An investigation of correlation factors linking footing resistance on sand with cone penetration test results
- Gavin, Kenneth, Tolooiyan, Ali
- Authors: Gavin, Kenneth , Tolooiyan, Ali
- Date: 2012
- Type: Text , Journal article
- Relation: Computers and Geotechnics Vol. 46, no. (2012), p. 84-92
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- Description: Significant research effort has led to improvements in our ability to estimate the ultimate bearing resistance of footings in sand. These techniques often estimate the footing resistance at relatively large displacements, typically 10% of the footing width, q b0.1. Cone Penetration Test (CPT) design methods typically link q b0.1 and q c through a constant reduction factor, α. A range of α factors for shallow footings have been proposed, some methods suggest that α is constant and while others that it varies with footing width and depth (or stress level). There is a dearth of field data with which to compare these correlation factors, in particular where foundation width and depth have been varied in the same ground conditions. For this reason finite element analyses have proven to be a useful tool for performing the parametric studies required to asses factors controlling α. This paper describes the results of numerical analyses performed to investigate α factors for soil profiles which were calibrated using the results of the CPT tests performed at a dense sand test-bed site. The numerical model was first used to perform parametric analyses to consider the effect of footing width, B and footing depth, D on the α factor mobilised in dense Blessington sand. In order to assess the effects of relative density, footing tests in a range of natural sands with variable in situ densities were modeled. The results of the finite element analyses suggest that a direct correlation between q b0.1 and q c can be established at a given test site which is independent of footing width and depth and is relatively weakly dependent on the sands relative density if the zone of influence of the foundation considered is large enough. © 2012 Elsevier Ltd.
- Authors: Gavin, Kenneth , Tolooiyan, Ali
- Date: 2012
- Type: Text , Journal article
- Relation: Computers and Geotechnics Vol. 46, no. (2012), p. 84-92
- Full Text:
- Reviewed:
- Description: Significant research effort has led to improvements in our ability to estimate the ultimate bearing resistance of footings in sand. These techniques often estimate the footing resistance at relatively large displacements, typically 10% of the footing width, q b0.1. Cone Penetration Test (CPT) design methods typically link q b0.1 and q c through a constant reduction factor, α. A range of α factors for shallow footings have been proposed, some methods suggest that α is constant and while others that it varies with footing width and depth (or stress level). There is a dearth of field data with which to compare these correlation factors, in particular where foundation width and depth have been varied in the same ground conditions. For this reason finite element analyses have proven to be a useful tool for performing the parametric studies required to asses factors controlling α. This paper describes the results of numerical analyses performed to investigate α factors for soil profiles which were calibrated using the results of the CPT tests performed at a dense sand test-bed site. The numerical model was first used to perform parametric analyses to consider the effect of footing width, B and footing depth, D on the α factor mobilised in dense Blessington sand. In order to assess the effects of relative density, footing tests in a range of natural sands with variable in situ densities were modeled. The results of the finite element analyses suggest that a direct correlation between q b0.1 and q c can be established at a given test site which is independent of footing width and depth and is relatively weakly dependent on the sands relative density if the zone of influence of the foundation considered is large enough. © 2012 Elsevier Ltd.
Differential evolution algorithm for predicting blast induced ground vibrations
- Saadat, Mahdi, Hasanzade, Ali, Khandelwal, Manoj
- Authors: Saadat, Mahdi , Hasanzade, Ali , Khandelwal, Manoj
- Date: 2015
- Type: Text , Journal article
- Relation: International Journal of Rock Mechanics and Mining Sciences Vol. 77, no. (2015), p. 97-104
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- Description: 1. Introduction One of the most crucial problems in construction blasting is to predict and then mitigate the ground vibration [1]. Blast-induced ground vibration is considered as one of the most important environmental hazards of mining operations and civil engineering projects. Intense vibration can cause critical damage to structures and plants nearby the open-pit mines, dams, and mine slopes, etc [2] and [3]. Researchers who deals with this undesirable phenomenon take into account various range of parameters in order to mitigate the detrimental effects of blasting. Blast influencing parameters can be divided into two categories [2]: (a) Uncontrollable parameters, such as geological and geotechnical characteristics of the rockmass. (b) Controllable parameters, such as burden, spacing, stemming, sub-drilling, delay time, etc.
