Magued Iskander

This book explores the effectiveness of Dynamic Image Analysis (DIA) in granulometry studies of sand, and presents criteria for soil characterization using DIA, including test parameters, specimen size, efficacy in gap-graded soils, and its limitations. DIA is a modern experimental technique used to analyze and classify particulate materials based on their size, shape, and other morphological properties. This method employs a high-frame-rate camera to capture images of individual sand particles, which have been transported and separated using various techniques. DIA generates both particle size and shape information by analyzing thousands to millions of particles, providing a quantitative statistical description of grain size and shape distribution within the specimen. The manuscript also offers a comprehensive examination of 2D and 3D particle size and shape descriptors. It demonstrates that there is no correlation between size and shape parameters in many sands and that shape descriptors can be reduced to four independent parameters representing sand granulometry at different scales. Additionally, the use of DIA in exploring the depositional history of two complex calcareous sands is presented. The manuscript presents the properties of 30 representative sands, including size and shape parameters, and fits them to statistical distributions. The investigated soils encompass both natural and machine-sorted materials, particles with regular and irregular shapes, as well as siliceous and calcareous sands. Physical granulometry of sand particles is compared using 2D, 3D DIA, and micro-computed tomography (μCT). The work demonstrates that DIA offers significant advantages in terms of efficiency for 3D shape analysis while providing an adequate representation of particle sizes and shapes of most sands. Finally, the manuscript integrates classical geotechnical engineering with computer vision and artificial intelligence. Size and shape descriptors are utilized for sand classification through machine learning models. This work represents a crucial step toward the automatic machine classification of soils, potentially enabling on-site classification using smartphones equipped with high-resolution cameras.

This book explores the effectiveness of Dynamic Image Analysis (DIA) in granulometry studies of sand, and presents criteria for soil characterization using DIA, including test parameters, specimen size, efficacy in gap-graded soils, and its limitations. DIA is a modern experimental technique used to analyze and classify particulate materials based on their size, shape, and other morphological properties. This method employs a high-frame-rate camera to capture images of individual sand particles, which have been transported and separated using various techniques. DIA generates both particle size and shape information by analyzing thousands to millions of particles, providing a quantitative statistical description of grain size and shape distribution within the specimen. The manuscript also offers a comprehensive examination of 2D and 3D particle size and shape descriptors. It demonstrates that there is no correlation between size and shape parameters in many sands and that shape descriptors can be reduced to four independent parameters representing sand granulometry at different scales. Additionally, the use of DIA in exploring the depositional history of two complex calcareous sands is presented. The manuscript presents the properties of 30 representative sands, including size and shape parameters, and fits them to statistical distributions. The investigated soils encompass both natural and machine-sorted materials, particles with regular and irregular shapes, as well as siliceous and calcareous sands. Physical granulometry of sand particles is compared using 2D, 3D DIA, and micro-computed tomography (µCT). The work demonstrates that DIA offers significant advantages in terms of efficiency for 3D shape analysis while providing an adequate representation of particle sizes and shapes of most sands. Finally, the manuscript integrates classical geotechnical engineering with computer vision and artificial intelligence. Size and shape descriptors are utilized for sand classification through machine learning models. This work represents a crucial step toward the automatic machine classification of soils, potentially enabling on-site classification using smartphones equipped with high-resolution cameras.

Structures placed on hillsides often present a number of challenges and a limited number of economical choices for site design. An option sometimes employed is to use the building frame as a retaining element, comprising a Rigidly Framed Earth Retaining Structure (RFERS). The relationship between temperature and earth pressure acting on RFERS, is explored in this monograph through a 4.5 year monitoring program of a heavily instrumented in service structure. The data indicated that the coefficient of earth pressure behind the monitored RFERS had a strong linear correlation with temperature. The study also revealed that thermal cycles, rather than lateral earth pressure, were the cause of failure in many structural elements.The book demonstrates that depending on the relative stiffness of the retained soil mass and that of the structural frame, the developed lateral earth pressure, during thermal expansion, can reach magnitudes several times larger than those determined using classical earth pressure theories. Additionally, a nearly perpetual lateral displacement away from the retained soil mass may occur at the free end of the RFERS leading to unacceptable serviceability problems. These results suggest that reinforced concrete structures designed for the flexural stresses imposed by the backfill soil will be inadequately reinforced to resist stresses produced during the expansion cycles.Parametric studies of single and multi-story RFERS with varying geometries and properties are also presented to investigate the effects of structural stiffness on the displacement of RFERS and the lateral earth pressure developed in the soil mass. These studies can aid the reader in selecting appropriate values of lateral earth pressure for the design of RFERS. Finally, simplified closed form equations that can be used to predict the lateral drift of RFERS are presented.KEY WORDS: Earth Pressure; Soil-Structure Interaction; Mechanics; Failure;Distress; Temperature; Thermal Effects; Concrete; Coefficient of Thermal Expansion; Segmental Bridges; Jointless Bridges; Integral Bridges; Geotechnical Instrumentation; Finite Element Modeling; FEM; Numerical Modeling.

Rapid Penetration into Granular Media: Visualizing the Fundamental Physics of Rapid Penetration introduces readers to the variety of methods developed to visualize, observe, and model the rapid penetration of natural and man-made projectiles into earth materials while providing seasoned practitioners with a standard reference that showcases the topic's most recent developments in research and application. There has been a flurry of recently funded research both in the U.S. and Europe on studying the behavior of projectiles in granular media. This book compiles the findings of recent research on the subject and outlines the fundamental physics of rapid earth penetration, and assembles a comprehensive collection of experimental and numerical techniques to study the problem.

