While damping devices can provide supplemental damping to mitigate building vibration due to wind or earthquake effects, integrating them into the design is more complex. For example, the Canadian code does not provide building designs with inline friction dampers. The objective of this present article was to study the overstrength, ductility, and response modification factors of concrete frame buildings with inline friction dampers in the Canadian context. For that purpose, a set of four-, eight-, and fourteen-story ductile concrete frames with inline seismic friction dampers, designed based on the 2015 National Building Code of Canada (NBCC), was considered. The analyses included pushover analysis in determining seismic characteristics and dynamic response history analysis using twenty-five ground motion records to assess the seismic performance of the buildings equipped with inline seismic friction dampers. The methodology considered diagonal braces, including different 6 m and 8 m span lengths. The discussion covers the prescribed design values for overstrength, ductility, and response modification factors, as well as the performance assessment of the buildings. The results revealed that increasing the height of the structure and reducing the span length increases the response modification factors.
{"title":"Seismic Resilience and Design Factors of Inline Seismic Friction Dampers (ISFDs)","authors":"Ali Naghshineh, A. Bagchi, F. M. Tehrani","doi":"10.3390/eng4030114","DOIUrl":"https://doi.org/10.3390/eng4030114","url":null,"abstract":"While damping devices can provide supplemental damping to mitigate building vibration due to wind or earthquake effects, integrating them into the design is more complex. For example, the Canadian code does not provide building designs with inline friction dampers. The objective of this present article was to study the overstrength, ductility, and response modification factors of concrete frame buildings with inline friction dampers in the Canadian context. For that purpose, a set of four-, eight-, and fourteen-story ductile concrete frames with inline seismic friction dampers, designed based on the 2015 National Building Code of Canada (NBCC), was considered. The analyses included pushover analysis in determining seismic characteristics and dynamic response history analysis using twenty-five ground motion records to assess the seismic performance of the buildings equipped with inline seismic friction dampers. The methodology considered diagonal braces, including different 6 m and 8 m span lengths. The discussion covers the prescribed design values for overstrength, ductility, and response modification factors, as well as the performance assessment of the buildings. The results revealed that increasing the height of the structure and reducing the span length increases the response modification factors.","PeriodicalId":10630,"journal":{"name":"Comput. Chem. Eng.","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83436989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Today’s roadways are subject to traffic congestion, the deterioration of surface-assets (often due to the overreliance on private vehicle traffic), increasing vehicle-operation and fuel costs, and pollutant emissions. In Abu Dhabi, private car traffic forms the major share on urban highways, as the infrastructure was built to a high quality and the public transport network needs expansion, resulting in traffic congestion on major highways. These issues are arguably addressable by appropriate decisions at the planning stage. Microsimulation modeling of driving behavior in Abu Dhabi is presented for empirical assessment of traffic management scenarios. This paper presents a technique for developing, calibrating, validating, and the scenario analysis of a detailed VISSIM-based microsimulation model of a 3.5 km section of a 5-lane divided highway in Abu Dhabi. Traffic-count data collected from two sources, i.e., the local transport department (year 2007) and municipality (2007 and 2015–2016) were used. Gaps in traffic-counts between ramps and the highway mainline were noted, which is a common occurrence in real-world data situations. A composite dataset for a representative week in 2015 was constructed, and the model was calibrated and validated with a 15% (<100 vehicles per hour) margin of error. Scenario analysis of a potential public bus transport service operating at 15 min headway and 40% capacity was assessed against the base case, for a 2015–2020 projected period. The results showed a significant capacity enhancement and improvement in the traffic flow. A reduction in the variation between vehicle travel times was observed for the bus-based scenario, as less bottlenecking and congestion were noted for automobiles in the mainline segments. The developed model could be used for further scenario analyses, to find optimized traffic management strategies over the highway’s lifecycle, whereas it could also be used for similar evaluations of other major roads in Abu Dhabi post-calibration.
