Pub Date : 2022-05-11DOI: 10.46604/ijeti.2022.9151
Chaoquan Ou, K. Ting, Nien-Tsung Lee, Wu-Chiao Shih
Field inspection is a traditional way to detect the problem of shaft imbalance or abnormal vibration in a ship propulsion system; however, the ship cannot execute any tasks or activities during calibration. This study develops a human-machine monitoring interface (HMMI) to estimate vibration abnormalities and implement an intelligent active balance correction to the propulsion system online. In this study, Arduino IDE, InduSoft, and LabVIEW are used to create a function monitored by HMMI. By comparing the abnormal vibration amplification of the moment of inertia, HMMI calculates the correct mass to reduce the vibration. The experimental results show that, after HMMI carries out continuous active balance correction online, the correction rate achieves 105.37%. This indicates that HMMI can calculate the amount of imbalance and phase angles and drive a counterweight to the correct balance position while the device is still operating.
{"title":"Intelligent Correction and Monitoring of Ship Propulsion Rotary Device Vibration","authors":"Chaoquan Ou, K. Ting, Nien-Tsung Lee, Wu-Chiao Shih","doi":"10.46604/ijeti.2022.9151","DOIUrl":"https://doi.org/10.46604/ijeti.2022.9151","url":null,"abstract":"Field inspection is a traditional way to detect the problem of shaft imbalance or abnormal vibration in a ship propulsion system; however, the ship cannot execute any tasks or activities during calibration. This study develops a human-machine monitoring interface (HMMI) to estimate vibration abnormalities and implement an intelligent active balance correction to the propulsion system online. In this study, Arduino IDE, InduSoft, and LabVIEW are used to create a function monitored by HMMI. By comparing the abnormal vibration amplification of the moment of inertia, HMMI calculates the correct mass to reduce the vibration. The experimental results show that, after HMMI carries out continuous active balance correction online, the correction rate achieves 105.37%. This indicates that HMMI can calculate the amount of imbalance and phase angles and drive a counterweight to the correct balance position while the device is still operating.","PeriodicalId":43808,"journal":{"name":"International Journal of Engineering and Technology Innovation","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47209232","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 : 2022-05-04DOI: 10.46604/ijeti.2022.8865
Yin Tong, Tou-Hong Lee, Kin‐Sam Yen
Deep learning (DL) approaches have received extensive attention in plant growth monitoring due to their ground-breaking performance in image classification; however, the approaches have yet to be fully explored. This review article, therefore, aims to provide a comprehensive overview of the work and the DL developments accomplished over the years. This work includes a brief introduction on plant growth monitoring and the image-based techniques used for phenotyping. The bottleneck in image analysis is discussed and the need of DL methods in plant growth monitoring is highlighted. A number of research works focused on DL based plant growth monitoring-related applications published since 2017 have been identified and included in this work for review. The results show that the advancement in DL approaches has driven plant growth monitoring towards more complicated schemes, from simple growth stages identification towards temporal growth information extraction. The challenges, such as resource-demanding data annotation, data-hungriness for training, and extraction of both spatial and temporal features simultaneously for accurate plant growth prediction, however, remain unsolved.
{"title":"Deep Learning for Image-Based Plant Growth Monitoring: A Review","authors":"Yin Tong, Tou-Hong Lee, Kin‐Sam Yen","doi":"10.46604/ijeti.2022.8865","DOIUrl":"https://doi.org/10.46604/ijeti.2022.8865","url":null,"abstract":"Deep learning (DL) approaches have received extensive attention in plant growth monitoring due to their ground-breaking performance in image classification; however, the approaches have yet to be fully explored. This review article, therefore, aims to provide a comprehensive overview of the work and the DL developments accomplished over the years. This work includes a brief introduction on plant growth monitoring and the image-based techniques used for phenotyping. The bottleneck in image analysis is discussed and the need of DL methods in plant growth monitoring is highlighted. A number of research works focused on DL based plant growth monitoring-related applications published since 2017 have been identified and included in this work for review. The results show that the advancement in DL approaches has driven plant growth monitoring towards more complicated schemes, from simple growth stages identification towards temporal growth information extraction. The challenges, such as resource-demanding data annotation, data-hungriness for training, and extraction of both spatial and temporal features simultaneously for accurate plant growth prediction, however, remain unsolved.","PeriodicalId":43808,"journal":{"name":"International Journal of Engineering and Technology Innovation","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43376015","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 : 2022-03-10DOI: 10.46604/ijeti.2022.8826
R. Tiwari, Ravindra K. Singh, N. Choudhary
The operating time of directional overcurrent relays (DOCRs) can be reduced with user-defined relay characteristics considering plug setting (PS), time multiplier setting (TMS), and relay characteristic coefficients (λ and γ). This study presents a comparative analysis of relay coordination with standard and user-defined relay characteristics. The proposed relay coordination scheme is formulated as a non-linear constraint optimization problem. The grey wolf optimization (GWO) algorithm is used to determine the optimal relay settings and total operating time of DOCRs. The performance of the proposed scheme is tested on the standard 8-bus, 9-bus, and 15-bus systems. The results show that the total operating time of DOCRs with user-defined relay characteristics is better than that with standard relay characteristics. The results of the GWO algorithm are compared with the performance of optimization techniques used in literature to solve the relay coordination problem.
