectively. Life cycle interpretation is the last phase of the LCA process. The International Organization for Standardization (ISO) has defined the following two objectives of life cycle interpretation:
1. Analyse results, reach conclusions, explain limitations and provide recommendations based on the findings of the preceding phases of the LCA and to report the results of the life cycle interpretation in a transparent manner.
2. Provide a readily understandable, complete, and consistent presentation of the results of an LCA study, in accordance with the goal and scope of the study.
Mining Life Cycle Modelling: Cluster Project LICYMIN
LICYMIN was designed as a minerals production LCA system, functioning on the basis of a cradle- to-gate principle. The objective was to include all production, processing and waste handling activities that usually take place around a mine site. These activities would normally be under the jurisdiction of the mine operator, who would in turn be responsible for the life cycle impact credits and burdens. Figure 0.20 illustrates the unit processes that have been included in the design of the LICYMIN life cycle inventory, and indicates the flow of materials and emissions accounted for in the inventory.
Figure 0.20 The mining life cycle impact assessment system.
The LCA database design was such that all inputs and outputs can be coded at the highest level of detail that is likely to be available to the site operators so that the environmental impacts can be accounted for each compartment they are found in and can be traced back to the unit processes that they stem from. The added benefit of this approach is that it enables the apportionment of the environmental burdens per product when more than one is produced – for example different metal concentrates. This is a significant improvement over the LCA studies carried out on the basis of generic data where the aggregation of processes, and therefore impacts, limits the ability to differentiate and credit the different unit processes with the corresponding environmental impacts.
The LICYMIN inventory database was coded using the flexible object relational database model, which allows for the modification and update of unit processes without disturbing the overall LCA model structure.
Mining methods are broadly categorised as surface and underground methods, depending, to a great extent, on the deposit type and depth. Based mainly on the deposit geometry and the support criteria used, the technology – therefore the number of specialised production operations and the equipment used in each method – varies significantly. Figure 0.21 presents a general overview of the industrial operations involved in surface and underground mining. In the same manner, and depending on the ore type, mineral composition and grade, the technology applied and the equipment used in ore processing varies significantly. Consequently, the nature, volume, treatment and disposal of the waste generated through these unit operations may not be the same.
(a) Surface Mining (b) Underground Mining
Figure 0.21 Generalised mining methods, systems and operation options for Surface and Underground mining in metal ore production
During model development, it was noted that an LCIA that honours the spatial and temporal dimensions of the environmental impacts of mining operations as a whole, and of the system components individually, should provide the user with the option to include the detailed information available at the operational level in the industry. For this purpose, the LICYMIN life cycle inventory database was designed to represent in detail the sub-systems that comprise the mining processes described in Figure 0.20. The operations, activities and sub-activities within each sub- system have been represented at the highest level of detail relevant for the LCA (Figure 0.22). For example, in the case of an open pit mine production loading operation using a rope shovel, the inventory data available would include energy supply, power rating of the shovel, the capital and operating costs of the equipment as inputs and rock (mineral) type and volume produced, discharges to the atmosphere such as PM10, and gaseous emissions together with composition and quantities as outputs.
The LICYMIN inventory
The LICYMIN life cycle inventory was developed using Oracle 9i Enterprise Edition. The object- relational database consists of forty-four object types, forming twenty eight object tables. These object tables were divided into five groups according to their function:
1. Mineral Extraction Group
• Engineering services
• Environmental impacts 2. Mineral Processing Group
3. Waste Handling and Remediation Group 4. Electrical Supply Source Group
5. Life Cycle Impact Assessment Group
Figure 0.22 Ore and waste rock excavation sub-activity details coded in the LICYMIN LCA inventory.
Object tables in groups one, two and three are interconnected to carry out a cradle-to-gate LCA study on a mine site or may be used independently to carry out a specific LCA study which ensures traceability in the system. Groups four and five provide information on environmental impacts due to the use of a particular electricity source and the LCA impact categories, respectively; both groups are widely used by groups one to three. Figure 0.23 shows an example of object table from the LICYMIN database, illustrating the total suspended particles object table within the environmental impacts sub-group.
