A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Field Trial to Rapidly Classify Soil Using Computer Vision with Electric Resistivity and Soil Strength
https://orcid.org/http://orcid.org/0000-0003-0899-7012
In Singapore, large volumes of excavated soils from the construction industry are sustainably re-purposed in land reclamation projects as fill material. The excavated soils are trucked to Staging Ground (SGs), where they are received and categorized into two broad groups (“Good Earth” and “Soft Clay”). The soil type categorization is traditionally done using properties such as particle size distribution (PSD) and water content (w). However, due to the heterogeneity of soils and non-uniform mixing during the excavation and truck loading process, the actual excavated soils in each truckload received at the SGs may vary. As such, visual checks of each truck are presently implemented at the SGs, which is labour-intensive and can be subjective. Therefore, an objective rapid classification method is required at the SGs. This can be achieved through an innovative system using computer vision complemented by in-situ probe measurement to perform non-destructive and instantaneous soil classification on-site. An accurate classification of excavated soils is critical in maximizing the recovery and reuse of natural resources in land reclamation projects for long-term sustainability. This paper presents the assessment of rapid soil testing methods that are suitable for integration with a recently developed novel rapid soil classification using computer vision. The objective of this complementary soil parameter measurement is to enhance the soil type prediction accuracy, as well as the capability to detect soil type with depth. The methods that are deemed suitable are the four-probe soil electrical resistivity measurement, cone penetration test (CPT) and moisture content test using time-domain reflectometer (TDR).
Field Trial to Rapidly Classify Soil Using Computer Vision with Electric Resistivity and Soil Strength
https://orcid.org/http://orcid.org/0000-0003-0899-7012
In Singapore, large volumes of excavated soils from the construction industry are sustainably re-purposed in land reclamation projects as fill material. The excavated soils are trucked to Staging Ground (SGs), where they are received and categorized into two broad groups (“Good Earth” and “Soft Clay”). The soil type categorization is traditionally done using properties such as particle size distribution (PSD) and water content (w). However, due to the heterogeneity of soils and non-uniform mixing during the excavation and truck loading process, the actual excavated soils in each truckload received at the SGs may vary. As such, visual checks of each truck are presently implemented at the SGs, which is labour-intensive and can be subjective. Therefore, an objective rapid classification method is required at the SGs. This can be achieved through an innovative system using computer vision complemented by in-situ probe measurement to perform non-destructive and instantaneous soil classification on-site. An accurate classification of excavated soils is critical in maximizing the recovery and reuse of natural resources in land reclamation projects for long-term sustainability. This paper presents the assessment of rapid soil testing methods that are suitable for integration with a recently developed novel rapid soil classification using computer vision. The objective of this complementary soil parameter measurement is to enhance the soil type prediction accuracy, as well as the capability to detect soil type with depth. The methods that are deemed suitable are the four-probe soil electrical resistivity measurement, cone penetration test (CPT) and moisture content test using time-domain reflectometer (TDR).
Field Trial to Rapidly Classify Soil Using Computer Vision with Electric Resistivity and Soil Strength
https://orcid.org/http://orcid.org/0000-0003-0899-7012
In Singapore, large volumes of excavated soils from the construction industry are sustainably re-purposed in land reclamation projects as fill material. The excavated soils are trucked to Staging Ground (SGs), where they are received and categorized into two broad groups (“Good Earth” and “Soft Clay”). The soil type categorization is traditionally done using properties such as particle size distribution (PSD) and water content (w). However, due to the heterogeneity of soils and non-uniform mixing during the excavation and truck loading process, the actual excavated soils in each truckload received at the SGs may vary. As such, visual checks of each truck are presently implemented at the SGs, which is labour-intensive and can be subjective. Therefore, an objective rapid classification method is required at the SGs. This can be achieved through an innovative system using computer vision complemented by in-situ probe measurement to perform non-destructive and instantaneous soil classification on-site. An accurate classification of excavated soils is critical in maximizing the recovery and reuse of natural resources in land reclamation projects for long-term sustainability. This paper presents the assessment of rapid soil testing methods that are suitable for integration with a recently developed novel rapid soil classification using computer vision. The objective of this complementary soil parameter measurement is to enhance the soil type prediction accuracy, as well as the capability to detect soil type with depth. The methods that are deemed suitable are the four-probe soil electrical resistivity measurement, cone penetration test (CPT) and moisture content test using time-domain reflectometer (TDR).
Field Trial to Rapidly Classify Soil Using Computer Vision with Electric Resistivity and Soil Strength
Lecture Notes in Civil Engineering
Atalar, Cavit (editor) / Çinicioğlu, Feyza (editor) / Eugene Aw, Y. J. (author) / Chew, Soon-Hoe (author) / Tan, Yeow-Chong (author) / Goh, Pei-Ling (author) / Teo, Cheng-Soon (author) / Tan, Danette S. E. (author) / Leong, Mei-Lin (author)
International Conference on New Developments in Soil Mechanics and Geotechnical Engineering ; 2022 ; Nicosia, Cyprus
5th International Conference on New Developments in Soil Mechanics and Geotechnical Engineering ; Chapter: 18 ; 193-202
2023-03-13
10 pages
Article/Chapter (Book)
Electronic Resource
English
Field Trial on Rapid Soil Classification Using Computer Vision
| British Library Conference Proceedings | 2022
Field Soil Electrical Resistivity Measurements of Some Soil of Iraq
| Springer Verlag | 2021
A Novel Methodology to Classify Soil Liquefaction Using Deep Learning
| Online Contents | 2020
Measuring Soil Electrical Resistivity Using a Resistivity Box and a Resistivity Probe
| Online Contents | 2004