{"title":"Optimal Relay Coordination for DG-Based Power System Using Standard and User-Defined Relay Characteristics","authors":"R. Tiwari, Ravindra K. Singh, N. Choudhary","doi":"10.46604/ijeti.2022.8826","DOIUrl":"https://doi.org/10.46604/ijeti.2022.8826","url":null,"abstract":"The operating time of directional overcurrent relays (DOCRs) can be reduced with user-defined relay characteristics considering plug setting (PS), time multiplier setting (TMS), and relay characteristic coefficients (λ and γ). This study presents a comparative analysis of relay coordination with standard and user-defined relay characteristics. The proposed relay coordination scheme is formulated as a non-linear constraint optimization problem. The grey wolf optimization (GWO) algorithm is used to determine the optimal relay settings and total operating time of DOCRs. The performance of the proposed scheme is tested on the standard 8-bus, 9-bus, and 15-bus systems. The results show that the total operating time of DOCRs with user-defined relay characteristics is better than that with standard relay characteristics. The results of the GWO algorithm are compared with the performance of optimization techniques used in literature to solve the relay coordination problem.","PeriodicalId":43808,"journal":{"name":"International Journal of Engineering and Technology Innovation","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45370043","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 : 2022-03-09DOI: 10.46604/ijeti.2022.8462
Priya S. Niranjan, Ravindra Kumar Singh, N. Choudhary
This study aims to analyze the optimal settings of directional overcurrent relays (DOCRs) for the protection of an alternating current (AC) microgrid in both islanded and grid-connected operation modes. In this context, two different types of objective functions are used for comparing the total operating time of all primary DOCRs. The optimal settings obtained in either mode of the microgrid are different due to the variable magnitude of fault currents. The proposed protection coordination scheme is formulated as a mixed-integer non-linear programming problem, and the settings are obtained using various optimization techniques such as firefly algorithm, simulated annealing algorithm, and genetic algorithm. The results show that the settings obtained in common operation modes are robust as no miscoordination of relays occurs in any of the operation modes.
{"title":"Comparative Study of Relay Coordination in a Microgrid with the Determination of Common Optimal Settings Based on Different Objective Functions","authors":"Priya S. Niranjan, Ravindra Kumar Singh, N. Choudhary","doi":"10.46604/ijeti.2022.8462","DOIUrl":"https://doi.org/10.46604/ijeti.2022.8462","url":null,"abstract":"This study aims to analyze the optimal settings of directional overcurrent relays (DOCRs) for the protection of an alternating current (AC) microgrid in both islanded and grid-connected operation modes. In this context, two different types of objective functions are used for comparing the total operating time of all primary DOCRs. The optimal settings obtained in either mode of the microgrid are different due to the variable magnitude of fault currents. The proposed protection coordination scheme is formulated as a mixed-integer non-linear programming problem, and the settings are obtained using various optimization techniques such as firefly algorithm, simulated annealing algorithm, and genetic algorithm. The results show that the settings obtained in common operation modes are robust as no miscoordination of relays occurs in any of the operation modes.","PeriodicalId":43808,"journal":{"name":"International Journal of Engineering and Technology Innovation","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47028223","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 : 2022-02-22DOI: 10.46604/ijeti.2022.8072
Sirish Kumar Pagoti, Bala Sai Srilatha Indira Dutt Vemuri, Mohammad Khaja Mohiddin
The accuracy of position estimation plays a key role in many of the precise positioning applications such as category I (CAT-I) aircraft landings, survey work, etc. To improve the accuracy of position estimation, a novel kinematic positioning algorithm designated as correntropy Kalman filter (CKF) is proposed in this study. Instead of minimum mean square error (MMSE), correntropy criterion (CC) is used as the optimality criterion of CKF. The prior estimates of the state and covariance matrix are computed in CKF and a novel fixed-point algorithm is then used to update the posterior estimates. The data of a dual-frequency global positioning system (GPS) receiver located at Indian Institute of Science (IISc), Bangalore (13.021°N/77.5°E) is collected from Scripps Orbit and Permanent Array Centre (SOPAC) to implement the proposed algorithm. The results of the proposed CKF algorithm are promising and exhibit significant improvement in position estimation compared to the conventional methods.