Figure 0.23 Total suspended particles object table (PM10) within the minerals extraction group under the environmental impacts sub-group.
The interaction between groups one to three, and groups four and five is achieved through the use of sub-programs written in Oracle PL/SQL or the so called member functions. These member functions carry out four types of computations for non-monitored or non-recorded environmental emissions, outputs to downstream unit processes, life cycle impacts due to electricity consumption, and the life cycle impacts of the sub-system in question. The member functions included in the mineral extraction group object tables are designed to calculate:
• Particulate matter less than ten microns (PM10) due to traffic on unpaved roads, drilling, primary crushing, conveying, stockpiling and blasting.
• Total Suspended Particulates (TSP) due to traffic on unpaved roads, drilling, primary crushing, conveying, stockpiling and blasting.
• Nitrogen monoxide (NO), carbon monoxide (CO), unburned hydrocarbons, benzene, PM10 and sulphur dioxide (SO2) due to combustion engines.
• Carbon dioxide (CO2), carbon monoxide (CO), nitrogen dioxide (NO2) and ammonia (NH3) due to blasting.
• Surface and underground mining solid waste volume and mineral composition.
• Feed grade (product).
The member functions listed above allow the LCA practitioner to specify conditions when estimating a particular emission, or use default values stored together with the factors in the environmental impact object tables.
The LICYMIN model allocation and characterisation
In deciding the allocation of the life cycle impacts for the minerals production process, few important considerations have been taken into account in the design of the LICYMIN system.
Mining operations are of a complex nature, involving the multi-use or cascade use of many unit processes. These processes require multiple inputs and produce multiple outputs, and involve both open-loop and close-loop recycling. A faithful representation of these complex links between activities and sub-activities was achieved through the object-relational model developed. The model uses a set of applications and links such that data complexity is maintained, complicated relationships are supported and communication at the highest level of detail is ensured. However, representing the mining system at such a high level of detail has its inherent problems:
1. Many sub-activities may occur at the same time;
2. It may not be possible to acquire sufficient data as inputs and environmental burdens for each sub-activity; and
3. It may not be possible to calculate and assign inputs and environmental burdens to each sub- activity.
To overcome these problems, a further clustering of sub-activities may be necessary, which could provide a meaningful set of inputs and burdens to characterise the (sub)system. The clustering has the advantage of identifying all activities that should be taken into account and grouping them in a coherent manner. For example, preproduction and production operations should be considered when clustering the underground mining system, and for production operations, the development work and ore excavation activities should be accounted for. It is then reasonable to assume that the drilling sub-activity will take place in all these cases. Furthermore, depending on the mining method used, the drilling sub-activity may take place more than once during the development work.
The next step following the allocation process is the LCA characterisation. All impact categories are defined at midpoint level on the basis that this is the “best-practice” for impact assessment. The impact factors stored in the LCA database cover several time horizons, scales and a wide range of compartments; for example:
- the factors that characterise climate change gases cover three time horizons: 500, 100 and 20 years;
- five indicators are used to characterise ecotoxic releases – fresh water aquatic ecotoxicity, marine aquatic ecotoxicity, fresh water sediment ecotoxicity, marine sediment ecotoxicity and terrestrial ecotoxicity. All of these indicators are evaluated for five different compartments:
air, fresh water, seawater, industrial soils and agricultural soils;
- the factors that characterise human toxic releases also have five indicators in five different compartments, and are evaluated for five time horizons: infinity at global scale, 500, 100, 20 and infinity at continental scale. Figure 0.24 shows the details of the Human Toxicity Object Table.
Figure 0.24 Human Toxicity object table.