{"title":"Microsimulation Modelling and Scenario Analysis of a Congested Abu Dhabi Highway","authors":"Umair Hasan, H. Aljassmi, Aisha Hasan","doi":"10.3390/eng4030113","DOIUrl":"https://doi.org/10.3390/eng4030113","url":null,"abstract":"Today’s roadways are subject to traffic congestion, the deterioration of surface-assets (often due to the overreliance on private vehicle traffic), increasing vehicle-operation and fuel costs, and pollutant emissions. In Abu Dhabi, private car traffic forms the major share on urban highways, as the infrastructure was built to a high quality and the public transport network needs expansion, resulting in traffic congestion on major highways. These issues are arguably addressable by appropriate decisions at the planning stage. Microsimulation modeling of driving behavior in Abu Dhabi is presented for empirical assessment of traffic management scenarios. This paper presents a technique for developing, calibrating, validating, and the scenario analysis of a detailed VISSIM-based microsimulation model of a 3.5 km section of a 5-lane divided highway in Abu Dhabi. Traffic-count data collected from two sources, i.e., the local transport department (year 2007) and municipality (2007 and 2015–2016) were used. Gaps in traffic-counts between ramps and the highway mainline were noted, which is a common occurrence in real-world data situations. A composite dataset for a representative week in 2015 was constructed, and the model was calibrated and validated with a 15% (<100 vehicles per hour) margin of error. Scenario analysis of a potential public bus transport service operating at 15 min headway and 40% capacity was assessed against the base case, for a 2015–2020 projected period. The results showed a significant capacity enhancement and improvement in the traffic flow. A reduction in the variation between vehicle travel times was observed for the bus-based scenario, as less bottlenecking and congestion were noted for automobiles in the mainline segments. The developed model could be used for further scenario analyses, to find optimized traffic management strategies over the highway’s lifecycle, whereas it could also be used for similar evaluations of other major roads in Abu Dhabi post-calibration.","PeriodicalId":10630,"journal":{"name":"Comput. Chem. Eng.","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87393953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The redesign of a failed hoisting shaft belonging to a 10 m stroke vertical transfer device (VTD) is presented. Firstly, the operation of the VTD is thoroughly analysed, the variation of loads and moments along the operating cycle is characterised, and transients such as emergency stop loads are calculated. The selection of safety factors and duty cycle factors was followed by the shaft sizing. After an initial rough sizing, the high-cycle fatigue (HCF) design for cyclic bending moments was performed, first considering constant torque and then considering cyclic torque. The number of bending and torsion cycles performed by the hoisting shaft over 10 years was shown to exceed 106, and an infinite life design is mandatory. The analyses showed that the initial shaft diameter was insufficient, thus justifying the failures observed before the present redesign. A classical fatigue model combining torsional shear stresses with bending stresses was used to take into account reversed torsional loading and ensure infinite fatigue life. This work highlights the need to thoroughly understand a machine’s operating cycle so that the wrong premises for fatigue design calculations are not assumed.
{"title":"Redesign of a Failed Hoisting Shaft of a Vertical Transfer Device","authors":"Filipe Alexandre Couto da Silva, P. D. de Castro","doi":"10.3390/eng4030112","DOIUrl":"https://doi.org/10.3390/eng4030112","url":null,"abstract":"The redesign of a failed hoisting shaft belonging to a 10 m stroke vertical transfer device (VTD) is presented. Firstly, the operation of the VTD is thoroughly analysed, the variation of loads and moments along the operating cycle is characterised, and transients such as emergency stop loads are calculated. The selection of safety factors and duty cycle factors was followed by the shaft sizing. After an initial rough sizing, the high-cycle fatigue (HCF) design for cyclic bending moments was performed, first considering constant torque and then considering cyclic torque. The number of bending and torsion cycles performed by the hoisting shaft over 10 years was shown to exceed 106, and an infinite life design is mandatory. The analyses showed that the initial shaft diameter was insufficient, thus justifying the failures observed before the present redesign. A classical fatigue model combining torsional shear stresses with bending stresses was used to take into account reversed torsional loading and ensure infinite fatigue life. This work highlights the need to thoroughly understand a machine’s operating cycle so that the wrong premises for fatigue design calculations are not assumed.","PeriodicalId":10630,"journal":{"name":"Comput. Chem. Eng.","volume":"98 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80901180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The necessity for environmentally friendly transportation systems has prompted the proliferation of electric vehicles (EVs) in low-voltage (LV) distribution networks. However, large-scale integration and simultaneous charging of EVs can create power quality challenges for the distribution grid. It is therefore important to assess the impact of connecting EVs for charging in existing distribution networks and determine the hosting capacity (HC) of such a network. This paper uses a deterministic time-series method and stochastic method based on a simplified Monte Carlo simulation to estimate the HC of single-phase and three-phase EV charging, respectively, for a South African low-voltage distribution network containing 21 households. Voltage drop and equipment loading are the performance indices (PI) considered for the impact assessment and HC estimation in this study. The impact assessment result confirms that increasing EV charging penetration will result in a corresponding movement of the PIs toward the allowable limits. The results show that the HC is 5–8 three-phase EVs charging simultaneously for the worst-case scenario and 9–13 EVs for the best-case scenario. Furthermore, the single-phase HC for the popular 3.7 kW EV charger is 15 and 8 EVs for the best-case and worst-case scenarios, respectively. The result showing the seasonal variation in HC and for other EV charging power is also presented. The difference in HC for the worst-case and best-case scenarios portrays the effect that the location of charging has on the HC.