{"title":"Enhanced Kalman Filter Navigation Algorithm Based on Correntropy and Fixed-Point Update","authors":"Sirish Kumar Pagoti, Bala Sai Srilatha Indira Dutt Vemuri, Mohammad Khaja Mohiddin","doi":"10.46604/ijeti.2022.8072","DOIUrl":"https://doi.org/10.46604/ijeti.2022.8072","url":null,"abstract":"The accuracy of position estimation plays a key role in many of the precise positioning applications such as category I (CAT-I) aircraft landings, survey work, etc. To improve the accuracy of position estimation, a novel kinematic positioning algorithm designated as correntropy Kalman filter (CKF) is proposed in this study. Instead of minimum mean square error (MMSE), correntropy criterion (CC) is used as the optimality criterion of CKF. The prior estimates of the state and covariance matrix are computed in CKF and a novel fixed-point algorithm is then used to update the posterior estimates. The data of a dual-frequency global positioning system (GPS) receiver located at Indian Institute of Science (IISc), Bangalore (13.021°N/77.5°E) is collected from Scripps Orbit and Permanent Array Centre (SOPAC) to implement the proposed algorithm. The results of the proposed CKF algorithm are promising and exhibit significant improvement in position estimation compared to the conventional methods.","PeriodicalId":43808,"journal":{"name":"International Journal of Engineering and Technology Innovation","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43483811","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 : 2022-02-22DOI: 10.46604/ijeti.2022.8889
Jeng-Liang Lin
The objective of this study is to evaluate the activation energy of agricultural residues with their ignition characteristics. The ignition temperature of agricultural residues (peanut shell, rice hull, and rice straw) is determined by measuring particle temperature, particle luminosity, and gas temperature for samples weighing 2.0, 2.5, and 3.0 grams. The maximal slope of the particle temperature versus furnace temperature is used to determine the occurrence of ignition. Values of activation energy are analyzed by the Semenov model with the measured ignition temperature. Results show that the particle ignition temperature is 317, 324, and 330°C for rice straw, peanut shell, and rice hull, respectively. The results also indicate that the particle ignition temperature reduces as the volatile content increases and the sample amount decreases. The value of activation energy is 157.2, 170.3, and 192.8 kJ/mole for rice straw, peanut shell, and rice hull, respectively.
{"title":"Evaluation of Activation Energy for Agricultural Residues with Ignition Temperature","authors":"Jeng-Liang Lin","doi":"10.46604/ijeti.2022.8889","DOIUrl":"https://doi.org/10.46604/ijeti.2022.8889","url":null,"abstract":"The objective of this study is to evaluate the activation energy of agricultural residues with their ignition characteristics. The ignition temperature of agricultural residues (peanut shell, rice hull, and rice straw) is determined by measuring particle temperature, particle luminosity, and gas temperature for samples weighing 2.0, 2.5, and 3.0 grams. The maximal slope of the particle temperature versus furnace temperature is used to determine the occurrence of ignition. Values of activation energy are analyzed by the Semenov model with the measured ignition temperature. Results show that the particle ignition temperature is 317, 324, and 330°C for rice straw, peanut shell, and rice hull, respectively. The results also indicate that the particle ignition temperature reduces as the volatile content increases and the sample amount decreases. The value of activation energy is 157.2, 170.3, and 192.8 kJ/mole for rice straw, peanut shell, and rice hull, respectively.","PeriodicalId":43808,"journal":{"name":"International Journal of Engineering and Technology Innovation","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44213442","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 : 2022-02-22DOI: 10.46604/ijeti.2022.7571
N. Bon, L. Dai
This study develops a hybrid method to identify, classify, and locate electrical faults on transmission lines based on Machine Learning (ML) methods. Firstly, Wavelet Transform (WT) technique is applied to extract features from the current or voltage signals. The extracted signals are decomposed into eleven coefficients. These coefficients are calculated to the energy level, and the data of teen fault types are converted to the RGB image. Secondly, GoogLeNet model is applied to classify the fault, and Convolutional Neural Network (CNN) method is proposed to locate the fault. The proposed method is tested on the four-bus power system with the 220 kV transmission line via time-domain simulation using Matlab software. The conditions of the fault resistance random values and the pre-fault load changes are considered. The simulation results show that the proposed method has high accuracy and fast processing time, and is a useful tool for analyzing the system stability in the field of electricity.
