Mohamed A. Shahin

Bio: Mohamed A. Shahin is an academic researcher from Curtin University. The author has contributed to research in topics: Ballast & Settlement (structural). The author has an hindex of 27, co-authored 138 publications receiving 3457 citations. Previous affiliations of Mohamed A. Shahin include University of Wollongong & Al-Azhar University.

Cementation of sand soil by microbially induced calcite precipitation at various degrees of saturation

Abstract: A newly emerging microbiological soil stabilization method, known as microbially induced calcite precipitation (MICP), has been tested for geotechnical engineering applications. MICP is a promising technique that utilizes the metabolic pathways of bacteria to form calcite precipitation throughout the soil matrix, leading to an increase in soil strength and stiffness. This paper investigates the geotechnical properties of sand bio-cemented under different degrees of saturation. A series of laboratory experiments was conducted, including sieve analysis, permeability, unconfined compressive strength, consolidated undrained triaxial, and durability tests. The results indicate that higher soil strength can be obtained at similar CaCO3 content when the treatment is performed under a low degree of saturation. The experimental results are further explained with a mathematical model, which shows that the crystallization efficiency, i.e., actual volume of crystals forming at the contact point where they contribute the most to strength, can be calculated from the degree of saturation and grain size. Fine sand samples exhibited higher cohesion, but lower friction angle than coarse sand samples with similar CaCO3 content. The results also confirm the potential of MICP as a viable alternative technique for soil improvement in many geotechnical engineering applications, including liquefiable sand deposits, slope stabilization, and subgrade reinforcement. The freeze-thaw and acid rain resistance of MICP-treated sand has also been tested.

Artificial neural network applications in geotechnical engineering

Abstract: Over the last few years or so, the use of artificial neural networks ( ANNs) has increased in many areas of engineering. In particular, ANNs have been applied to many geotechnical engineering problems and have demonstrated some degree of success. A review of the literature reveals that ANNs have been used successfully in pile capacity prediction, modelling soil behaviour, site characterisation, earth retaining structures, settlement of structures, slope stability, design of tunnels and underground openings, liquefaction, soil permeability and hydraulic conductivity, soil compaction, soil swelling and classification of soils. The objective of this paper is to provide a general view of some ANN applications for solving some types of geotechnical engineering problems. It is not intended to describe the ANNs modelling issues in geotechnical engineering. The paper also does not intend to cover every single application or scientific paper that found in the literature. For brevity, some works are selected to be described in some detail, while others are acknowledged for reference purposes. The paper then discusses the strengths and limitations of ANNs compared with the other modelling approaches. The engineering properties of soil and rock exhibit varied and uncertain behaviour due to the complex and imprecise physical processes associated with the formation of these materials (Jaksa 1995). This is in contrast to most other civil engineering materials, such as steel, concrete and timber, which exhibit far greater homogeneity and isotropy. In order to cope with the complexity of geotechnical behaviour, and the spatial variability of these materials, traditional forms of engineering design models are justifiably simplified. An alternative approach, which has been shown to have some degree of success, is based on the data alone to determine the structure and parameters of the model . The technique is known as artificial neural networks ( ANNs) and is well suited to model complex problems where the relationship between the model variables is unknown (Hubick 1992). This paper is intended to be for readers in the field of geotechnical engineering who are not familiar with artificial neural networks. The paper aims to detail some features associated with ANNs through a review for some of their applications to-date in geotechnical engineering. It is hoped that this review may attract more geotechnical engineers to pay better attention to this promising tool. The paper starts with a brief overview of the structure and operation of the ANNs and gives a general overview of most ANN applications that have appeared in the geotechncial engineering literature. Finally, the paper discusses the relative success of ANNs in predicting various geotechnical engineering properties and behaviour.

Predicting settlement of shallow foundations using neural networks

Abstract: Over the years, many methods have been developed to predict the settlement of shallow foundations on cohesionless soils. However, methods for making such predictions with the required degree of accuracy and consistency have not yet been developed. Accurate prediction of settlement is essential since settlement, rather than bearing capacity, generally controls foundation design. In this paper, artificial neural networks (ANNs) are used in an attempt to obtain more accurate settlement prediction. A large database of actual measured settlements is used to develop and verify the ANN model. The predicted settlements found by utilizing ANNs are compared with the values predicted by three of the most commonly used traditional methods. The results indicate that ANNs are a useful technique for predicting the settlement of shallow foundations on cohesionless soils, as they outperform the traditional methods.

