Mohamed Shahin | Tanta University (original) (raw)

Papers by Mohamed Shahin

Geoscience Frontiers, 2014

Surgical Science, 2011

Hemangiopericytoma is a rare vascular tumour of infants. Although generally considered to be beni... more Hemangiopericytoma is a rare vascular tumour of infants. Although generally considered to be benign, local recurrence and metastases can occur. Herein, we report on two full term girls, delivered with lumbosacral swelling and left thigh swelling respectively. Complete surgical excision with safety margins was performed for each lesion. Histologic examination of both lesions showed picture of infantile hemangiopericytoma. There is no evidence of local recurrence or distant metastasis during last 20 and 17 months for 1 st case and 2 nd case respectively. In conclusion; most infantile hemangiopericytoma follow a benign course. Rarely these tumours behave aggressively with local infiltration, recurrences and even distant metastases. Careful follow up is therefore essential.

In recent years, artificial neural networks (ANNs) have emerged as one of the potentially most su... more In recent years, artificial neural networks (ANNs) have emerged as one of the potentially most successful modelling approaches in engineering. In particular, ANNs have been applied to many areas of geotechnical engineering and have demonstrated considerable success. The objective of this paper is to highlight the use of ANNs in foundation engineering. The paper describes ANN techniques and some of

Over the last few years, artificial neural networks (ANNs) have been used successfully for modeli... more Over the last few years, artificial neural networks (ANNs) have been used successfully for modeling almost all aspects of geotechnical engineering problems. Whilst ANNs provide a great deal of promise, they suffer from a number of shortcomings such as knowledge extraction, extrapolation and uncertainty. This paper presents a state-of-the-art examination of ANNs in geotechnical engineering and provides insights into the

Geotechnical Special Publication, 2006

Railway ballast deforms and degrades progressively under heavy cyclic loading. Ballast degradatio... more Railway ballast deforms and degrades progressively under heavy cyclic loading. Ballast degradation is influenced by several factors including the amplitude and number of load cycles, gradation of aggregates, track confining pressure, angularity and fracture strength of individual grains. The degraded ballast is usually cleaned on track, otherwise, fully or partially replaced by fresh ballast, depending on the track settlement and current density. The use of composite geosynthetics at the bottom of recycled ballast layer is highly desirable to serve the functions of both drainage and separation of ballast from subballast. Construction of the rail track also requires appropriate improvement of the subgrade soils to achieve an adequately stiff surface layer prior to placing the ballast and subballast. Based on extensive research at University of Wollongong, it is found that the gradation of ballast plays a significant role in the strength, deformation, degradation, stability and drainage of rail tracks. Results from large-scale triaxial testing indicate that a small increase in confining pressure improves track stability with less ballast degradation. Bonded geogridsgeotextiles also decrease differential settlements of tracks, ballast degradation and lateral movement, and the risk of subgrade pumping. Stabilization of soft subgrade soils is also essential for improving the overall stability of track and to reduce the differential settlement during the operation of trains. This paper also highlights the

GeoCongress 2006: Geotechnical Engineering in the Information Technology Age, 2006

Ballasted rail track substructure usually consists of graded layers of granular media of ballast ... more Ballasted rail track substructure usually consists of graded layers of granular media of ballast and subballast (capping) placed above a compacted subgrade (formation soil). The optimum design of railway track substructure relies on many factors that affect the ...

Geotechnical Special Publication, 2009

In the last few decades, numerous methods have been developed for predicting the axial capacity o... more In the last few decades, numerous methods have been developed for predicting the axial capacity of drilled shafts. Among the available methods, the cone penetration test (CPT) based models have been shown to give better predictions in many situations. This can be attributed to the fact that CPT-based methods have been developed in accordance with the results of the CPT tests, which have been found to yield more reliable soil properties, hence, more accurate axial capacity predictions of drilled shafts. In this paper, one of the most commonly used artificial intelligence techniques, i.e. artificial neural networks (ANNs), was utilized in an attempt to obtain more accurate axial capacity predictions for drilled shafts. The ANN model was developed using data collected from the literature that comprise CPT results and drilled shaft load tests of 94 case records. The predictions from the ANN model were compared with those obtained from three commonly used available CPT-based methods. The results indicate that the ANN-based model provides more accurate axial capacity predictions of drilled shafts and outperforms the available conventional methods.

