Preliminary analysis of rock mass weathering grade using image analysis of CIELAB color space with the validation of Schmidt hammer: A case study

Yee, Andrew Lee Kwan; Razali, M.; Ismail, Mohd Nazri; Yusoff, Intan Norsheira; Nagendran, Sharan Kumar; Zainal, Zuraini; Tobe, Hayato; Date, K.; Yokota, Yasuhiro

doi:10.1016/j.pce.2022.103291

Cited by 6 publications

(8 citation statements)

References 19 publications

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“…From Figure 8, Cluster 6 has two outliers that are far from the interquartile range, so, it can be said that the other limitation of this study is the number of segmentation chosen. 10.11113/mjfas.v20n3.3344 The 𝒂 * and 𝒃 * values from Table 3 are presented in radar charts in Figure 9. This sample has a value of 𝒂 * and 𝒃 * which are 9.43 and 27.37 respectively from the previous research by Lee et al [10].…”

Section: Resultsmentioning

confidence: 99%

“…10.11113/mjfas.v20n3.3344 The 𝒂 * and 𝒃 * values from Table 3 are presented in radar charts in Figure 9. This sample has a value of 𝒂 * and 𝒃 * which are 9.43 and 27.37 respectively from the previous research by Lee et al [10]. Moreover, the sample is described as slightly weathered.…”

Section: Resultsmentioning

confidence: 99%

“…Meanwhile, in 2017, Bruce Lindbloom [11] presents matrices and formulations to convert RGB to XYZ color space and vice versa. Then, in order to match the information from the colorimeter data and Schmidt hammer rebound value, Lee et al [10] analyzed natural slope images. By utilizing colorimeter data for calibration, the slope with high values for 𝒂 * and 𝒃 * from CIELAB color space was thought to be weathered.…”

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: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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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“…Disintegration: Limestone in Batu Caves disintegrates (Fig. 3a) as a result of physical forces such as the expansion and contraction of rock induced by temperature variations, resulting in the formation of cracks and fissures (Lee et al, 2023). In regions with periodic freezing and thawing cycles, water can permeate limestone, freeze, and expand, exerting pressure on the rock and causing its fragmentation (Camuffo, 1995).…”

Section: Physical Weatheringmentioning

confidence: 99%

Comprehensive Study on Limestone Weathering Grade and Risk Potential in Batu Cave, Malaysia.

Kambakhsh,

Nik Daud

2024

IEMJ

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The limestone formations within Batu Cave, situated in the Selangor region represent a significant geological phenomenon in Malaysia, capturing the attention of engineers due to their distinctive karst features. The assessment entails a comprehensive review of geological characteristics, currently employed categorization frameworks, assessment approaches, and the possible ramifications for the construction industry, infrastructure advancement, and the natural surroundings. This study aims to enhance the collective comprehension of limestone weathering and its impact on risk assessment due to weathered processes. The insights gained from the distinctive circumstances surrounding Batu Cave possess the capacity to guide optimal approaches and tactics for addressing the consequences of limestone weathering. The Batu Caves attract a substantial number of tourists annually. Consequently, it is subject to significant levels of human activity and encounters high traffic volumes. Moreover, Batu Cave encompasses an assemblage of Hindu temples and shrines, so setting it apart from numerous other limestone formations due to its cultural and religious significance. The impact of urban environments on weathering processes differs significantly from that observed in limestone formations found in other geographical areas. based on the geological landscape of Batu cave effects reveal moderate stability in sections 1, 3, and 8, while sections 6 and 7 pose an elevated risk of limestone collapse, emphasizing the need for efficient risk mitigation measures for the cave's preservation. The parts that have been assessed as risk areas need risk reduction techniques such as taking nets and giving strong foundations and installing danger signs so that people who come to the area are safe. This analysis provides a deeper understanding of limestone weathering in Batu Cave, emphasizing the need for efficient risk mitigation techniques to address weathering issues. This could lead to the preservation of Batu Cave's geological significance and potentially global applications for limestone-rich places. The study contributes to engineering, construction, and environmental conservation by providing valuable resources for decision-making.

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“…For each image, six key features were extracted: mean and standard deviation for each channel of the CIELAB color space (L, a, b). This color space could mimic how human perceive colors and ensures device independence to get consistency in analysis [24]. The neighborhood size (k) was determined by using k-fold-cross-validation to get a more balanced result [25].…”

Section: ) Knn Developmentmentioning

confidence: 99%

Automated Anaemia Detection From Conjunctiva Images : A Machine Learning Approach for Android Application

Henry,

Sumihar,

Maedjaja

2024

jipi. jurnal. ilmiah. penelitian. dan. pembelajaran. informatik

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Anaemia is a common global disorder condition in which the red blood cell count is lower than normal. Traditional diagnostic methods often prove costly, invasive, and inaccessible, leading to delays in treatment and severe consequences. This study explores non-invasive techniques leveraging smartphone technology for efficient anaemia detection. Some researcher investigated that eye’s conjunctiva analysis can a viable alternative, considering its rich blood vessel network and sensitivity to anaemia indicators, independent of skin color. Utilizing smartphone cameras, the study establishes a robust correlation between the color of the conjunctiva and anaemia status, offering a promising avenue for non-invasive diagnosis. Employing a hybrid methodology, the study integrates You Only Look Once (YOLO) version 8 for efficient object detection, achieving a mean average precision of 96% in extracting Regions of Interest (ROI) from conjunctiva images. Subsequently, K-Nearest Neighbors (KNN) classification of features extracted from these ROI’s attained an 83% accuracy rate in determining anaemia status. Furthermore, the study aims to extend its impact by developing an Android application using the Flutter framework. This application integrates the established YOLO and KNN approaches, enabling real-time anaemia detection through smartphone cameras. Such a tool holds the potential to facilitate early evaluations in resource-constrained regions, enabling timely diagnosis and intervention, thus mitigating the adverse effects of untreated anaemia.

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Preliminary analysis of rock mass weathering grade using image analysis of CIELAB color space with the validation of Schmidt hammer: A case study

Cited by 6 publications

References 19 publications

Analysis of Rock Mass Weathering Grade Using Image Processing Technique

Analysis of Rock Mass Weathering Grade Using Image Processing Technique

Comprehensive Study on Limestone Weathering Grade and Risk Potential in Batu Cave, Malaysia.

Automated Anaemia Detection From Conjunctiva Images : A Machine Learning Approach for Android Application

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