State-of-the-Art Review of Biocementation by Microbially Induced Calcite Precipitation (MICP) for Soil Stabilization

Abstract: Biocementation is a recently developed new branch in geotechnical engineering that deals with the application of microbiological activity to improve the engineering properties of soils. One of the most commonly adopted processes to achieve soil biocementation is through microbially induced calcite precipitation (MICP). This technique utilizes the metabolic pathways of bacteria to form calcite (CaCO3) that binds the soil particles together, leading to increased soil strength and stiffness. This paper presents a review of the use of MICP for soil improvement and discusses the treatment process including the primary components involved and major affecting factors. Envisioned applications, potential advantages and limitations of MICP for soil improvement are also presented and discussed. Finally, the primary challenges that lay ahead for the future research (i.e. treatment optimization, upscaling for in situ implementation and self-healing of biotreated soils) are briefly discussed.

Data division for developing neural networks applied to geotechnical engineering

Abstract: In recent years, artificial neural networks (ANNs) have been applied to many geotechnical engineering problems with some degree of success. In the majority of these applications, data division is carried out on an arbitrary basis. However, the way the data are divided can have a significant effect on model performance. In this paper, the issue of data division and its impact on ANN model performance is investigated for a case study of predicting the settlement of shallow foundations on granular soils. Four data division methods are investigated: (1) random data division; (2) data division to ensure statistical consistency of the subsets needed for ANN model development; (3) data division using self-organizing maps (SOMs); and (4) a new data division method using fuzzy clustering. The results indicate that the statistical properties of the data in the training, testing, and validation sets need to be taken into account to ensure that optimal model performance is achieved. It is also apparent from the results that the SOM and fuzzy clustering methods are suitable approaches for data division.

Machine learning in geosciences and remote sensing

Abstract: Learning incorporates a broad range of complex procedures. Machine learning (ML) is a subdivision of artificial intelligence based on the biological learning process. The ML approach deals with the design of algorithms to learn from machine readable data. ML covers main domains such as data mining, difficult-to-program applications, and software applications. It is a collection of a variety of algorithms (e.g. neural networks, support vector machines, self-organizing map, decision trees, random forests, case-based reasoning, genetic programming, etc.) that can provide multivariate, nonlinear, nonparametric regression or classification. The modeling capabilities of the ML-based methods have resulted in their extensive applications in science and engineering. Herein, the role of ML as an effective approach for solving problems in geosciences and remote sensing will be highlighted. The unique features of some of the ML techniques will be outlined with a specific attention to genetic programming paradigm. Furthermore, nonparametric regression and classification illustrative examples are presented to demonstrate the efficiency of ML for tackling the geosciences and remote sensing problems.

Cementation of sand soil by microbially induced calcite precipitation at various degrees of saturation

Abstract: A newly emerging microbiological soil stabilization method, known as microbially induced calcite precipitation (MICP), has been tested for geotechnical engineering applications. MICP is a promising technique that utilizes the metabolic pathways of bacteria to form calcite precipitation throughout the soil matrix, leading to an increase in soil strength and stiffness. This paper investigates the geotechnical properties of sand bio-cemented under different degrees of saturation. A series of laboratory experiments was conducted, including sieve analysis, permeability, unconfined compressive strength, consolidated undrained triaxial, and durability tests. The results indicate that higher soil strength can be obtained at similar CaCO3 content when the treatment is performed under a low degree of saturation. The experimental results are further explained with a mathematical model, which shows that the crystallization efficiency, i.e., actual volume of crystals forming at the contact point where they contribute the most to strength, can be calculated from the degree of saturation and grain size. Fine sand samples exhibited higher cohesion, but lower friction angle than coarse sand samples with similar CaCO3 content. The results also confirm the potential of MICP as a viable alternative technique for soil improvement in many geotechnical engineering applications, including liquefiable sand deposits, slope stabilization, and subgrade reinforcement. The freeze-thaw and acid rain resistance of MICP-treated sand has also been tested.

Biomineralization of calcium carbonates and their engineered applications: a review

Abstract: Microbially induced calcium carbonate precipitation (MICCP) is a naturally occurring biological process in which microbes produce inorganic materials as part of their basic metabolic activities. This technology has been widely explored and promising with potential in various technical applications. In the present review, the detailed mechanism of production of calcium carbonate biominerals via ureolytic bacteria has been discussed along with role of bacteria and the sectors where these biominerals are being used. The review discusses the applications of bacterially produced carbonate biominerals for improving the durability of buildings, remediation of environment (water and soil), sequestration of atmospheric CO2, filler material in rubbers and plastics etc. The study also sheds light on benefits of bacterial biominerals over traditional agents and also the issues that lie in the path of successful commercialization of the technology of Microbially induced calcium carbonate precipitation from lab to field scale.