{"title":"Hosting Capacity Assessment of South African Residential Low-Voltage Networks for Electric Vehicle Charging","authors":"V. Umoh, Abayomi Adebiyi, K. Moloi","doi":"10.3390/eng4030111","DOIUrl":"https://doi.org/10.3390/eng4030111","url":null,"abstract":"The necessity for environmentally friendly transportation systems has prompted the proliferation of electric vehicles (EVs) in low-voltage (LV) distribution networks. However, large-scale integration and simultaneous charging of EVs can create power quality challenges for the distribution grid. It is therefore important to assess the impact of connecting EVs for charging in existing distribution networks and determine the hosting capacity (HC) of such a network. This paper uses a deterministic time-series method and stochastic method based on a simplified Monte Carlo simulation to estimate the HC of single-phase and three-phase EV charging, respectively, for a South African low-voltage distribution network containing 21 households. Voltage drop and equipment loading are the performance indices (PI) considered for the impact assessment and HC estimation in this study. The impact assessment result confirms that increasing EV charging penetration will result in a corresponding movement of the PIs toward the allowable limits. The results show that the HC is 5–8 three-phase EVs charging simultaneously for the worst-case scenario and 9–13 EVs for the best-case scenario. Furthermore, the single-phase HC for the popular 3.7 kW EV charger is 15 and 8 EVs for the best-case and worst-case scenarios, respectively. The result showing the seasonal variation in HC and for other EV charging power is also presented. The difference in HC for the worst-case and best-case scenarios portrays the effect that the location of charging has on the HC.","PeriodicalId":10630,"journal":{"name":"Comput. Chem. Eng.","volume":"68 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89500713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
I. Mellal, A. Latrach, V. Rasouli, O. Bakelli, Abdesselem Dehdouh, H. Ouadi
Tight reservoirs around the world contain a significant volume of hydrocarbons; however, the heterogeneity of these reservoirs limits the recovery of the original oil in place to less than 20%. Accurate characterization is therefore needed to understand variations in reservoir properties and their effects on production. Water saturation (Sw) has always been challenging to estimate in ultra-tight reservoirs such as the Bakken Formation due to the inaccuracy of resistivity-based methods. While machine learning (ML) has proven to be a powerful tool for predicting rock properties in many tight formations, few studies have been conducted in reservoirs of similar complexity to the Bakken Formation, which is an ultra-tight, multimineral, low-resistivity reservoir. This study presents a workflow for Sw prediction using well logs, core data, and ML algorithms. Logs and core data were gathered from 29 wells drilled in the Bakken Formation. Due to the inaccuracy and lack of robustness of the tried and tested regression models (e.g., linear regression, random forest regression) in predicting Sw as a continuous variable, the problem was reformulated as a classification task. Instead of exact values, the Sw predictions were made in intervals of 10% increments representing 10 classes from 0% to 100%. Gradient boosting and random forest classifiers scored the best classification accuracy, and these two models were used to construct a voting classifier that achieved the best accuracy of 85.53%. The ML model achieved much better accuracy than conventional resistivity-based methods. By conducting this study, we aim to develop a new workflow to improve the prediction of Sw in reservoirs where conventional methods have poor performance.