LICYMIN model implementation at Bakonyi Bauxitbánya kft
The Bakonyi Bauxitbánya Kft. (Bakony Bauxite Mines Ltd.) is the only active bauxite mining company in Hungary. The company’s activities (exploration and exploitation of bauxite) cover an
area of about 150×75 km2 in the northwest of Hungary. The annual production of about 3 Mt of the 70s and 80s decreased to annual levels below 1 Mt in recent years. Some 6 Mt are left in various individual and relatively small occurrences.
At the time of model implementation, there were three underground mines and four open-pits in operation. The recently developed underground mine, and a pair of completed open pits, were selected for data collection and analysis using the LICYMIN model. The Bakonyoszlop open pit mined two small bauxite lenses situated close to one another, using a shared dump area for overburden. After mining, the whole area was reshaped and re-cultivated. The second operation that was used in the model implementation was the Halimba II-DNY underground, mine which is a new planned development and was used as a case study to validate the LICYMIN model and make predictive calculations and scenario analysis.
The data collected on energy and materials from mining operations were collated as the LCA technology matrix. The main objective of the technology matrix was to describe the economic flows per unit process. Once the economic inputs and outputs were in tabular form, each was followed individually until they were attributed to their corresponding environmental interventions. A detail of the technology matrix for the Halimba II-DNY underground mine is shown in Figure 0.25 for a projected production of 1,250 tonnes per day.
Figure 0.25 Detail of the technology matrix for the Halimba II-DNY underground bauxite mine.
The member functions coded in the LICYMIN model were used to calculate the environmental emissions and outputs to downstream unit processes as explained before. Figure 0.26 and Figure 0.27 present two examples of the outputs calculated for the solid waste produced and the estimated gaseous emissions released from diesel combustion in the Bakonyoslop mine. Finally, the combined
effect of all emissions in each impact category was calculated through the life cycle impact assessment group member functions. Figure 0.28 presents the acidification impact category results for the same mine. As illustrated in the acidifying air releases table, it is possible to attribute the environmental impact calculated to each and every sub-activity and output produced/emitted, and trace it back to the specific unit process(es) that generate them.
Figure 0.26 Solid waste produced for the Bakonyoszlop bauxite mine.
Figure 0.27 Gaseous emissions from diesel combustion for Bakonyoszlop mine.
Figure 0.28 Acidification life cycle impact assessment results for the Bakonyoszlop mine.
Finally, the results were normalised at the mid-point level on the basis of the Western Europe 1995 levels. Figure 0.29 illustrates the projected results for the Halimba II-DNY underground mine until the planned end of the mining operations in 2009.
The climate change impacts calculated are solely due to the blasting agents used in the operations.
In terms of eutrophication effects, the highest proportion in this category is due to the nitrogen oxide produced from blasting, occasionally accounting for more than 80% of the total impact in this category. Similarly, acidification is also mainly influenced by the blasting fumes air emissions.
It is worthwhile noting the negative scores in the photo-oxidant formation category, particularly for the surface activities, which are due to the NO production from diesel combustion that induces a negative impact (positive environmental contribution). In the case of underground operations, these negative impacts are eroded by the positive blasting fume impacts that dominate the photo- oxidising air releases (NO2 and CO). Another interesting consideration is that the greatest proportion of the human toxicity effects are due to the PM10 releases that dominate this category over the blasting and diesel emissions which only account for a very small proportion (less than 1%) of the impacts. Finally, the authors feel that the current method used in accounting for the depletion of abiotic resources is inadequate for the purposes of LCA in minerals extraction.
However, this was also included in this study to cover for all baseline categories defined in the ISO 14040.
Figure 0.29 Projected normalised life cycle impact assessment category results for the Halimba II- DNY underground mine.
The LCA model presented in this report was developed through a European Commission funded research project, Contract No: G1RD-CT-2000-00162. The authors wish to thank the Commission, their industrial research partners, and the engineering staff of Bakonyi Bauxitbánya Kft. in particular, for their valuable contributions to the project elements described here.
Predictive models for deterioration of structures - alternative principles of Lifecon methods
Contributor: Asko Sarja
• Alternative models
Three types of degradation models are described in detail, including some examples of application.