Proceedings of the 17th International Conference on Soil Mechanics and Geotechnical Engineering: The Academia and Practice of Geotechnical Engineering, 2009

Geotechnical Special Publication, 2009

Metaheuristics in Water, Geotechnical and Transport Engineering, 2013

Agriculture and Biology Journal of North America, 2010

This experiment was carried out during 2008 and 2009 seasons on Keitte mango trees grown in sandy... more This experiment was carried out during 2008 and 2009 seasons on Keitte mango trees grown in sandy soil under drip irrigation system in National Research Centre farm at El-Nobaria district, El-Behiera Governorate, Egypt. Experiment studied the effect of spraying trees with algae extract at ( 0.5, 1 and 2%) + yeast extract at (0.05, 0.1 and 0.2%) at full bloom stage on fruit set, fruit drop, fruit retention, tree yield, fruit quality and leaf mineral content. Results showed that spraying Keitte mango trees once at full bloom with algae at 2% combined with yeast at 0.2% was very effective in improving fruit set, fruit retention, yield as number of fruits or weight (kg) / tree and increased fruit length (cm), fruit width (cm), fruit weight (g), pulp/fruit percentage and enhanced total soluble solids (T.S.S.). Moreover, it reduced fruit drop and weight of peel and seed (g) comparing with the control. This treatment improved nitrogen, potassium and boron contents in the leaves. On the other hand, all treatments had no effect on leaf phosphorus percentage.

Over the last few years or so, the use of artificial neural networks ( ANNs) has increased in man... more 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 wo...

Artificial neural networks (ANNs) are a form of artificial intelligence and, since the mid-1990s,... more Artificial neural networks (ANNs) are a form of artificial intelligence and, since the mid-1990s, ANN- based models have been successfully applied to virtually every problem in geotechnical engineering. This paper briefly examines the areas of geotechnical engineering to which ANNs have been applied, provides a brief overview of the operation of ANN models, and highlights and discusses four important issues which require further attention in the future. These are model robustness, transparency and knowledge extraction, extrapolation, and uncertainty. For ANN models to be more effective and useful in the future, it is essential that further work be undertaken in these four areas, particularly in the context of geotechnical engineering.

Proceedings of the ICE - Ground Improvement, 2007

Indraratna, B.; Shahin, M. A.; and Salim, W.: Stabilisation of granular media and formation soil ... more Indraratna, B.; Shahin, M. A.; and Salim, W.: Stabilisation of granular media and formation soil using geosynthetics with special reference to railway engineering 2007.

Journal of Geotechnical and Geoenvironmental Engineering, 2003

Artificial neural networks (ANNs) are a form of artificial intelligence (AI), which in their arch... more Artificial neural networks (ANNs) are a form of artificial intelligence (AI), which in their architecture attempt to simulate the biological structure of the human brain and nervous system. In this report, back-propagation neural networks are used to predict the settlement of shallow foundations on cohesionless soils. More than two hundred cases of actual measured settlements are used to develop and verify the ANN model. The predicted settlements found by utilising ANNs are compared with the values predicted by three commonly used deterministic methods. The results indicate that artificial neural networks are a promising method for predicting settlement of shallow foundations on cohesionless soils, as they outperform the conventional methods.

CORE2008: Rail; The Core of Integrated …, 2008

Due to the daily congestion of highways, railways have become the most popular means of public tr... more Due to the daily congestion of highways, railways have become the most popular means of public transportation, which increased the demand for heavier and faster trains while keeping the cost of track maintenance at its minimum level. This requires an investigation ...

Journal of Constructional Steel Research, 2008

The behaviour of steel circular tubes under pure bending is complex and highly nonlinear. The lit... more The behaviour of steel circular tubes under pure bending is complex and highly nonlinear. The literature has a number of solutions to predict the response of steel circular tubes under pure bending; however, most of these solutions are complicated and difficult to use in routine design practice. In this paper, the feasibility of using artificial neural networks (ANNs) for developing more accurate and simple-to-use models for predicting the ultimate pure bending of steel circular tubes is investigated. The data used to calibrate and validate the ANN models are obtained from the literature and comprise a series of 49 pure bending tests conducted on fabricated steel circular tubes and 55 tests carried out on coldformed tubes. Multilayer feed-forward neural networks that are trained with the back-propagation algorithm are constructed using four design parameters (i.e. tube thickness, tube diameter, yield strength of steel and modulus of elasticity of steel) as network inputs and the ultimate pure bending as the only output. A sensitivity analysis is conducted on the ANN models to investigate the generalization ability (robustness) of the developed models, and predictions from the ANN models are compared with those obtained from most available codes and standards. To facilitate the use of the developed ANN models, they are translated into design equations suitable for spreadsheet programming or hand calculations. The results indicate that ANNs are capable of predicting the ultimate bending capacity of steel circular tubes with a high degree of accuracy, and outperform most available codes and standards. Crown

Journal of Computing in Civil Engineering, 2004

In recent years, artificial neural networks ͑ANNs͒ have been applied to many geotechnical enginee... more In recent years, artificial neural networks ͑ANNs͒ 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: ͑1͒ random data division; ͑2͒ data division to ensure statistical consistency of the subsets needed for ANN model development; ͑3͒ data division using self-organizing maps ͑SOMs͒; and ͑4͒ 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.