{"title":"Fault Identification, Classification, and Location on Transmission Lines Using Combined Machine Learning Methods","authors":"N. Bon, L. Dai","doi":"10.46604/ijeti.2022.7571","DOIUrl":"https://doi.org/10.46604/ijeti.2022.7571","url":null,"abstract":"This study develops a hybrid method to identify, classify, and locate electrical faults on transmission lines based on Machine Learning (ML) methods. Firstly, Wavelet Transform (WT) technique is applied to extract features from the current or voltage signals. The extracted signals are decomposed into eleven coefficients. These coefficients are calculated to the energy level, and the data of teen fault types are converted to the RGB image. Secondly, GoogLeNet model is applied to classify the fault, and Convolutional Neural Network (CNN) method is proposed to locate the fault. The proposed method is tested on the four-bus power system with the 220 kV transmission line via time-domain simulation using Matlab software. The conditions of the fault resistance random values and the pre-fault load changes are considered. The simulation results show that the proposed method has high accuracy and fast processing time, and is a useful tool for analyzing the system stability in the field of electricity.","PeriodicalId":43808,"journal":{"name":"International Journal of Engineering and Technology Innovation","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43542002","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 : 2022-02-22DOI: 10.46604/ijeti.2022.8599
A. Chatamoni, R. Bhukya
The security and energy efficiency of resource-constrained distributed sensors are the major concerns in the Internet of Things (IoT) network. A novel lightweight compressive sensing (CS) method is proposed in this study for simultaneous compression and encryption of sensor data in IoT scenarios. The proposed method reduces the storage space and transmission cost and increases the IoT security, with joint compression and encryption of data by image sensors. In this proposed method, the cryptographic advantage of CS with a structurally random matrix (SRM) is considered. Block compressive sensing (BCS) with an SRM-based measurement matrix is performed to generate the compressed and primary encrypted data. To enhance security, a stream cipher-based pseudo-error vector is added to corrupt the compressed data, preventing the leakage of statistical information. The experimental results and comparative analyses show that the proposed scheme outperforms the conventional and state-of-art schemes in terms of reconstruction performance and encryption efficiency.
{"title":"Lightweight Compressive Sensing for Joint Compression and Encryption of Sensor Data","authors":"A. Chatamoni, R. Bhukya","doi":"10.46604/ijeti.2022.8599","DOIUrl":"https://doi.org/10.46604/ijeti.2022.8599","url":null,"abstract":"The security and energy efficiency of resource-constrained distributed sensors are the major concerns in the Internet of Things (IoT) network. A novel lightweight compressive sensing (CS) method is proposed in this study for simultaneous compression and encryption of sensor data in IoT scenarios. The proposed method reduces the storage space and transmission cost and increases the IoT security, with joint compression and encryption of data by image sensors. In this proposed method, the cryptographic advantage of CS with a structurally random matrix (SRM) is considered. Block compressive sensing (BCS) with an SRM-based measurement matrix is performed to generate the compressed and primary encrypted data. To enhance security, a stream cipher-based pseudo-error vector is added to corrupt the compressed data, preventing the leakage of statistical information. The experimental results and comparative analyses show that the proposed scheme outperforms the conventional and state-of-art schemes in terms of reconstruction performance and encryption efficiency.","PeriodicalId":43808,"journal":{"name":"International Journal of Engineering and Technology Innovation","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46542909","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 : 2022-02-22DOI: 10.46604/ijeti.2022.8912
Vasudevan Yathushan, U. Puswewala
This study aims to investigate the geotechnical characteristics of three soils by adding waste plastics and a mixture of leaf ashes. The soil stabilizers used in the study are the plastics strips from waste plastic file folders and a mixture of ashes from five naturally occurring pozzolanic leaves in Sri Lanka. The plastics used in this study have a width of 5 mm and aspect ratios of 1, 2, 3, and 4 in the weight percentages 0.5, 1, 2, 4, and 8. The mixture of leaf ashes used is in the weight percentages 2, 4, 6, 8, and 10. The investigated geotechnical characteristics of the soils include the improvement of maximum dry density (MDD), optimum moisture content (OMC), soaked California bearing ratio (CBR), shear strength parameters, plastic index (PI), and Atterberg limits. The results suggest that the optimum improvement in soaked CBR and MDD can be achieved by adding 2% plastics and 6% leaf ash mixture into the soils. Shear strength parameters and PI can also be improved.