- Authors: Saadat, Mahdi , Hasanzade, Ali , Khandelwal, Manoj
- Date: 2015
- Type: Text , Journal article
- Relation: International Journal of Rock Mechanics and Mining Sciences Vol. 77, no. (2015), p. 97-104
- Full Text:
- Reviewed:
- Description: 1. Introduction One of the most crucial problems in construction blasting is to predict and then mitigate the ground vibration [1]. Blast-induced ground vibration is considered as one of the most important environmental hazards of mining operations and civil engineering projects. Intense vibration can cause critical damage to structures and plants nearby the open-pit mines, dams, and mine slopes, etc [2] and [3]. Researchers who deals with this undesirable phenomenon take into account various range of parameters in order to mitigate the detrimental effects of blasting. Blast influencing parameters can be divided into two categories [2]: (a) Uncontrollable parameters, such as geological and geotechnical characteristics of the rockmass. (b) Controllable parameters, such as burden, spacing, stemming, sub-drilling, delay time, etc.
Modelling a biorefinery concept producing carbon fibre-polybutylene succinate composite foam
- Ghayur, Adeel, Verheyen, Vincent
- Authors: Ghayur, Adeel , Verheyen, Vincent
- Date: 2019
- Type: Text , Journal article
- Relation: Chemical Engineering Science Vol. 209, no. (2019), p. 1-7
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- Description: In this study, a novel biorefinery concept producing carbon fibre-poly(butylene succinate) composite foam (CPC foam) from lignocellulose and CO 2 is modelled. The biodegradable nature of poly(butylene succinate) would allow for easy carbon fibre recovery from the CPC foam for reuse at the end of product lifecycle, thus allowing for a circular materials flow. Technical simulation results show the biorefinery consumes 417 kg of biomass, 33 kg of CO 2 , 86 kg of methanol, 23 kg of acetic anhydride, 130 kWh of electricity and 1166 kW of heat per hour. The facility generates 72 kg of CPC foam, 82 kg of carbon fibre, 24 kg of tetrahydrofuran and 50 kg of dimethyl ether (DME). DME is used to fulfil parasitic electricity requirement. These results demonstrate the technical viability of this biorefinery although, research is needed to reduce parasitic energy demand. This carbon negative biorefinery avoids carcinogens and halogens for polymeric materials synthesis by utilising green chemistry principles and lignocellulose feedstock.
- Authors: Ghayur, Adeel , Verheyen, Vincent
- Date: 2019
- Type: Text , Journal article
- Relation: Chemical Engineering Science Vol. 209, no. (2019), p. 1-7
- Full Text:
- Reviewed:
- Description: In this study, a novel biorefinery concept producing carbon fibre-poly(butylene succinate) composite foam (CPC foam) from lignocellulose and CO 2 is modelled. The biodegradable nature of poly(butylene succinate) would allow for easy carbon fibre recovery from the CPC foam for reuse at the end of product lifecycle, thus allowing for a circular materials flow. Technical simulation results show the biorefinery consumes 417 kg of biomass, 33 kg of CO 2 , 86 kg of methanol, 23 kg of acetic anhydride, 130 kWh of electricity and 1166 kW of heat per hour. The facility generates 72 kg of CPC foam, 82 kg of carbon fibre, 24 kg of tetrahydrofuran and 50 kg of dimethyl ether (DME). DME is used to fulfil parasitic electricity requirement. These results demonstrate the technical viability of this biorefinery although, research is needed to reduce parasitic energy demand. This carbon negative biorefinery avoids carcinogens and halogens for polymeric materials synthesis by utilising green chemistry principles and lignocellulose feedstock.
Guidelines for safe cable crossing over a pipeline
- Reda, Ahmed, Rawlinson, Andrew, Sultan, Ibrahim, Elgazzar, Mohammed, Howard, Ian
- Authors: Reda, Ahmed , Rawlinson, Andrew , Sultan, Ibrahim , Elgazzar, Mohammed , Howard, Ian
- Date: 2020
- Type: Text , Journal article
- Relation: Applied Ocean Research Vol. 102, no. (2020), p.
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- Description: High voltage submarine cables are increasingly being installed in existing and new offshore oil and gas fields for power supply and control purposes. These power cables are both large and with a high submerged weight, which poses a challenge when designing a safe, maintenance free (economical), and fit-for-purpose crossing over a pipeline. Damage to subsea pipeline crossings caused by deterioration of a crossing support, field joint materials and cover components is well known in the industry, particularly with old pipelines. Crossing cables over an existing pipeline should be avoided whenever economical and practical. However, it is inevitable in some situations to use the existing pipeline (unburied) as the crossing support to a new cable/umbilical. In these situations, crossing the cable/umbilical over the existing pipeline may be a cost-effective and worthy consideration. However, there are no explicit guidelines or criteria in the industry concerning the acceptable practice of design and construction of crossings. The only clear recommendation is relating to pipeline separation distances. This paper documents a recent case study of damage of a field joint coating at a crossing of an existing pipeline by a 132 kV subsea cable of 191 mm outside diameter. Investigation of the damage on site revealed that it was caused by lateral movement of the cable under the influence of hydrodynamic forces. Further to investigation and assessment of the damage of the case study presented here, the paper proposes some guidelines for the safe design and construction of cable crossing. Another objective of this paper is to invite further evaluation of the proposed guidelines so that appropriate crossing design requirements can be further developed and standardised. © 2020 Elsevier Ltd
- Authors: Reda, Ahmed , Rawlinson, Andrew , Sultan, Ibrahim , Elgazzar, Mohammed , Howard, Ian
- Date: 2020
- Type: Text , Journal article
- Relation: Applied Ocean Research Vol. 102, no. (2020), p.
