Multi-sensor data fusion system for enhanced analysis of deterioration in concrete structures

Othman Sidek and S.A.Quadri

Collaborative µ-electronic Design Excellence CentreUniversiti Sains Malaysia

Paper presented at PIERS Conference:Progress In Electromagnetic Research Symposium Proceedings, Suzhou, China, Sept. 12-16, 2011, 637

Data fusion Data-fusion is a problem-solving technique based on the idea of integrating many answers to a question into a single; best answer.

Data fusion is defined as process of combining inputs from various sensors to provide a robust and complete description of an environment or process of interest.

It is multilevel, multifaceted process dealing with the automatic detection, association, correlation , estimation, and combination of data and information from single and multiple sources.

Data fusion

“Properly said, fusion is neither a theory nor a technology in its own. It is a concept which uses various techniques pertaining to information theory, artificial intelligence and statistics”

[1] Dave L. Hall and James Llinas, “Introduction to Multisensor Data Fusion”, IEEE , Vol. 85, No. 1, pp. 6 – 23, Jan 1997.

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Multisensor data fusion provides significant advantages over single source data. The use of multiple types of sensors plays an important role in achieving reasonable accuracy and precision.

A novel integrated multi sensor data fusion approach in structural health monitoring is proposed.

The study concerns to find a simple and affordable monitoring strategy for Alkali-aggregate reaction (AAR), which is one of the root causes for structural deterioration in concrete.

AAR Expansion in Concrete Structure

Alkali-aggregate reaction is a term mainly referring to a reaction which occurs over time in concrete between the highly alkaline cement paste and non-crystalline silicon dioxide.

It produces a gel that expands when in contact with the moisture in concrete, and can lead to the development of high tensile stresses and cracking of concrete.

AAR is a serious problem that adversely affects the durability and integrity of concrete structures.

AAR Expansion in Concrete Structure

AAR GEL

AAR Expansion in Concrete Structure

AAR GEL

AAR Expansion in Concrete Structure

AAR GEL

AAR Expansion in Concrete Structure

AAR GEL

Simulating AAR expansion within reasonable laboratory timescale.

Because of the lengthy lead time required to evaluate adequately aggregate sources for potential alkali-aggregate reaction and expansion we use standard accelerated method to accomplish evaluation process.

National Building Research Institute (NBRI) standard testing method is employed to accelerate AAR expansion on four samples which are prepared with different level of alkali concentrations.

Four samples are with different alkali concentrations :

1. Non reactive

2. Marginal reactive

3. Moderately reactive and

4. Very reactive

Hypothetical graph showing expansions (in percentage) in various samples

Hypothetical graph showing expansions (in percentage) in various samples

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Non Reactive Marginal ReactiveModerate ReactiveVery Reactive

Heterogeneous sensor system

Different sensor systems are used at surface and internal level.

• Acoustic sensor system (Pulse-echo Method)

Electro-mechanical system(LVDT Sensors)

• Optical systems

(CCD Camera)

• Embedded sensors (PZT Piezo electric sensors)

Surface Damage Level

Internal Damage Level

Figure showing experimental setup

Features extraction

Data obtained from the various sensors is subjected to feature extraction process.

Feature extraction is a process of removing redundant data and extracting informative data from large set of data.

Multi sensor Data Fusion

The challenge for data fusion is to merge heterogeneous data from acoustic system, electro-mechanical system, optical system and embedded sensors in an efficient way to increase the accuracy and consistency of the acquired data.

Features extracted from heterogeneous sensors are fed to Decentralized Kalman filter.

Decentralized Kalman filter has long been regarded as the optimal solution to many tracking and data prediction tasks and a robust means to fuse heterogeneous data.

Decentralized Kalman filter .The Kalman filter uses a system's dynamics model (i.e., physical laws of motion), known control inputs to that system, and measurements (such as from sensors) to form an estimate of the system's varying quantities (its state) that is better than the estimate obtained by using any one measurement alone. As such, it is a common sensor fusion algorithm.

If raw data obtained sensors is fused it is Centralized Kalman filter.

If processed data (features) are fused is called Decentralized Kalman filter .

If raw data and features are fused it is called Hybrid Kalman filter

Data flow diagram

Artificial neural network (ANN)

The fused global estimates and individual source estimates are fed to artificial neural network (ANN), which characterize and quantify the level of damage.

Multi-layer perceptron (MLP) network is used which is having three layers:

• Input layer

• Hidden layer

• Output layer

ANN is ideally suited to identify non linear system dynamics

A well trained ANN network characterizes and quantifies level of deterioration of the specimen under study.

Expected results :Establishing correlation among surface damage level, internal damage level and the amount of gel concentration in the structure.

Improved accuracy using Multi sensor data fusion

Hypothetical graph showing damage level using single data source sensor system and data fusion system

Hypothetical graph showing correlation between external and internal damage levels.

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Acoustic Sensor

LVDT Sensor

CCD Camera

PZT EmbeddedSensorData fusion

The root-mean-square deviation (RMSD) is used as the damage indexing

Hypothetical graph showing damage level using single data source sensor system and data fusion system

Hypothetical graph showing correlation between external and internal damage level

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Thanks