Automated joint faulting measurement based on full-lane 3D pavement surface data

2021 ◽  
Vol 128 ◽  
pp. 103221
Author(s):  
Allen A. Zhang ◽  
Guangwei Yang ◽  
Kelvin C.P. Wang ◽  
Baoxian Li ◽  
Haiwang Kong ◽  
...  
Keyword(s):  
Pavement Surface ◽  
Surface Data

Automated Asphalt Pavement Raveling Detection and Classification using Convolutional Neural Network and Macrotexture Analysis

2021 ◽  
pp. 036119812110054
Author(s):  
Yung-An Hsieh ◽  
Yichang (James) Tsai
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Asphalt Pavement ◽  
Training Data ◽  
Data Driven ◽  
Pavement Surface ◽  
Surface Data ◽  
Proposed Model ◽  
Combining Data ◽  
Pavement Distresses

Raveling is one of the most common asphalt pavement distresses. The survey of its condition is required for transportation agencies to ensure roadway safety and appropriately apply preservation and rehabilitation treatments. However, the traditional raveling condition survey, including the determination of the raveling severity, is typically manually conducted by in-field visual inspection methods that are time consuming, labor intensive, and error prone. Although automated raveling detection and severity classification models have been developed, these existing models have shortcomings. Therefore, there is an urgent need to develop a more accurate and reliable model to automatically detect and classify raveling. This study proposes the first convolutional neural network (CNN)-based model for automated raveling detection and classification. Compared with general CNNs, the proposed model combines the data-driven features learned from training data and macrotexture features of 3D pavement surface data to achieve better performance. The proposed model was evaluated and compared with existing machine learning models using real-world 3D pavement surface data collected from the state of Georgia, U.S. By combining data-driven features with macrotexture features, the proposed model achieved the highest accuracy of 90.8% on raveling classification. The proposed model also achieved classification precision and recall higher than 85% for all raveling severity levels, which is more accurate and robust than existing models. It is concluded that, with multi-type features extraction and proper model design, the proposed model can provide more accurate and reliable predictions for raveling detection and classification.

New Pavement Performance Indicators using Crack Fundamental Elements and 3D Pavement Surface Data with Multiple-Timestamp Registration for Crack Deterioration Analysis and Optimal Treatment Determination

2020 ◽  
Vol 2674 (7) ◽  
pp. 115-126
Author(s):  
Yichang (James) Tsai ◽  
Zhongyu Yang
Keyword(s):  
Performance Indicators ◽  
Temporal Analysis ◽  
Intersection Point ◽  
Optimal Treatment ◽  
Pavement Performance ◽  
Optimal Timing ◽  
Great Opportunity ◽  
Pavement Surface ◽  
Surface Data ◽  
Spatial Temporal Analysis

With the availability of pavement distress information with high granularity, there is a great opportunity to develop and apply new pavement performance indicators, including crack length, width, intersection point, and polygon, derived from crack fundamental elements (CFEs), to study pavement behavior and determine the optimal timing of treatments. Using CFEs and 3D high-resolution pavement surface data, we can study real-world crack deterioration behavior and correlate these new performance indicators to determine optimal maintenance and rehabilitation (M&R) method and timing (e.g., crack filling/sealing) to take full advantage of these 3D pavement surface data. This paper presents a proposed methodology to explore this opportunity. The proposed methodology consists of the following steps: (1) multiple-timestamp 3D pavement data registration, (2) new pavement performance indicators extraction from CFEs, (3) spatial–temporal analysis of new pavement performance indicators, and (4) optimal treatment and timing determination using the proposed spatial–temporal analysis of new pavement performance indicators (e.g., optimal crack filling/sealing timing and location). A case study using 6 years of 3D pavement surface data collected using 3D laser technology on SR-26 in Savannah, Georgia, was conducted to evaluate the feasibility of using the new pavement performance indicators generated by the proposed methodology. The outcomes demonstrate the proposed method is very promising for quantifying and planning M&R treatments (e.g., crack filling/sealing), which has previously been very difficult to achieve. Results also show that multiple-timestamp registration is a very crucial step in ensuring the consistent measurement of regions of interest for different years.

The Grobogan Mud Volcano Complex: An Identification to Reveal the Opportunity of Hydrocarbon Exploration

2020 ◽  
Author(s):  
C. Jatu
Keyword(s):  
Water Analysis ◽  
Middle Eocene ◽  
Research Result ◽  
Hydrocarbon Exploration ◽  
System Model ◽  
Mud Volcano ◽  
Hydrocarbon Potential ◽  
Mud Volcanoes ◽  
Central Java ◽  
Surface Data

Mud volcanoes in Grobogan are referred as the Grobogan Mud Volcanoes Complex in Central Java where there is evidence of oil seepages. This comprehensive research is to determine the characteristics and hydrocarbon potential of the mud volcanoes in the Central Java region as a new opportunity for hydrocarbon exploration. The Grobogan Mud Volcano Complex consists of eight mud volcanoes that have its characteristics based on the study used the geological surface data and seismic literature as supporting data on eight mud volcanoes. The determination of geological surface characteristics is based on geomorphological analysis, laboratory analysis such as petrography, natural gas geochemistry, water analysis, mud geochemical analysis and biostratigraphy. Surface data and subsurface data are correlated, interpreted, and validated to make mud volcano system model. The purpose of making the mud volcanoes system model is to identify the hydrocarbon potential in Grobogan. This research proved that each of the Grobogan Mud Volcanoes has different morphological forms. Grobogan Mud Volcanoes materials are including muds, rock fragments, gas, and water content with different elemental values. Based on this research result, there are four mud volcano systems models in Central Java, they are Bledug Kuwu, Maesan, Cungkrik, and Crewek type. The source of the mud is from Ngimbang and Tawun Formation (Middle Eocene to Early Miocene) from biostratigraphy data and it been correlated with seismic data. Grobogan Mud Volcanoes have potential hydrocarbons with type III kerogen of organic matter (gas) and immature to early mature level based on TOC vs HI cross plot. The main product are thermogenic gas and some oil in relatively small quantities. Water analysis shows that it has mature sodium chloride water. This analysis also shows the location was formed within formations that are deposited in a marine environment with high salinity. Research of mud volcanos is rarely done in general. However, this comprehensive research shows the mud volcano has promising hydrocarbon potential and is a new perspective on hydrocarbon exploration.

Smoothing Surface Data by Spline Functions.

1985 ◽  
Author(s):  
P. C. Stein ◽  
W. L. White
Keyword(s):  
Spline Functions ◽  
Surface Data

Prediction based on response surface data obtained with random blocking

Test ◽  
1994 ◽  
Vol 3 (2) ◽  
pp. 87-99
Author(s):  
Irwin Guttman ◽  
Ulrich Menzefricke
Keyword(s):  
Response Surface ◽  
Surface Data
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