Geoscience Frontiers, 2014

Surgical Science, 2011

Hemangiopericytoma is a rare vascular tumour of infants. Although generally considered to be beni... more Hemangiopericytoma is a rare vascular tumour of infants. Although generally considered to be benign, local recurrence and metastases can occur. Herein, we report on two full term girls, delivered with lumbosacral swelling and left thigh swelling respectively. Complete surgical excision with safety margins was performed for each lesion. Histologic examination of both lesions showed picture of infantile hemangiopericytoma. There is no evidence of local recurrence or distant metastasis during last 20 and 17 months for 1 st case and 2 nd case respectively. In conclusion; most infantile hemangiopericytoma follow a benign course. Rarely these tumours behave aggressively with local infiltration, recurrences and even distant metastases. Careful follow up is therefore essential.

In recent years, artificial neural networks (ANNs) have emerged as one of the potentially most su... more In recent years, artificial neural networks (ANNs) have emerged as one of the potentially most successful modelling approaches in engineering. In particular, ANNs have been applied to many areas of geotechnical engineering and have demonstrated considerable success. The objective of this paper is to highlight the use of ANNs in foundation engineering. The paper describes ANN techniques and some of

Over the last few years, artificial neural networks (ANNs) have been used successfully for modeli... more Over the last few years, artificial neural networks (ANNs) have been used successfully for modeling almost all aspects of geotechnical engineering problems. Whilst ANNs provide a great deal of promise, they suffer from a number of shortcomings such as knowledge extraction, extrapolation and uncertainty. This paper presents a state-of-the-art examination of ANNs in geotechnical engineering and provides insights into the

Geotechnical Special Publication, 2006

Railway ballast deforms and degrades progressively under heavy cyclic loading. Ballast degradatio... more Railway ballast deforms and degrades progressively under heavy cyclic loading. Ballast degradation is influenced by several factors including the amplitude and number of load cycles, gradation of aggregates, track confining pressure, angularity and fracture strength of individual grains. The degraded ballast is usually cleaned on track, otherwise, fully or partially replaced by fresh ballast, depending on the track settlement and current density. The use of composite geosynthetics at the bottom of recycled ballast layer is highly desirable to serve the functions of both drainage and separation of ballast from subballast. Construction of the rail track also requires appropriate improvement of the subgrade soils to achieve an adequately stiff surface layer prior to placing the ballast and subballast. Based on extensive research at University of Wollongong, it is found that the gradation of ballast plays a significant role in the strength, deformation, degradation, stability and drainage of rail tracks. Results from large-scale triaxial testing indicate that a small increase in confining pressure improves track stability with less ballast degradation. Bonded geogridsgeotextiles also decrease differential settlements of tracks, ballast degradation and lateral movement, and the risk of subgrade pumping. Stabilization of soft subgrade soils is also essential for improving the overall stability of track and to reduce the differential settlement during the operation of trains. This paper also highlights the

GeoCongress 2006: Geotechnical Engineering in the Information Technology Age, 2006

Ballasted rail track substructure usually consists of graded layers of granular media of ballast ... more Ballasted rail track substructure usually consists of graded layers of granular media of ballast and subballast (capping) placed above a compacted subgrade (formation soil). The optimum design of railway track substructure relies on many factors that affect the ...

Geotechnical Special Publication, 2009

In the last few decades, numerous methods have been developed for predicting the axial capacity o... more In the last few decades, numerous methods have been developed for predicting the axial capacity of drilled shafts. Among the available methods, the cone penetration test (CPT) based models have been shown to give better predictions in many situations. This can be attributed to the fact that CPT-based methods have been developed in accordance with the results of the CPT tests, which have been found to yield more reliable soil properties, hence, more accurate axial capacity predictions of drilled shafts. In this paper, one of the most commonly used artificial intelligence techniques, i.e. artificial neural networks (ANNs), was utilized in an attempt to obtain more accurate axial capacity predictions for drilled shafts. The ANN model was developed using data collected from the literature that comprise CPT results and drilled shaft load tests of 94 case records. The predictions from the ANN model were compared with those obtained from three commonly used available CPT-based methods. The results indicate that the ANN-based model provides more accurate axial capacity predictions of drilled shafts and outperforms the available conventional methods.