- Full Text:
- Reviewed:
- Description: High voltage submarine cables are increasingly being installed in existing and new offshore oil and gas fields for power supply and control purposes. These power cables are both large and with a high submerged weight, which poses a challenge when designing a safe, maintenance free (economical), and fit-for-purpose crossing over a pipeline. Damage to subsea pipeline crossings caused by deterioration of a crossing support, field joint materials and cover components is well known in the industry, particularly with old pipelines. Crossing cables over an existing pipeline should be avoided whenever economical and practical. However, it is inevitable in some situations to use the existing pipeline (unburied) as the crossing support to a new cable/umbilical. In these situations, crossing the cable/umbilical over the existing pipeline may be a cost-effective and worthy consideration. However, there are no explicit guidelines or criteria in the industry concerning the acceptable practice of design and construction of crossings. The only clear recommendation is relating to pipeline separation distances. This paper documents a recent case study of damage of a field joint coating at a crossing of an existing pipeline by a 132 kV subsea cable of 191 mm outside diameter. Investigation of the damage on site revealed that it was caused by lateral movement of the cable under the influence of hydrodynamic forces. Further to investigation and assessment of the damage of the case study presented here, the paper proposes some guidelines for the safe design and construction of cable crossing. Another objective of this paper is to invite further evaluation of the proposed guidelines so that appropriate crossing design requirements can be further developed and standardised. © 2020 Elsevier Ltd
Intelligent modeling of blast-induced rock movement prediction using dimensional analysis and optimized artificial neural network technique
- Yu, Zhi, Shi, Xiaohu, Miao, Xiaohu, Zhou, Jian, Khandelwal, Manoj
- Authors: Yu, Zhi , Shi, Xiaohu , Miao, Xiaohu , Zhou, Jian , Khandelwal, Manoj
- Date: 2021
- Type: Text , Journal article
- Relation: International Journal of Rock Mechanics and Mining Sciences Vol. 143, no. (2021), p.
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- Description: For maximum metal recovery, considering the movement of ore and waste during the blasting process in loading design is meaningful for reducing ore loss and ore dilution in an open-pit mine. The blast-induced rock movement (BIRM) can be directly measured; nevertheless, it is time-consuming and relative expensive. To solve this problem, a novel intelligent prediction model was proposed by using dimensional analysis and optimized artificial neural network technique in this paper based on the BIRM monitoring test in Husab Uranium Mine, Namibia and Phoenix Mine, USA. After using dimensional analysis, five input variables and one output variable were determined with both considering the dimension and physical meaning of each dimensionless variable. Then, artificial neural network technique (ANN) technique was utilized to develop an accurate prediction model, and a metaheuristic algorithm namely the Equilibrium Optimizer (EO) algorithm was applied to search the optimal hyper-parameter combination. For comparison aims, a linear model and a non-linear regression model were also performed, and the comparison results show that the provided hybrid ANN-based model can yield better prediction performance. As a result, it can be concluded that the developed intelligent model in this article has the potential to predict BIRM during bench blasting, and the analysis method and modeling process in this paper can provide a reference for solving other engineering problems. © 2021 Elsevier Ltd. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Manoj Khandelwal” is provided in this record**
- Authors: Yu, Zhi , Shi, Xiaohu , Miao, Xiaohu , Zhou, Jian , Khandelwal, Manoj
- Date: 2021
- Type: Text , Journal article
- Relation: International Journal of Rock Mechanics and Mining Sciences Vol. 143, no. (2021), p.