{"title":"Water Saturation Prediction in the Middle Bakken Formation Using Machine Learning","authors":"I. Mellal, A. Latrach, V. Rasouli, O. Bakelli, Abdesselem Dehdouh, H. Ouadi","doi":"10.3390/eng4030110","DOIUrl":"https://doi.org/10.3390/eng4030110","url":null,"abstract":"Tight reservoirs around the world contain a significant volume of hydrocarbons; however, the heterogeneity of these reservoirs limits the recovery of the original oil in place to less than 20%. Accurate characterization is therefore needed to understand variations in reservoir properties and their effects on production. Water saturation (Sw) has always been challenging to estimate in ultra-tight reservoirs such as the Bakken Formation due to the inaccuracy of resistivity-based methods. While machine learning (ML) has proven to be a powerful tool for predicting rock properties in many tight formations, few studies have been conducted in reservoirs of similar complexity to the Bakken Formation, which is an ultra-tight, multimineral, low-resistivity reservoir. This study presents a workflow for Sw prediction using well logs, core data, and ML algorithms. Logs and core data were gathered from 29 wells drilled in the Bakken Formation. Due to the inaccuracy and lack of robustness of the tried and tested regression models (e.g., linear regression, random forest regression) in predicting Sw as a continuous variable, the problem was reformulated as a classification task. Instead of exact values, the Sw predictions were made in intervals of 10% increments representing 10 classes from 0% to 100%. Gradient boosting and random forest classifiers scored the best classification accuracy, and these two models were used to construct a voting classifier that achieved the best accuracy of 85.53%. The ML model achieved much better accuracy than conventional resistivity-based methods. By conducting this study, we aim to develop a new workflow to improve the prediction of Sw in reservoirs where conventional methods have poor performance.","PeriodicalId":10630,"journal":{"name":"Comput. Chem. Eng.","volume":"47 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91498825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Implementing multivariate predictive analysis to ascertain stream-water (SW) parameters including dissolved oxygen, specific conductance, discharge, water level, temperature, pH, and turbidity is crucial in the field of water resource management. This is especially important during a time of rapid climate change, where weather patterns are constantly changing, making it difficult to forecast these SW variables accurately for different water-related problems. Various numerical models based on physics are utilized to forecast the variables associated with surface water (SW). These models rely on numerous hydrologic parameters and require extensive laboratory investigation and calibration to minimize uncertainty. However, with the emergence of data-driven analysis and prediction methods, deep-learning algorithms have demonstrated satisfactory performance in handling sequential data. In this study, a comprehensive Exploratory Data Analysis (EDA) and feature engineering were conducted to prepare the dataset, ensuring optimal performance of the predictive model. A neural network regression model known as Long Short-Term Memory (LSTM) was trained using several years of daily data, enabling the prediction of SW variables up to one week in advance (referred to as lead time) with satisfactory accuracy. The model’s performance was evaluated by comparing the predicted data with observed data, analyzing the error distribution, and utilizing error matrices. Improved performance was achieved by increasing the number of epochs and fine-tuning hyperparameters. By applying proper feature engineering and optimization, this model can be adapted to other locations to facilitate univariate predictive analysis and potentially support the real-time prediction of SW variables.
