Assessment of local earthen bricks in perspective of physical and mechanical properties using Geographical Information System in Peshawar, Pakistan

Ali, Mujahid; Room, Shah; Khan, Muhammad Imran; Masood, Faisal; Memon, Riaz A.; Khan, Razaullah; Memon, Abdul Muhaimin

doi:10.1016/j.istruc.2020.10.075

Cited by 28 publications

(11 citation statements)

References 22 publications

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“…Silicon metal is a semi-metallic element having several characteristics of metals. After oxygen, silica is the second most readily available element in the Earth’s crust in various forms of silicon dioxide or silicates but is readily available in its pure state [ 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Machine Learning Techniques

et al. 2021

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Silica fume (SF) is a frequently used mineral admixture in producing sustainable concrete in the construction sector. Incorporating SF as a partial substitution of cement in concrete has obvious advantages, including reduced CO2 emission, cost-effective concrete, enhanced durability, and mechanical properties. Due to ever-increasing environmental concerns, the development of predictive machine learning (ML) models requires time. Therefore, the present study focuses on developing modeling techniques in predicting the compressive strength of silica fume concrete. The employed techniques include decision tree (DT) and support vector machine (SVM). An extensive and reliable database of 283 compressive strengths was established from the available literature information. The six most influential factors, i.e., cement, fine aggregate, coarse aggregate, water, superplasticizer, and silica fume, were considered as significant input parameters. The evaluation of models was performed by different statistical parameters, such as mean absolute error (MAE), root mean squared error (RMSE), root mean squared log error (RMSLE), and coefficient of determination (R2). Individual and ensemble models of DT and SVM showed satisfactory results with high prediction accuracy. Statistical analyses indicated that DT models bested SVM for predicting compressive strength. Ensemble modeling showed an enhancement of 11 percent and 1.5 percent for DT and SVM compressive strength models, respectively, as depicted by statistical parameters. Moreover, sensitivity analyses showed that cement and water are the governing parameters in developing compressive strength. A cross-validation technique was used to avoid overfitting issues and confirm the generalized modeling output. ML algorithms are used to predict SFC compressive strength to promote the use of green concrete.

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Section: Introductionmentioning

confidence: 99%

Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Machine Learning Techniques

et al. 2021

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“…Meanwhile, for the safest side, a figure of 0.5 for P should be used for the population proportion. The value of P = 0.5 will provide the largest sample size in simple random sampling techniques [42,43]. Based on the equation, a total of 436 respondents samples need to be collected, representing 0.017% of the total population of the inner area in 2013.…”

Section: The Bandung Metropolitan Area (Bma) Data Setmentioning

confidence: 99%

Time-Use and Spatio-Temporal Variables Influence on Physical Activity Intensity, Physical and Social Health of Travelers

Ali

Dharmowijoyo

Azevedo³

et al. 2021

Sustainability

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Using a multi-dimensional three-week household time-use and activity diary, this study aims to investigate the interaction between time-use and activity travel participation, built environment, leisure-time physical activity, travel parameters, and physical intensity on physical and social health. The relationship between time-use and activity travel participation is complex. Therefore, physical activity (PA) intensity is assumed to intermediate the relationship between endogenuous and exogenous variables. This study use a comprehensive set of data that was collected at a household level for twenty-one (21) consecutive days. A total of 732 individuals and 191 households were recorded, representing 0.029% total population of Bandung Metropolitan Area (BMA). The data analyzed with descriptive and linear regression analysis using Statistical Package for Social Sciences SPSS version 26.0.0 software (IBM: Armonk, NY, USA). An advanced model, such as the hierarchical Structural Equation Model (SEM), is used to validate the relationship between activity patterns and health parameters. The estimated results indicate that a minute increase in public transport mode has an 8.8% positive correlation with physical health and 9.0% with social health. Furthermore, an increase in the one-minute duration of in-home maintenance and out-of-home leisure activities are positively correlated by 2.9% and 3.2%, respectively, with moderate-intensity PA and by 4.5% and 1.8% strenuous-intensity PA. Additionally, high accessibility and availability of basic amenities at a walkable distance and using auxiliary time in social activities are significantly correlated with better physical and social health. Moreover, this study adopted multidisciplinary approaches for better transport policy and a healthier society with a better quality of life.

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“…The list of technologies related to IR 4.0 is expansive. The leading technologies associated with IR 4.0 include augmented reality, cloud computing, cyber-physical systems, big data, additive manufacturing, cybersecurity, smart robotics, modeling and simulation, and system integration [19,20]. Cyber-physical systems comprise transformative technologies for handling interconnected systems by linking physical resources and computational technologies.…”

Section: Enabling Technologies For Ir 40mentioning

confidence: 99%

Real-Time Implementation of a Fully Automated Industrial System Based on IR 4.0 Concept

Salah¹

2021

Actuators

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With the advent of modern communication and control strategies, existing industrial enterprises are now being transformed as per Industrial Revolution (IR) 4.0 standards to maximize production rates and monetary gains. To cope with the pace of the modern technological revolution, the Government of Saudi Arabia has launched “Vision 2030”. This research article presents the full automation process of an existing production line at the College of Engineering, King Saud University, as per “Vision 2030” guidelines. Initially, a production line was designed to produce flavored yogurt bottles from a user-defined flavor and plain yogurt mixture. The research project was completed in two phases. During phase I, smart sensing, control, and automation equipment were used to minimize human intervention, the so-called semi-automated mode of operation. A bottle-feeding mechanism and robotic arms were later integrated to eliminate human intervention during the second phase. Moreover, during phase II, Node-RED, Telegram Bots, and a Raspberry Pi 4 controller were used to achieve IoT-based monitoring and control as per Industry 4.0 requirements. A comparative performance analysis was conducted between semi-automated and fully automated modes of operation to demonstrate the benefits of the fully automated operational mode. The performance of the fully automated system was found to be superior in comparison with the semi-automated system.

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Assessment of local earthen bricks in perspective of physical and mechanical properties using Geographical Information System in Peshawar, Pakistan

Cited by 28 publications

References 22 publications

Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Machine Learning Techniques

Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Machine Learning Techniques

Time-Use and Spatio-Temporal Variables Influence on Physical Activity Intensity, Physical and Social Health of Travelers

Real-Time Implementation of a Fully Automated Industrial System Based on IR 4.0 Concept

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