These models are:
• Statistical degradation models
• RILEM TC130 CSL models
• Reference Structure model
Characteristic properties of these models are as follows:
• Statistical degradation models are based on physical and chemical laws of thermodynamics, and thus have a strong theoretical base. They include parameters, which have to be determined with specific laboratory or field tests. Therefore some equipment and personnel requirements exist for the users. The application of statistical "Duracrete" method raises need for a statistically sufficient number of tests. Statistical reliability method can be directly applied with these models.
• RILEM TC 130 CLS models are based on parameters, which are available from the mix design of concrete. The asset of these models is the availability of the values from the documentation of the concrete mix design and of the structural design.
• Reference structure model is based on statistical treatment of the degradation process and condition of real reference structures, which are in similar conditions and own similar durability properties with the actual objects. This method is suited in the case of a large network of objects, for example bridges. It can be combined with Markovian Chain method in the classification and statistical control of the condition of structures.
Because of the openness principle of Lifecon LMS, users can select the best suited models for them.
It is sure, that there exist also a lot of other suited models, and new models are under development.
They can be used in Lifecon LMS after a careful validation of the suitability and reliability. Special attention has to be paid to the compatibility of the entire chain of the procedure of reliability calculations.
• Statistical degradation models [Lifecon D3.2]
Statistical degradation models include the mathematical modelling of corrosion induction due to carbonation and chloride ingress, corrosion propagation, frost (internal damage and surface scaling) and alkali-aggregate reaction. Models are presented on a semi-probabilistic and a full-probabilistic level. Semi-probabilistic models only include parameters obtainable throughout structure investigations, without making use of default material and environmental data. Full-probabilistic models are applicable for service life design purposes and for existing objects, including the effect of environmental parameters. For each full-probabilistic model a parameter study was performed in order to classify environmental data.
The application of the models for real structures is outlined. The objects of the case studies have been assessed in order to obtain input data for calculations on residual service life. Each degradation mechanism will be treated separately hereby demonstrating:
• possible methods to assess concrete structures
• the sources for necessary input data
• approach used in durability design
• application of models for existing structures
• the precision of the applied models
• necessary assumptions due to lack of available data
• possible method to update data gained from investigations throughout condition assessment
• default values for input data
• output of the calculations
The use of full-probabilistic models for the calibration of the Markov Chain approach is described.
• RILEM TC 130 CSL models [Lifecon D2.1]
RILEM TC 130 CSL degradation models include a set of selected calculation models consisting of parameters, which are known from mix design and other material properties and ordinary tests.
Therefore these models are usually easy to apply also in cases when no advanced laboratories and equipment are available. The following degradation processes are included in the RILEM TC 130 CSL models:
• Corrosion due to chloride penetration
• Corrosion due to carbonation
• Mechanical abrasion
• Salt weathering
• Surface deterioration
• Frost attack
Degradation affects either the concrete or the steel or both. Usually degradation takes place on the surface zone of concrete or steel, gradually destroying the material. The main structural effects of degradation in concrete and steel are the following:
• Loss of concrete leading to reduced cross-sectional area of the concrete.
• Corrosion of reinforcement leading to reduced cross-sectional area of steel bars. Corrosion may occur
a) at cracks
b) at all steel surfaces, assuming that the corrosion products are able to leach out through the pores of the concrete (general corrosion in wet conditions).
• Splitting and spalling of the concrete cover due to general corrosion of reinforcement, leading to a reduced cross-sectional area of the concrete, to a reduced bond between concrete and reinforcement and to visual unfitness.
• Reference structure models [Lifecon D2.2]
Reference structure degradation prediction is aimed for the use in cases, when the network of objects (e.g. bridges) is so large in number that a sample of them can be selected for a follow-up testing, and these experiences can be used for describing the degradation process of the entire population. The reference structure models are of two types: 1) surface damage and 2) crack damage models. Degradation factors such as frost damage, corrosion of reinforcement, carbonation and chloride penetration may have combined effects that may be of great importance to the service life of a structure. By traditional prediction methods of service life these combined effects are usually ignored. However in computer simulation they can be considered without great theoretical problems. The progress of the depth of carbonation or the depth of critical chloride content is promoted by both the frost-salt scaling of a concrete surface and the internal frost damage of
concrete. The internal frost damage is evaluated using the theory of critical degree of saturation.
