Book Description

Management of concrete structures requires an understanding of the deterioration processes involved and the rate at which they proceed.

Intelligent monitoring is automated monitoring which explicitly provides information on current condition and deterioration rates to assist in predicting the remaining life of a component or structure. Surface mounted or embedded sensors may be used to monitor various aspects of structural condition, reinforcement corrosion, and the environment in and around a concrete structure.

126 pages

Contents: Summary, Acknowledgements, Foreword, List of figures, List of tables, 1.1 Background, 1.2 How to use this guide, 1.3 The DTI reports, 2 The role of intelligent monitoring in the management of concrete structures, 2.1 Residual life prediction, 2.2 Assessment of current condition and cause of deterioration, 2.3 Definition of limit state(s), 2.4 Assessment of rate of deterioration, 3 Automated monitoring, 3.1 Introduction, 3.2 Planning and operating a monitoring system, 3.2.1 Purpose of monitoring, 3.2.2 Variables measured, 3.2.3 Sensor/equipment selection, 3.2.4 Number, location and installation of sensors, 3.2.5 Data management, 3.2.6 Maintenance, 3.2.7 Health and safety, 3.2.8 Other sources of information, 3.3 Structural change, 3.3.1 Introduction, 3.3.2 Strain, 3.3.2.1 General, 3.3.2.2 Mechanical strain gauge, 3.3.2.3 Electrical resistance strain gauge, 3.3.2.4 Vibrating wire (acoustic) strain gauge, 3.3.2.5 Optical fibre strain gauge, 3.3.2.6 Strain gauges compared, 3.3.3 Crack/displacement, 3.3.3.1 General, 3.3.3.2 Mechanical gauge, 3.3.3.3 Inductive displacement transducer, 3.3.3.4 Electrical resistance crack propagation gauge, 3.3.3.5 Electrolytic tilt sensor, 3.3.3.6 Lasers, 3.3.3.7 Crack/displacement monitoring gauges based on various sensor types, 3.3.3.8 Crack/displacement gauges compared, 3.3.4 Stress, 3.3.5 Acoustic emission 3.3.6 Acceleration/vibration, 3.3.7 Methods under development, 3.4 Reinforcing steel corrosion, 3.4.1 Introduction, 3.4.2 Half-cell potential, 3.4.3 Concrete resistivity, 3.4.4 Galvanic current, 3.4.5 Linear polarisation resistance, 3.4.6 Electrical resistance, 3.4.7 Electrochemical noise, 3.4.8 Methods under development, 3.4.9 Corrosion monitoring methods compared, 3.5 Concrete temperature, 3.5.1 Introduction, 3.5.2 Thermocouples, 3.5.3 Resistance thermometers, 3.5.4 Thermistors, 3.5.5 Fibre optic sensors, 3.5.6 Temperature sensors compared, 3.6 Concrete moisture state, 3.6.1 Introduction, 3.6.2 Humidity, 3.6.2.1 General, 3.6.2.2 Capacitive sensor, 3.6.2.3 Dew point sensor, 3.6.2.4 Wood/brick resistance sensor, 3.6.2.5 Fibre optic sensor, 3.6.3 Concrete resistivity, 3.7 Concrete chemistry, 3.7.1 Introduction, 3.7.2 Chloride, 3.7.3 pH, 3.8 Exposure environment, 3.8.1 Introduction, 3.8.2 Water and soil, 3.8.3 Air, 3.8.4 Earthquake, 3.9 Data management, 3.9.1 Introduction, 3.9.2 Data logging, 3.9.3 Data transmission, 3.9.4 Computer software .54 3.9.5 Integrated automated monitoring systems, 4 Predicting remaining life based on monitoring data, 4.1 Introduction, 4.1.1 Deterioration mechanisms considered, 4.1.2 Availability of service life models, 4.2 Suitability of models for use with automated monitoring, 4.3 Reinforcing steel corrosion initiation, 4.3.1 Introduction, 4.3.2 Corrosion initiation, 4.3.3 Chloride penetration into concrete, 4.3.4 Carbonation of concrete, 4.3.5 Service life models limited for use with automated monitoring, 4.3.6 Service life models most suited for use with automated monitoring, 4.3.6.1 Corrosion front prediction, 4.3.6.2 Direct monitoring of contaminants, 4.4 Reinforcing steel corrosion propagation, 4.4.1 Introduction, 4.4.2 Rate of reinforcement corrosion, 4.4.3 Cover cracking and loss of bond models, 4.4.3.1 Cracking, 4.4.3.2 Loss of bond, 4.4.4 Structural performance, 4.4.4.1 Failure limit states, 4.4.4.2 Structural service life models, 4.4.5 Monitoring strain as inputs to simplified structural models, 4.5 Concrete deterioration, 4.5.1 Introduction, 4.5.2 Failure limit states, 4.5.3 Deterioration mechanisms, 4.5.3.1 Alkali-aggregate reaction, 4.5.3.2 Sulphate attack, 4.5.3.3 Freeze-thaw attack, 4.5.3.4 Leaching, 4.5.3.5 Acid attack, 4.5.3.6 Abrasion, 4.5.4 Service life models limited for use with automated monitoring, 4.5.5 Predictions based on expansion monitoring, 4.5.5.1 Alkali-silica reaction, 4.5.5.2 Sulphate attack, 4.5.5.3 Freeze-thaw attack, 4.5.5.4 Restraint provided by reinforcement, 4.5.5.5 Discussion, 4.6 Use of monitoring data with statistical techniques, 4.6.1 Introduction, 4.6.2 Sources of uncertainty, 4.6.3 Reliability theory, 4.6.3.1 Estimation of structural reliability, 4.6.3.2 Specification of target reliability indices, 4.6.4 Bayesian updating, 4.6.4.1 Application of Bayesian updating to intelligent monitoring, 4.6.5 Spatial variability of deterioration mechanisms, 4.6.5.1 Sources of spatial variability, 4.6.5.2 Spatial correlation structure, 4.6.5.3 Random field analysis, 4.6.5.4 Application of spatial analysis in service life prediction, 4.6.5.5 Intelligent monitoring within the context of spatial analysis, 4.7 Concluding remarks, 5 Case studies, 6 Looking to the future, 6.1 Drivers for more widespread monitoring, 6.2 Trends, 6.3 Areas meriting further work, 7 References