Proceedings of the 17th International Conference on Soil Mechanics and Geotechnical Engineering: The Academia and Practice of Geotechnical Engineering, 2009

Geotechnical Special Publication, 2009

Metaheuristics in Water, Geotechnical and Transport Engineering, 2013

Agriculture and Biology Journal of North America, 2010

This experiment was carried out during 2008 and 2009 seasons on Keitte mango trees grown in sandy... more This experiment was carried out during 2008 and 2009 seasons on Keitte mango trees grown in sandy soil under drip irrigation system in National Research Centre farm at El-Nobaria district, El-Behiera Governorate, Egypt. Experiment studied the effect of spraying trees with algae extract at ( 0.5, 1 and 2%) + yeast extract at (0.05, 0.1 and 0.2%) at full bloom stage on fruit set, fruit drop, fruit retention, tree yield, fruit quality and leaf mineral content. Results showed that spraying Keitte mango trees once at full bloom with algae at 2% combined with yeast at 0.2% was very effective in improving fruit set, fruit retention, yield as number of fruits or weight (kg) / tree and increased fruit length (cm), fruit width (cm), fruit weight (g), pulp/fruit percentage and enhanced total soluble solids (T.S.S.). Moreover, it reduced fruit drop and weight of peel and seed (g) comparing with the control. This treatment improved nitrogen, potassium and boron contents in the leaves. On the other hand, all treatments had no effect on leaf phosphorus percentage.

Over the last few years or so, the use of artificial neural networks ( ANNs) has increased in man... more 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 wo...

Artificial neural networks (ANNs) are a form of artificial intelligence and, since the mid-1990s,... more Artificial neural networks (ANNs) are a form of artificial intelligence and, since the mid-1990s, ANN- based models have been successfully applied to virtually every problem in geotechnical engineering. This paper briefly examines the areas of geotechnical engineering to which ANNs have been applied, provides a brief overview of the operation of ANN models, and highlights and discusses four important issues which require further attention in the future. These are model robustness, transparency and knowledge extraction, extrapolation, and uncertainty. For ANN models to be more effective and useful in the future, it is essential that further work be undertaken in these four areas, particularly in the context of geotechnical engineering.

Proceedings of the ICE - Ground Improvement, 2007

Indraratna, B.; Shahin, M. A.; and Salim, W.: Stabilisation of granular media and formation soil ... more Indraratna, B.; Shahin, M. A.; and Salim, W.: Stabilisation of granular media and formation soil using geosynthetics with special reference to railway engineering 2007.

Journal of Geotechnical and Geoenvironmental Engineering, 2003

Artificial neural networks (ANNs) are a form of artificial intelligence (AI), which in their arch... more Artificial neural networks (ANNs) are a form of artificial intelligence (AI), which in their architecture attempt to simulate the biological structure of the human brain and nervous system. In this report, back-propagation neural networks are used to predict the settlement of shallow foundations on cohesionless soils. More than two hundred cases of actual measured settlements are used to develop and verify the ANN model. The predicted settlements found by utilising ANNs are compared with the values predicted by three commonly used deterministic methods. The results indicate that artificial neural networks are a promising method for predicting settlement of shallow foundations on cohesionless soils, as they outperform the conventional methods.

CORE2008: Rail; The Core of Integrated …, 2008

Due to the daily congestion of highways, railways have become the most popular means of public tr... more Due to the daily congestion of highways, railways have become the most popular means of public transportation, which increased the demand for heavier and faster trains while keeping the cost of track maintenance at its minimum level. This requires an investigation ...

Journal of Constructional Steel Research, 2008

The behaviour of steel circular tubes under pure bending is complex and highly nonlinear. The lit... more The behaviour of steel circular tubes under pure bending is complex and highly nonlinear. The literature has a number of solutions to predict the response of steel circular tubes under pure bending; however, most of these solutions are complicated and difficult to use in routine design practice. In this paper, the feasibility of using artificial neural networks (ANNs) for developing more accurate and simple-to-use models for predicting the ultimate pure bending of steel circular tubes is investigated. The data used to calibrate and validate the ANN models are obtained from the literature and comprise a series of 49 pure bending tests conducted on fabricated steel circular tubes and 55 tests carried out on coldformed tubes. Multilayer feed-forward neural networks that are trained with the back-propagation algorithm are constructed using four design parameters (i.e. tube thickness, tube diameter, yield strength of steel and modulus of elasticity of steel) as network inputs and the ultimate pure bending as the only output. A sensitivity analysis is conducted on the ANN models to investigate the generalization ability (robustness) of the developed models, and predictions from the ANN models are compared with those obtained from most available codes and standards. To facilitate the use of the developed ANN models, they are translated into design equations suitable for spreadsheet programming or hand calculations. The results indicate that ANNs are capable of predicting the ultimate bending capacity of steel circular tubes with a high degree of accuracy, and outperform most available codes and standards. Crown

Journal of Computing in Civil Engineering, 2004

In recent years, artificial neural networks ͑ANNs͒ have been applied to many geotechnical enginee... more In recent years, artificial neural networks ͑ANNs͒ 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: ͑1͒ random data division; ͑2͒ data division to ensure statistical consistency of the subsets needed for ANN model development; ͑3͒ data division using self-organizing maps ͑SOMs͒; and ͑4͒ 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.