Deterioration Assessment of Infrastructure Using Fuzzy Logic and Image Processing Algorithm

Abstract

The safety and serviceability of civil infrastructures have to be ensured either as part of a periodic inspection program or immediately following a given hazardous event. The use of digital imaging techniques to identify the deformed or deteriorated surfaces of structures is a substantial area of research and aims to investigate a number of unknown parameters, including damage quantification and condition rating. This manuscript illustrates the integration of previously developed fuzzy logic-based decision-making tools with the currently developed image processing algorithm to quantify the damage for the condition rating of civil infrastructures. The proposed integrated framework exploits visual specifics of different elements of the infrastructure to perform automated evaluation of structural anomalies such as cracks and surface degradation. Two different image segmentation tools, (1) bottom hat transform and (2) hue, saturation, color (HSV) thresholding, are applied to identify the surface defects. The developed image processing software is used with the fuzzy set framework proposed in the previous research to gauge the damage indices due to various deterioration types like corrosion, alkali aggregate reaction, freeze-thaw attack, sulfate attack, acid attack or loading, fatigue, shrinkage, and honeycombing. Case studies of a long-span bridge and a warehouse building are illustrated for concept validation. The refined comprehensive method is presented as a graphical user interface (GUI) to facilitate the real-time condition assessment of civil infrastructures.

A fuzzy logic expert system for selecting optimal and sustainable life cycle maintenance and rehabilitation strategies for road pavements

“…Fuzzy logic has successfully been applied to decision-making in different fields of engineering ( In the pavement management field, FLS have been applied to determine maintenance needs, deterioration and maintenance timing (Chen and Flintsch 2007, Moazami et al 2011, Pragalath et al 2018. Moazami et al (2011) applied a prioritisation system based on fuzzy logic to determine maintenance needs at the network level.…”

“…Moazami et al (2011) applied a prioritisation system based on fuzzy logic to determine maintenance needs at the network level. A recent application developed by Pragalath et al (2018) combined fuzzy logic and image processing to assess infrastructure deterioration. Similarly, Chen and Flintsch (2007) applied fuzzy logic in combination with probabilistic life-cycle cost analysis to determine the timing of M&R.…”

Identifikasi Degradasi Mutu Beton Pada Struktur Bangunan Gedung Di Jakarta

“…Mitra, et al (2010), Jain dan Bhattacharjee (2012), Tirpude, et al (2014), Pragalath, et al (2018) melakukan penilaian kondisi beton dengan menggunakan enam rating yang terdiri dari rating tertinggi (kondisi yang tidak memerlukan perbaikan) hingga rating terendah (kondisi yang harus segera dilakukan tindakan). Pembagian rating ditentukan berdasarkan prioritas perbaikan beton.…”

Deep Learning for Detecting Building Defects Using Convolutional Neural Networks

“…Whilst the latter is time-consuming and not suitable for quantitative analysis, image analysis-based detection techniques, on the other hand, can be quite challenging and fully dependent on the quality of images taken under different real-world situations (e.g., light, shadow, noise, etc.). In recent years, researchers have experimented with the application of a number of soft computing and machine learning-based detection techniques as an attempt to increase the level of automation of asset condition inspection [13,14,15,16,17,18,19,20]. The notable efforts include; structural health monitoring with Bayesian method [13], surface crack estimation using Gaussian regression, support vector machines (SVM), and neural networks [14], SVM for wall defects recognition [15], crack-detection on concrete surfaces using deep belief networks (DBN) [16], crack detection in oak flooring using ensemble methods of random forests (RF) [17], deterioration assessment using fuzzy logic [18], defect detection of ashlar masonry walls using logistic regression [19,20].…”