{"title":"Multivariate Multi-Step Long Short-Term Memory Neural Network for Simultaneous Stream-Water Variable Prediction","authors":"M. Khosravi, Bushra Monowar Duti, Munshi Md. Shafwat Yazdan, Shima Ghoochani, Neda Nazemi, Hanieh Shabanian","doi":"10.3390/eng4030109","DOIUrl":"https://doi.org/10.3390/eng4030109","url":null,"abstract":"Implementing multivariate predictive analysis to ascertain stream-water (SW) parameters including dissolved oxygen, specific conductance, discharge, water level, temperature, pH, and turbidity is crucial in the field of water resource management. This is especially important during a time of rapid climate change, where weather patterns are constantly changing, making it difficult to forecast these SW variables accurately for different water-related problems. Various numerical models based on physics are utilized to forecast the variables associated with surface water (SW). These models rely on numerous hydrologic parameters and require extensive laboratory investigation and calibration to minimize uncertainty. However, with the emergence of data-driven analysis and prediction methods, deep-learning algorithms have demonstrated satisfactory performance in handling sequential data. In this study, a comprehensive Exploratory Data Analysis (EDA) and feature engineering were conducted to prepare the dataset, ensuring optimal performance of the predictive model. A neural network regression model known as Long Short-Term Memory (LSTM) was trained using several years of daily data, enabling the prediction of SW variables up to one week in advance (referred to as lead time) with satisfactory accuracy. The model’s performance was evaluated by comparing the predicted data with observed data, analyzing the error distribution, and utilizing error matrices. Improved performance was achieved by increasing the number of epochs and fine-tuning hyperparameters. By applying proper feature engineering and optimization, this model can be adapted to other locations to facilitate univariate predictive analysis and potentially support the real-time prediction of SW variables.","PeriodicalId":10630,"journal":{"name":"Comput. Chem. Eng.","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84462969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Hamadi, Tayeb El Mehadji, A. Laalam, N. Zeraibi, O. Tomomewo, H. Ouadi, Abdesselem Dehdouh
The accurate determination of key parameters, including the CO2-hydrocarbon solubility ratio (Rs), interfacial tension (IFT), and minimum miscibility pressure (MMP), is vital for the success of CO2-enhanced oil recovery (CO2-EOR) projects. This study presents a robust machine learning framework that leverages deep neural networks (MLP-Adam), support vector regression (SVR-RBF) and extreme gradient boosting (XGBoost) algorithms to obtained accurate predictions of these critical parameters. The models are developed and validated using a comprehensive database compiled from previously published studies. Additionally, an in-depth analysis of various factors influencing the Rs, IFT, and MMP is conducted to enhance our understanding of their impacts. Compared to existing correlations and alternative machine learning models, our proposed framework not only exhibits lower calculation errors but also provides enhanced insights into the relationships among the influencing factors. The performance evaluation of the models using statistical indicators revealed impressive coefficients of determination of unseen data (0.9807 for dead oil solubility, 0.9835 for live oil solubility, 0.9931 for CO2-n-Alkane interfacial tension, and 0.9648 for minimum miscibility pressure). One notable advantage of our models is their ability to predict values while accommodating a wide range of inputs swiftly and accurately beyond the limitations of common correlations. The dataset employed in our study encompasses diverse data, spanning from heptane (C7) to eicosane (C20) in the IFT dataset, and MMP values ranging from 870 psi to 5500 psi, covering the entire application range of CO2-EOR. This innovative and robust approach presents a powerful tool for predicting crucial parameters in CO2-EOR projects, delivering superior accuracy, speed, and data diversity compared to those of the existing methods.
{"title":"Prediction of Key Parameters in the Design of CO2 Miscible Injection via the Application of Machine Learning Algorithms","authors":"M. Hamadi, Tayeb El Mehadji, A. Laalam, N. Zeraibi, O. Tomomewo, H. Ouadi, Abdesselem Dehdouh","doi":"10.3390/eng4030108","DOIUrl":"https://doi.org/10.3390/eng4030108","url":null,"abstract":"The accurate determination of key parameters, including the CO2-hydrocarbon solubility ratio (Rs), interfacial tension (IFT), and minimum miscibility pressure (MMP), is vital for the success of CO2-enhanced oil recovery (CO2-EOR) projects. This study presents a robust machine learning framework that leverages deep neural networks (MLP-Adam), support vector regression (SVR-RBF) and extreme gradient boosting (XGBoost) algorithms to obtained accurate predictions of these critical parameters. The models are developed and validated using a comprehensive database compiled from previously published studies. Additionally, an in-depth analysis of various factors influencing the Rs, IFT, and MMP is conducted to enhance our understanding of their impacts. Compared to existing correlations and alternative machine learning models, our proposed framework not only exhibits lower calculation errors but also provides enhanced insights into the relationships among the influencing factors. The performance evaluation of the models using statistical indicators revealed impressive coefficients of determination of unseen data (0.9807 for dead oil solubility, 0.9835 for live oil solubility, 0.9931 for CO2-n-Alkane interfacial tension, and 0.9648 for minimum miscibility pressure). One notable advantage of our models is their ability to predict values while accommodating a wide range of inputs swiftly and accurately beyond the limitations of common correlations. The dataset employed in our study encompasses diverse data, spanning from heptane (C7) to eicosane (C20) in the IFT dataset, and MMP values ranging from 870 psi to 5500 psi, covering the entire application range of CO2-EOR. This innovative and robust approach presents a powerful tool for predicting crucial parameters in CO2-EOR projects, delivering superior accuracy, speed, and data diversity compared to those of the existing methods.","PeriodicalId":10630,"journal":{"name":"Comput. Chem. Eng.","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79312419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the interest aroused by the development of modern concretes such as printable or self-compacting concretes, a better understanding of the rheological behavior, directly linked to fresh state properties, seems essential. This paper aims to provide a phenomenological description of the rheological behavior of cement paste. The first part is devoted to the most common testing procedures that can be performed to characterize the rheological properties of cement suspensions. The second one deals with the complexities of the rheological behavior of cement paste including the non-linearity of flow behavior, the viscoelasticity and yielding, and the structural build-up over time.
