Quantitative Weathering Assessment of Rock Slope using CIELAB Color Space and Image Analysis Technique

Razali, M.; Jamal, Muhd Herman; Ismail, Mohd Nazri; Yusoff, Intan Norsheira; Nagendran, Sharan Kumar; Zainal, Zuraini; Zainuddin, Nurul Eilmy; Tobe, Hayato; Miyoshi, Takako; Date, K.; Yokota, Yasuhiro

doi:10.21203/rs.3.rs-937366/v1

Cited by 1 publication

(4 citation statements)

References 34 publications

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“…Since the proposed study are located in the same country which has the same tropical environment, it can be assumed the type of rock are the same hence the same classification of the rock mass can be used in the study. However, the classifications mentioned by Razali et al [15] were only three grades which are unweathered, slightly weathered and highly weathered.…”

Section: Data Visualizationmentioning

confidence: 93%

“…The range from unweathered to weathered regions was determined by referring to the work of Razali et al [15]. This study was conducted in Lebuhraya Kuala Kubu Bharu-Teranum-Raub which located in Peninsular Malaysia.…”

Section: Data Visualizationmentioning

confidence: 99%

“…Moreover, the weathered class is determined to have values that are the highest among all of the classes, hence, the range of the weathered class in weathering grade is higher than highly weathered. It is important to note that the value taken from Razali et al [15] is the upper bound for each class except for the weathered class.…”

Section: Data Visualizationmentioning

confidence: 99%

“…Rock masses exposed to the elements such as sunlight and water steadily change over time, gradually covering their surface in weathering layers. Rock weathering is caused by physical disintegration, chemical deterioration, and biological factors [15]. Meanwhile, a new measurement system based on Structure from Motion photogrammetry had been proposed to evaluate tunnel faces, which aims to determine the tunnel faces discontinuity patterns and orientations for the tunnel face stability evaluation [20].…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Rock Mass Weathering Grade Using Image Processing Technique

Mohamad Nasir,

Misro

2024

Mal. J. Fund. Appl. Sci.

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Image processing techniques refer to the process of converting an image into a digital format and then performing various operations on it to extract useful information. In this study, image processing technique has been used to categorize rock masses according to its weathering grades. The pixel values of the sample images were in the form of RGB color space before being converted to CIELAB color space. The conversion uses ° as the illuminant. The value in CIELAB color space represents the green-red opponent colors, with negative values for green and positive values for red. In contrast, the value represents the blue-yellow opponents with negative values for blue and positive values for yellow. From the values of and of the samples, clustering was used to classify the samples. This method will group the and values into seven clusters according to the closest distance between the values and the centroids. The proposed study can differentiate between the rock mass and rejected clusters containing plants and painted numbers on the rock. The painted number is placed in a rejected cluster due to the inability to determine the exact color of the rock, thereby impacting the data accuracy. The results have been discussed, and the rock masses have been categorized based on weathering grade. Several limitations have been identified, such as the presence of shadows in the sample images and the lack of arrangement of outcome images according to their a* and b* values. This research has also been validated and compared with previous studies. The JudGeo software utilized in prior research required human input to manually estimate suitable a* and b* values, whereas the proposed method automatically computes these values during color space conversion of the sample. Additionally, the proposed method can calculate the percentage of each cluster, facilitating the classification of rock mass into its respective weathering grade.Image processing techniques refer to the process of converting an image into a digital format and then performing various operations on it to extract useful information. In this study, image processing technique has been used to categorize rock masses according to its weathering grades. The pixel values of the sample images were in the form of RGB color space before being converted to CIELAB color space. The conversion uses ° as the illuminant. The value in CIELAB color space represents the green-red opponent colors, with negative values for green and positive values for red. In contrast, the value represents the blue-yellow opponents with negative values for blue and positive values for yellow. From the values of and of the samples, clustering was used to classify the samples. This method will group the and values into seven clusters according to the closest distance between the values and the centroids. The proposed study can differentiate between the rock mass and rejected clusters containing plants and painted numbers on the rock. The painted number is placed in a rejected cluster due to the inability to determine the exact color of the rock, thereby impacting the data accuracy. The results have been discussed, and the rock masses have been categorized based on weathering grade. Several limitations have been identified, such as the presence of shadows in the sample images and the lack of arrangement of outcome images according to their a* and b* values. This research has also been validated and compared with previous studies. The JudGeo software utilized in prior research required human input to manually estimate suitable a* and b* values, whereas the proposed method automatically computes these values during color space conversion of the sample. Additionally, the proposed method can calculate the percentage of each cluster, facilitating the classification of rock mass into its respective weathering grade.

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Section: Data Visualizationmentioning

confidence: 93%

Section: Data Visualizationmentioning

confidence: 99%

Section: Data Visualizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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