One of the major difficulties in predicting the capacity of pipe piles in sand has resulted from a lack of understanding of the physical processes that control the behavior of piles during installation and loading.This monograph presents a detailed blue print for developing experimental facilities necessary to identify these processes. These facilities include a unique instrumented double-walled pipe-pile that is used to delineate the frictional stresses acting against the external and internal surfaces of the pile. The pile is fitted with miniature pore-pressure transducers to monitor the generation of pore water pressure during installation and loading. A fast automatic laboratory pile hammer capable of representing the phenomena that occur during pile driving was also developed and used.

Structures placed on hillsides often present a number of challenges and a limited number of economical choices for site design. An option sometimes employed is to use the building frame as a retaining element, comprising a Rigidly Framed Earth Retaining Structure (RFERS). The relationship between temperature and earth pressure acting on RFERS, is explored in this monograph through a 4.5 year monitoring program of a heavily instrumented in service structure. The data indicated that the coefficient of earth pressure behind the monitored RFERS had a strong linear correlation with temperature. The study also revealed that thermal cycles, rather than lateral earth pressure, were the cause of failure in many structural elements.The book demonstrates that depending on the relative stiffness of the retained soil mass and that of the structural frame, the developed lateral earth pressure, during thermal expansion, can reach magnitudes several times larger than those determined using classical earth pressure theories. Additionally, a nearly perpetual lateral displacement away from the retained soil mass may occur at the free end of the RFERS leading to unacceptable serviceability problems. These results suggest that reinforced concrete structures designed for the flexural stresses imposed by the backfill soil will be inadequately reinforced to resist stresses produced during the expansion cycles.Parametric studies of single and multi-story RFERS with varying geometries and properties are also presented to investigate the effects of structural stiffness on the displacement of RFERS and the lateral earth pressure developed in the soil mass. These studies can aid the reader in selecting appropriate values of lateral earth pressure for the design of RFERS. Finally, simplified closed form equations that can be used to predict the lateral drift of RFERS are presented.KEY WORDS: Earth Pressure; Soil-Structure Interaction; Mechanics; Failure;Distress; Temperature; Thermal Effects; Concrete; Coefficient of Thermal Expansion; Segmental Bridges; Jointless Bridges; Integral Bridges; Geotechnical Instrumentation; Finite Element Modeling; FEM; Numerical Modeling.

up with automated systems for assessment of road condition. For example, Haas et al (1997) developed an automated algorithm for detecting cracks and joints con- tion. Smith and Lin (1997) developed a fuzzy logic classification scheme for pavement distress condition. Oh et al (1997) developed iterative algorithm for overcoming noisy images of roads due to shadows and low light conditions. Koustsopoulos and Mishalani (1997) presented a model for distress assessment in a local (microscopic) and global (macroscopic) level using captured images of pavement. Lee (1993) presented a comparison between 15 different imaging al- rithms used in crack detection. Ground Penetration Radar (GPR) has also been used for pavement assessment. Special computer algorithms were developed for quick analysis of GPR data (Adeli & Hung 1993 and Maser 1996). Heiler and McNeil (1997) proposed a modified system for analyzing the GPR data using an artificial neural network (ANN). 2.3.2 Traffic Analysis and Control Currently imaging systems provide essential data for transportation and traffic engineering planning (Anon 1999). Machine vision techniques were introduced to intersection traffic signal control in the late 1970’s (Chou and Sethi 1993). No- days, many systems have been developed all over the world for traffic analysis and control applications, in addition to image based systems for traffic violations. Nallamathu and Wang (1997) developed one of the first automated systems for license plate recognition using character recognition algorithm for the use in monitoring violators at toll stations and many other traffic applications.

One of the major difficulties in predicting the capacity of pipe piles in sand has resulted from a lack of understanding of the physical processes that control the behavior of piles during installation and loading.This monograph presents a detailed blue print for developing experimental facilities necessary to identify these processes. These facilities include a unique instrumented double-walled pipe-pile that is used to delineate the frictional stresses acting against the external and internal surfaces of the pile. The pile is fitted with miniature pore-pressure transducers to monitor the generation of pore water pressure during installation and loading. A fast automatic laboratory pile hammer capable of representing the phenomena that occur during pile driving was also developed and used.

up with automated systems for assessment of road condition. For example, Haas et al (1997) developed an automated algorithm for detecting cracks and joints con- tion. Smith and Lin (1997) developed a fuzzy logic classification scheme for pavement distress condition. Oh et al (1997) developed iterative algorithm for overcoming noisy images of roads due to shadows and low light conditions. Koustsopoulos and Mishalani (1997) presented a model for distress assessment in a local (microscopic) and global (macroscopic) level using captured images of pavement. Lee (1993) presented a comparison between 15 different imaging al- rithms used in crack detection. Ground Penetration Radar (GPR) has also been used for pavement assessment. Special computer algorithms were developed for quick analysis of GPR data (Adeli & Hung 1993 and Maser 1996). Heiler and McNeil (1997) proposed a modified system for analyzing the GPR data using an artificial neural network (ANN). 2.3.2 Traffic Analysis and Control Currently imaging systems provide essential data for transportation and traffic engineering planning (Anon 1999). Machine vision techniques were introduced to intersection traffic signal control in the late 1970’s (Chou and Sethi 1993). No- days, many systems have been developed all over the world for traffic analysis and control applications, in addition to image based systems for traffic violations. Nallamathu and Wang (1997) developed one of the first automated systems for license plate recognition using character recognition algorithm for the use in monitoring violators at toll stations and many other traffic applications.