{"title":"Effectiveness of Pozzolanic Leaf Ashes and Plastics on Geotechnical Characteristics","authors":"Vasudevan Yathushan, U. Puswewala","doi":"10.46604/ijeti.2022.8912","DOIUrl":"https://doi.org/10.46604/ijeti.2022.8912","url":null,"abstract":"This study aims to investigate the geotechnical characteristics of three soils by adding waste plastics and a mixture of leaf ashes. The soil stabilizers used in the study are the plastics strips from waste plastic file folders and a mixture of ashes from five naturally occurring pozzolanic leaves in Sri Lanka. The plastics used in this study have a width of 5 mm and aspect ratios of 1, 2, 3, and 4 in the weight percentages 0.5, 1, 2, 4, and 8. The mixture of leaf ashes used is in the weight percentages 2, 4, 6, 8, and 10. The investigated geotechnical characteristics of the soils include the improvement of maximum dry density (MDD), optimum moisture content (OMC), soaked California bearing ratio (CBR), shear strength parameters, plastic index (PI), and Atterberg limits. The results suggest that the optimum improvement in soaked CBR and MDD can be achieved by adding 2% plastics and 6% leaf ash mixture into the soils. Shear strength parameters and PI can also be improved.","PeriodicalId":43808,"journal":{"name":"International Journal of Engineering and Technology Innovation","volume":"55 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70564981","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 : 2022-02-22DOI: 10.46604/ijeti.2022.8766
A. Lukjan, Arsit Iyaruk, Chumroon Somboon
This research investigates the effect of corrosion solutions on the mechanical properties of asphalt concrete mixtures. A control asphalt mixture (CM) and five polymer-modified (PM) or filler-modified (FM) mixtures containing waste materials are prepared, namely PM high-density polyethylene plastic (PM-PL), PM crumb rubber (PM-CR), FM Para wood ash (FM-PA), FM palm empty fruit bunch ash (FM-EA), and FM rice husk ash (FM-RA). The experiment is conducted by immersing the mixture specimens in four types of water solutions (i.e., distilled water, alkaline solution, sulfate solution, and acid solution), followed by the splitting tests. Finally, the corrosion resistance factor (fc) is computed to assess the corrosive effect of the corrosion solutions. The results show that the degree of reduction in tensile strength mainly depends on the type of corrosion solutions, type of mixtures, and immersion time. FM-EA provides better resistance under the alkaline and acid solutions, while PM-PL exhibits the greatest fc under the sulfate solution. Among all the mixtures, PM-PL shows the greatest ability in withstanding the corrosion solutions.
{"title":"Evaluation on Mechanical Deterioration of the Asphalt Mixtures Containing Waste Materials When Exposed to Corrosion Solutions","authors":"A. Lukjan, Arsit Iyaruk, Chumroon Somboon","doi":"10.46604/ijeti.2022.8766","DOIUrl":"https://doi.org/10.46604/ijeti.2022.8766","url":null,"abstract":"This research investigates the effect of corrosion solutions on the mechanical properties of asphalt concrete mixtures. A control asphalt mixture (CM) and five polymer-modified (PM) or filler-modified (FM) mixtures containing waste materials are prepared, namely PM high-density polyethylene plastic (PM-PL), PM crumb rubber (PM-CR), FM Para wood ash (FM-PA), FM palm empty fruit bunch ash (FM-EA), and FM rice husk ash (FM-RA). The experiment is conducted by immersing the mixture specimens in four types of water solutions (i.e., distilled water, alkaline solution, sulfate solution, and acid solution), followed by the splitting tests. Finally, the corrosion resistance factor (fc) is computed to assess the corrosive effect of the corrosion solutions. The results show that the degree of reduction in tensile strength mainly depends on the type of corrosion solutions, type of mixtures, and immersion time. FM-EA provides better resistance under the alkaline and acid solutions, while PM-PL exhibits the greatest fc under the sulfate solution. Among all the mixtures, PM-PL shows the greatest ability in withstanding the corrosion solutions.","PeriodicalId":43808,"journal":{"name":"International Journal of Engineering and Technology Innovation","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46103284","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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