Prediction of uniaxial compressive strength of carbonate rocks and cement mortar using artificial neural network and multiple linear regressions

Abdelhedi, Mohamed

doi:10.13168/agg.2020.0027

Cited by 15 publications

(3 citation statements)

References 39 publications

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“…Machine Learning can be applied to solve complex problems efficiently (Said and Erradi, 2019), (Abdelhedi et al, 2020), (Ayadi et al, 2020), (Jabbar et al, 2018). In this study, random forest (RF), a widely used machine learning technique (Wainberg, Alipanahi and Frey, 2016) is used.…”

Section: Feature Importance Analysismentioning

confidence: 99%

Transition to Sustainable Smart Energy: An analysis of human- building interactions in Qatar Using Machine Learning Techniques

Jabbar¹,

Zaidan²,

Said³

et al. 2021

Proceedings of the 57th ISOCARP World Planning Congress

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Policy Planning have the potential to contribute to the strategic development and economic diversification of developing countries even without considerable structural changes. In this study, we analysed a set of humanoriented dimensions aimed at improving energy policies related to the building sector in Qatar. Considering the high percentage of expatriate and migrant communities with different financial and cultural backgrounds and behavioral patterns compared with local communities in the GCC Union, it is required to investigate human dimensions to propose adequate energy policies. This study explored the correlations of socioeconomic, behavioral, and demographic dimensions to determine the main factors behind discrepancies in energy use, responsibilities, motivations, habits, and overall well-being. The sample included 2,200 people in Qatar, and it was clustered into two consumer categories: high and low. In particular, the study focused on exploring human indoor comfort perception dependencies with building features. Financial drivers, such as demand programs and energy subsidies, were explored in relation to behavioral patterns. Subsequently, the data analysis resulted in implications for energy policies regarding interventions, social well-being, and awareness. Machine learning methods were used to perform a feature importance analysis to determine the main factors of human behavior. The findings of this study demonstrated how human factors impact comfort perception in residential and work environments, norms, habits, self-responsibility, consequence awareness, and consumption. The study has important implications for developing targeted strategies aimed at improving the efficacy of energy policies and sustainability performance indicators.

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Section: Feature Importance Analysismentioning

confidence: 99%

Transition to Sustainable Smart Energy: An analysis of human- building interactions in Qatar Using Machine Learning Techniques

Jabbar¹,

Zaidan²,

Said³

et al. 2021

Proceedings of the 57th ISOCARP World Planning Congress

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“…AI has been applied in geoscience for the determination of reservoir rock properties, drilling optimization, and enhanced production facilities (Solanki et al, 2022). Furthermore, these techniques have been used in carbonate rock exploration for the prediction of rocks and mortar UCS (Uniaxial Compressive Strength) values (Abdelhedi et al, 2020). Additionally, AI has been applied in mining and geological engineering, including rock mechanics, mining method selection, mining equipment, drilling-blasting, slope stability, and environmental issues (Bui, Bui & Nguyen, 2021).…”

Section: Introductionmentioning

confidence: 99%

Exploration of Carbonate Aggregates in Road Construction using Ultrasonic and Artificial Intelligence Approaches

Abdelhedi,

Jabbar,

Abbes

2023

The International Conference on Civil Infrastructure and Construction

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The COVID-19 pandemic has significantly impacted the construction sector, which is highly sensitive to economic cycles. In order to boost value and efficiency in this sector, the use of innovative exploration technologies such as ultrasonic and Artificial Intelligence techniques in building material research is becoming increasingly crucial. In this study, we developed two models for predicting the Los Angeles (LA) and Micro Deval (MDE) coefficients, the two important geo-technical tests used to determine the quality of carbonate rock aggregates. These coefficients describe the resistance of aggregates to fragmentation and abrasion. The ultrasound velocity, porosity, and density of the rocks were determined and used as inputs to develop prediction models using multiple regressions and an artificial neural network. These models may be used to assess the quality of rock aggregates at the exploration stage without the need for tedious laboratory analysis.

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“…Machine Learning is able to extract and learn automatically from large-scale data using for example sophisticated Neural Networks (NNs). NNs are mainly used in Deep Learning algorithms that handily become state-of-theart across a range of difficult problem domains [17][18][19][20]. Thus, the use of these developed technologies can improve the monitoring process and achieve an efficient performance in recognizing animal behavior.…”

Section: Introductionmentioning

confidence: 99%

Dairy Cow rumination detection: A deep learning approach

Ayadi,

said,

Jabbar

et al. 2021

Preprint

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Cattle activity is an essential index for monitoring health and welfare of the ruminants. Thus, changes in the livestock behavior are a critical indicator for early detection and prevention of several diseases. Rumination behavior is a significant variable for tracking the development and yield of animal husbandry. Therefore, various monitoring methods and measurement equipment have been used to assess cattle behavior. However, these modern attached devices are invasive, stressful and uncomfortable for the cattle and can influence negatively the welfare and diurnal behavior of the animal. Multiple research efforts addressed the problem of rumination detection by adopting new methods by relying on visual features. However, they only use few postures of the dairy cow to recognize the rumination or feeding behavior. In this study, we introduce an innovative monitoring method using Convolution Neural Network (CNN)-based deep learning models. The classification process is conducted under two main labels: ruminating and other, using all cow postures captured by the monitoring camera. Our proposed system is simple and easy-to-use which is able to capture long-term dynamics using a compacted representation of a video in a single 2D image. This method proved efficiency in recognizing the rumination behavior with 95%, 98% and 98% of average accuracy, recall and precision, respectively.

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Prediction of uniaxial compressive strength of carbonate rocks and cement mortar using artificial neural network and multiple linear regressions

Cited by 15 publications

References 39 publications

Transition to Sustainable Smart Energy: An analysis of human- building interactions in Qatar Using Machine Learning Techniques

Transition to Sustainable Smart Energy: An analysis of human- building interactions in Qatar Using Machine Learning Techniques

Exploration of Carbonate Aggregates in Road Construction using Ultrasonic and Artificial Intelligence Approaches

Dairy Cow rumination detection: A deep learning approach

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