Service Life and Durability of Building Materials and Components

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Background

Since the UN Conference on Environment and Development (UNCED) that was held in Rio de Janeiro, Brazil in 1992 and resulted in an agenda for global sustainable development, Agenda 21, “there has been an ever-increasing focus on the needs to determine durability and service life of materials, components, installations, structures and buildings based on the following two important aspects:  Environmental issues – scarcity of material and energy resources and the building and construction sector as a big consumer of these resources, and the environmental impact caused by buildings.

Problem Statement

The global importance of and need for sustainable socio-economic development demand an informed decision-making process from the built environment. Resources and non-renewable resources in particular, should be used as responsible and best possible to ensure optimum service life and life cycle costs. Optimum service life and life cycle costs depend on the ability to quantify (calculate) the changes in condition of building fabric and components over time in any given physical and operational environment.

Hypotheses

Hypothesis No. 1:
The Markov Chain can be used to calculate the estimated service life of a building or component, quantify changes in condition over time and determine the effect of maintenance levels on service life.
Hypothesis No. 2:
The limited availability of historic performance data on degradation of building materials can be supplemented with expert knowledge and reasoning, to develop transition probability matrices for the Markov Chain.
Hypothesis No. 3:
Expert knowledge and reasoning can be expressed in terms of ‘IF-THEN’ rules, and translated into probability values for the transitional probability matrices of the Markov Chain through the application of Fuzzy Logic Artificial Intelligence.
Hypothesis No. 4:
The reduction in service life due to inappropriate maintenance levels, deferred maintenance and
maintenance budgets cuts can be quantified through the application of the proposed Markov Chain
model.

Objective of the Thesis

The objective of this thesis is to develop a model, which translates expert knowledge and reasoning into probability values through the application of Fuzzy Logic Artificial Intelligence to supplement limited historical performance data on degradation of building materials for the development of Markov Chain transitional probability matrices to predict service life, condition changes over time, and consequences of maintenance levels on service life of buildings.

THESIS SUMMARY
ABSTRACT
ACKNOWLEDGEMENTS
1. INTRODUCTION
1.1. Background
1.2. Problem Statement
1.3. Hypotheses
1.4. Objective of the Thesis
1.5. Scope of the Thesis
1.6. Methodology
1.7. Terminology and Abbreviations
1.7.1. Terminology
1.7.2. Abbreviations
1.8. Organisation of the Thesis
2. LITERATURE REVIEW
2.1. Introduction
2.2. Sustainable Development
2.3. Service Life and Durability of Building Materials and Components
2.4. Degradation
2.4.1. Degradation Agents
2.4.2. Climate
2.4.3. Evaluation of degradation
2.5. The Factor Method
2.6. The Markov Chain
2.6.1. Transitional Probability
2.6.2. Application of the Markov model
2.7. Artificial Intelligence Applications
2.7.1. Introduction
2.7.2. Fuzzy Logic
2.7.3. Artificial Neural Networks (ANN)
2.7.4. Neuro-Fuzzy Systems
2.7.5. Examples of relevant Artificial Intelligence Applications
3. RESEARCH METHODOLOGY
3.1. Introduction
3.2. The Degradation of Building Materials and Components
3.2.1. Introduction
3.2.2. The Degradation Process of Building Materials and Components
3.2.3. Durability Factors
3.2.4. Degradation Factors
3.2.5. Condition
3.2.6. Degradation Rate
3.2.7. Condition ratings and assessment consistency
3.3. The Application of Artificial Intelligence to Simulate the Degradation Process
3.3.1. Introduction
3.3.2. Selection of an appropriate Artificial Intelligence system
3.3.3. Fuzzy Logic
3.3.4. Fuzzy Sets
3.3.5. Fuzzy Rules
3.4. Development of Transition Probability Matrices for the Markovian Model
3.4.1. Introduction
3.4.2. Neuro-fuzzy model
3.4.3. Transition from Artificial Intelligence to Markov Chain
3.4.4. Calibration of the neuro-fuzzy model
3.5. The Prediction of Service Life for Buildings and Components
3.5.1. Introduction
3.5.2. Service Life Prediction
3.6. Other Applications
3.7. Summary of Methodology
4. RESULTS AND DISCUSSION
4.1. Introduction
4.2. Results
4.3. Discussion
4.3.1. Hospitals
4.3.2. Maintenance level
4.3.3. Service life prediction
4.3.4. Current Regime vs Proposed Model
5. CONCLUSIONS AND RECOMMENDATIONS
5.1. Introduction
5.2. Conclusions
5.2.1. Conclusion No. 1
5.2.2. Conclusion No. 2
5.2.3. Conclusion No. 3
5.2.4. Conclusion No. 4
5.3. Contribution to Knowledge Base of Engineering Science and Practice
5.4. Recommendations
REFERENCES

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