{"title":"Rheological Behavior of Cement Paste: A Phenomenological State of the Art","authors":"Y. El Bitouri","doi":"10.3390/eng4030107","DOIUrl":"https://doi.org/10.3390/eng4030107","url":null,"abstract":"With the interest aroused by the development of modern concretes such as printable or self-compacting concretes, a better understanding of the rheological behavior, directly linked to fresh state properties, seems essential. This paper aims to provide a phenomenological description of the rheological behavior of cement paste. The first part is devoted to the most common testing procedures that can be performed to characterize the rheological properties of cement suspensions. The second one deals with the complexities of the rheological behavior of cement paste including the non-linearity of flow behavior, the viscoelasticity and yielding, and the structural build-up over time.","PeriodicalId":10630,"journal":{"name":"Comput. Chem. Eng.","volume":"53 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85730761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lorena Carias de Freitas Gomes, Henrique Comba Gomes, E. Reis
Considering the various problems caused by infiltration in civil construction, this study aimed to identify the most appropriate waterproofing methods for different types of surfaces. A study was conducted on the mechanisms of water infiltration on surfaces and the waterproofing methods available on the market, focusing on asphalt blankets, in addition to a literature review highlighting state-of-the-art methods on this topic. A case study was also conducted in a residence in Nova Lima, Brazil, analyzing different waterproofing techniques, including their characteristics and stages. Among the conclusions, it is highlighted that the implementation of adequate project, installation, inspection, and maintenance techniques can significantly reduce the waterproofing failure rate and repair costs, and that the excellent choice of materials, along with the skill of the labor force in the application, is fundamental to guarantee the adequate performance of these materials in buildings.
{"title":"Surface Waterproofing Techniques: A Case Study in Nova Lima, Brazil","authors":"Lorena Carias de Freitas Gomes, Henrique Comba Gomes, E. Reis","doi":"10.3390/eng4030106","DOIUrl":"https://doi.org/10.3390/eng4030106","url":null,"abstract":"Considering the various problems caused by infiltration in civil construction, this study aimed to identify the most appropriate waterproofing methods for different types of surfaces. A study was conducted on the mechanisms of water infiltration on surfaces and the waterproofing methods available on the market, focusing on asphalt blankets, in addition to a literature review highlighting state-of-the-art methods on this topic. A case study was also conducted in a residence in Nova Lima, Brazil, analyzing different waterproofing techniques, including their characteristics and stages. Among the conclusions, it is highlighted that the implementation of adequate project, installation, inspection, and maintenance techniques can significantly reduce the waterproofing failure rate and repair costs, and that the excellent choice of materials, along with the skill of the labor force in the application, is fundamental to guarantee the adequate performance of these materials in buildings.","PeriodicalId":10630,"journal":{"name":"Comput. Chem. Eng.","volume":"6 2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78310751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1016/j.compchemeng.2023.108347
B. Ammari, Emma S. Johnson, Georgia Stinchfield, Taehun Kim, M. Bynum, W. Hart, J. Pulsipher, C. Laird
{"title":"Linear model decision trees as surrogates in optimization of engineering applications","authors":"B. Ammari, Emma S. Johnson, Georgia Stinchfield, Taehun Kim, M. Bynum, W. Hart, J. Pulsipher, C. Laird","doi":"10.1016/j.compchemeng.2023.108347","DOIUrl":"https://doi.org/10.1016/j.compchemeng.2023.108347","url":null,"abstract":"","PeriodicalId":10630,"journal":{"name":"Comput. Chem. Eng.","volume":"18 1","pages":"108347"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87985885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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