- Full Text:
- Reviewed:
- Description: For maximum metal recovery, considering the movement of ore and waste during the blasting process in loading design is meaningful for reducing ore loss and ore dilution in an open-pit mine. The blast-induced rock movement (BIRM) can be directly measured; nevertheless, it is time-consuming and relative expensive. To solve this problem, a novel intelligent prediction model was proposed by using dimensional analysis and optimized artificial neural network technique in this paper based on the BIRM monitoring test in Husab Uranium Mine, Namibia and Phoenix Mine, USA. After using dimensional analysis, five input variables and one output variable were determined with both considering the dimension and physical meaning of each dimensionless variable. Then, artificial neural network technique (ANN) technique was utilized to develop an accurate prediction model, and a metaheuristic algorithm namely the Equilibrium Optimizer (EO) algorithm was applied to search the optimal hyper-parameter combination. For comparison aims, a linear model and a non-linear regression model were also performed, and the comparison results show that the provided hybrid ANN-based model can yield better prediction performance. As a result, it can be concluded that the developed intelligent model in this article has the potential to predict BIRM during bench blasting, and the analysis method and modeling process in this paper can provide a reference for solving other engineering problems. © 2021 Elsevier Ltd. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Manoj Khandelwal” is provided in this record**
Developing a hybrid model of Jaya algorithm-based extreme gradient boosting machine to estimate blast-induced ground vibrations
- Zhou, Jian, Qiu, Yingui, Khandelwal, Manoj, Zhu, Shuangli, Zhang, Xiliang
- Authors: Zhou, Jian , Qiu, Yingui , Khandelwal, Manoj , Zhu, Shuangli , Zhang, Xiliang
- Date: 2021
- Type: Text , Journal article
- Relation: International Journal of Rock Mechanics and Mining Sciences Vol. 145, no. (2021), p.
- Full Text:
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- Description: Blasting is still being considered to be one the most important applicable alternatives for conventional excavations. Ground vibration generated due to blasting is an undesirable phenomenon which is harmful for the nearby structures and should be prevented. In this regard, a novel intelligent approach for predicting blast-induced PPV was developed. The distinctive Jaya algorithm and high efficient extreme gradient boosting machine (XGBoost) were applied to obtain the goal, called the Jaya-XGBoost model. Accordingly, 150 sets of data composed of 13 controllable and uncontrollable parameters are chosen as input independent variables and the measured peak particle velocity (PPV) is chosen as an output dependent variable. Also, the Jaya algorithm was used for optimization of hyper-parameters of XGBoost. Additionally, six empirical models and several machine learning models such as XGBoost, random forest, AdaBoost, artificial neural network and Bagging were also considered and applied for comparison of the proposed Jaya-XGBoost model. Accuracy criteria including determination coefficient (R2), root-mean-square error (RMSE), mean absolute error (MAE), and the variance accounted for (VAF) were used for the assessment of models. For this study, 150 blasting operations were analyzed. Also, the Shapley Additive Explanations (SHAP) method is used to interpret the importance of features and their contribution to PPV prediction. Findings reveal that the proposed Jaya-XGBoost emerged as the most reliable model in contrast to other machine learning models and traditional empirical models. This study may be helpful to mining researchers and engineers who use intelligent machine learning algorithms to predict blast-induced ground vibration. © 2021 Elsevier Ltd
- Authors: Zhou, Jian , Qiu, Yingui , Khandelwal, Manoj , Zhu, Shuangli , Zhang, Xiliang
- Date: 2021
- Type: Text , Journal article
- Relation: International Journal of Rock Mechanics and Mining Sciences Vol. 145, no. (2021), p.
- Full Text:
- Reviewed:
- Description: Blasting is still being considered to be one the most important applicable alternatives for conventional excavations. Ground vibration generated due to blasting is an undesirable phenomenon which is harmful for the nearby structures and should be prevented. In this regard, a novel intelligent approach for predicting blast-induced PPV was developed. The distinctive Jaya algorithm and high efficient extreme gradient boosting machine (XGBoost) were applied to obtain the goal, called the Jaya-XGBoost model. Accordingly, 150 sets of data composed of 13 controllable and uncontrollable parameters are chosen as input independent variables and the measured peak particle velocity (PPV) is chosen as an output dependent variable. Also, the Jaya algorithm was used for optimization of hyper-parameters of XGBoost. Additionally, six empirical models and several machine learning models such as XGBoost, random forest, AdaBoost, artificial neural network and Bagging were also considered and applied for comparison of the proposed Jaya-XGBoost model. Accuracy criteria including determination coefficient (R2), root-mean-square error (RMSE), mean absolute error (MAE), and the variance accounted for (VAF) were used for the assessment of models. For this study, 150 blasting operations were analyzed. Also, the Shapley Additive Explanations (SHAP) method is used to interpret the importance of features and their contribution to PPV prediction. Findings reveal that the proposed Jaya-XGBoost emerged as the most reliable model in contrast to other machine learning models and traditional empirical models. This study may be helpful to mining researchers and engineers who use intelligent machine learning algorithms to predict blast-induced ground vibration. © 2021 Elsevier Ltd
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