Geomorphology's role in the study of weathering of cultural stone ☆

1. Introduction 2. Rock weathering and desert surfaces 3. Aeolian geomorphology 4. Dunes 5. Rivers and slopes 6. Applied geomorphology in deserts: hazards, resources and the future 7. Regional variety.

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Arid and Semi-Arid Geomorphology

Development of an artificial neural network correlation for prediction of hold-up of slurry transport in pipelines

https://hal.archives-ouvertes.fr/hal-01689086/document

Neutron radiography and X-ray computed tomography for quantifying weathering and water uptake processes inside porous limestone used as building material

It was 35 years ago since the first usage of Artificial Intelligence (AI) technique in geotechnical engineering, during those years many (AI) techniques were developed based in mathematical, statistical and logical concepts, but the breakthrough occurs by mimicking the natural searching and optimization algorithms. This huge development in (AI) techniques reflected on the geotechnical engineering problems. In this research, 626 paper and thesis published in the period from 1984 to 2019 concerned in applying (AI) techniques in geotechnical engineering were collected, filtered, arranged and classified with respect to subject, (AI) technique, publisher and publishing date and stored in a database. The extracted information from the database were tabulated, presented graphically and commented. The main conclusions is that the number of researches in this field increases almost exponentially, the most used (AI) technique is the Artificial Neural Networks and its enhancements where it is presents about half the researches and finally correlating soil and rock properties is the most addressed subject with about 30% of the researches.

35 Years of (AI) in Geotechnical Engineering: State of the Art

In the modern urban atmosphere SO{sub 2} and NO{sub 2} attack calcite (CaCO{sub 3}) in marble exposed at rain-sheltered surfaces creating largely gypsum (CaSO{sub 4}{center_dot}2H{sub 2}O) crusts that eventually exfoliate. In combination with CO{sub 2} these gases erode the marble at unsheltered surfaces. the authors report the development of mathematical models to predict the rate of growth of crust and the rate of surface recession. To determine the rate of growth of crust the kinetic rate constant, diffusion rate, and the order of reaction were determined by the application of the shrinking-core model applied to data generated in laboratory experiments. Based on these parameters /and average ambient levels of 10 parts per billion (ppb) SO{sub 2} and 25 ppb NO{sub 2} in Louisville, Ky., the rate of crust formation for this metro area was calculated to be 1.8 {micro}m in the first year. However, the rate of recession was modeled from data obtained by exposing marble slabs to rainfalls. A surface recession of 15 {micro}m/yr was calculated. The models predicted well the rate of growth of crust observed at several sites in Louisville and the predicted surface recession compared well with values reported in the literature.

Weathering Rates of Marble in Laboratory and Outdoor Conditions

Regulatory control of a chaotic nonisothermal CSTR

Four varieties of limestone distinguished on the basis of pore-size distributions were exposed in dynamic 10-, 20- and 50-ppm SO{sub 2} atmospheres for up to 500 h at 25 C and 100% rh. The resulting conversion of the limestone was measured as a function of the reaction product formed and the change in porosity. These conversions could be predicted correctly using either the shrinking unreacted core model or the distributed pore model. An artificial neural network (ANN) was also trained for the purpose. All three approaches predicted conversions that fitted well with the observed data; however, those predicted by ANN were the most accurate. Further, the weights determined for ANN on the basis of three limestone varieties also accurately predicted the conversions of the fourth variety for which no information was supplied in the training phase, showing that ANN can also be used successfully to estimate gas-solid noncatalytic reactions.

Application of artificial neural networks in modeling limestone–SO2 reaction

On dynamic control of chaos: A study with reference to a reacting system

The Great Sphinx of Giza is considered by Egyptologists to have been excavated by the Pharaoh Kephren nearly 4500 years ago. Schoch and West (1991) have suggested that the Sphinx is much older, based primarily upon the rounded profile of the strata of the Sphinx thorax and the deep channels present in the walls surrounding the Sphinx ditch. These features, according to them, are due to “precipitation-induced weathering” formed when the Sahara still experienced a humid climate at least 7000 years ago. In this article we show how weathering in an arid environment can produce the rounded profile given the gradual change in lithology of the alternating hard and soft limestone strata. We show further that the channels are actually the pre-Pliocene karst features formed by underground water and exposed due to the excavation of the Sphinx ditch. We propose therefore that, for now, the Sphinx may still be regarded as of pharaonic origin. © 1995 John Wiley & Sons, Inc.

Jayanta K. Bandyopadhyay

Papers

Weathering Rates of Marble in Laboratory and Outdoor Conditions

Regulatory control of a chaotic nonisothermal CSTR

Application of artificial neural networks in modeling limestone–SO2 reaction

On dynamic control of chaos: A study with reference to a reacting system

Geologic weathering and its implications on the age of the sphinx