The internal damage is evaluated as the reduction of the dynamic E-modulus of concrete.
The condition state (or damage index) of a structure is evaluated using the scale 0, 1, 2, 3, 4. This scale is also used throughout the bridge management system.
The degradation models for both surface damage and crack damage have been programmed on Excel worksheets. The surface damage models describe normal degradation processes on the surfaces of reinforced concrete structures combining the effects of frost-salt attack, internal frost damage attack, carbonation, chloride ingress and corrosion of reinforcement. The crack damage models emulate the processes of depassivation and corrosion at a crack of a concrete structure.
All management systems that include a prediction module, such as Lifecon LMS, need reliable environmental load data. In Lifecon deliverable D4.2 the relevant systematic and requirements for quantitative classification of environmental loading onto structures, as well as sources of environmental exposure data are given. Lifecon D4.2, chapter 6 contains instructions and guidelines for how to characterise the environmental loads on concrete structures on object and network level.
However, these guidelines have to be validated (and possibly adjusted) before they finally can be used in the LMS. In this report the results from the practical validation are summarised. The EN 206-1 system and the standard prEN 13013 have been tested out on the chosen objects and compared with detailed environmental characterisation of the same objects using the available data and methods for environmental characterisation. Such studies have been undertaken in five countries (Norway, Sweden, Germany, Finland and United Kingdom) to develop the needed national annexes for a proper implementation of EN206-1 across Europe.
Strategies and methodologies for developing the quantitative environmental classification system for concrete are given. Those are, firstly, comparative case studies using the new European standard -“EN 206-1 Concrete” and detailed environmental characterisation of the same objects, and secondly, a more theoretical classification based upon parametric sensitivity analysis of the complex Duracrete damage functions under various set conditions. In this way the determining factors are singled out and classified. Such classification systematic is needed to enable sound prediction of service lives and maintenance intervals both on object and network level. This in turn is a necessary prerequisite for change of current reactive practise into a pro-active life-cycle based maintenance management.
• Environmental load parameters [Lifecon D4.1]
The first and general approach to generate data on the degradation agents affecting concrete infrastructures ought to be through utilisation of the climate and weather data normally measured at meteorological sites. This data has to be processed, adapted and modelled to fit into the degradation models. A summary of the needed environmental data is presented in Table 0.1.
Table 0.1 Environmental data for degradation modelling.
RH Temp. CO2 Precipi- tation
Wind Radia- tion
Freeze -thaw cycles
Reinforced concrete Carbonation
induced corrosion X (X) X X X Chloride induced
corrosion X X X X
corrosion X X X X
reaction No model
internal/scaling (X) X X (X) (X) (X) X
Supplementary materials (Dose-response functions) Galvanised
steel/zink coating X X X X X
Coil coated steel X X X X
Sealants/bitumen No function
Polymers No function
Aluminium X X X X
• Quantitative classification of environmental loads [Lifecon D4.2]
The different components are exposed in different ways and different amounts, due to orientation, sheltering, sun/shadow, distance from “source” for exposure, and more, and all this have to be taken into account.
A step-wise characterisation of the environmental parameters onto the surface of the structure is as follows:
1. Choose object
2. Divide the structure/construction into components with different expected Categories of Location (due to orientation, sheltering...). Use either the EOTA system [D4.2 Annexes] or the height classification system [Table 5 in D4.2].
3. Attain EN206-1 exposure classes to the various components/parts of the construction
4. Adjust for the effect of sheltering, etc. on driving rain and deposition on other agents to the structure by calculation of CR, CT, O and W [Chapter 5.6.1 in D4.2].
5. Find climatic information from nearby meteorological stations. Necessary information: