Soil liquefaction is a phenomenon that occurs in which the behavior of soils changes from solid to viscous liquid due to the effect of earthquake intensity or other sudden loadings. The earthquake results in excess pore water pressure, which leads to saturated loose soil with weaker characteristics and potentially causes large ground deformation and lateral spreading. Soil liquefaction is a dangerous event that can lead to catastrophic outcomes for humans and infrastructures, especially in countries prone to earthquake shaking, where soil liquefaction is considered one of the most prevalent types of ground failure. Hence, precautions to reduce and/or prevent soil liquefaction are essential and required. One of the countermeasures to avoid soil liquefaction is the introduction of fibers in the soil since fibers can act as reinforcement by enhancing the soil’s strength and resistance to liquefaction. The process of including fibers into the soil is known as soil stabilization and is considered one of the ground improvement techniques. Therefore, this paper aims to summarize and review the consequences of adding fiber as a reinforcement technique to overcome the issue of soil liquefaction.
{"title":"State-of-the-Art Review: Fiber-Reinforced Soil as a Proactive Approach for Liquefaction Mitigation and Risk Management","authors":"Hasan Alqawasmeh, Yazan Alzubi, Ali Mahamied","doi":"10.1155/2023/8737304","DOIUrl":"https://doi.org/10.1155/2023/8737304","url":null,"abstract":"Soil liquefaction is a phenomenon that occurs in which the behavior of soils changes from solid to viscous liquid due to the effect of earthquake intensity or other sudden loadings. The earthquake results in excess pore water pressure, which leads to saturated loose soil with weaker characteristics and potentially causes large ground deformation and lateral spreading. Soil liquefaction is a dangerous event that can lead to catastrophic outcomes for humans and infrastructures, especially in countries prone to earthquake shaking, where soil liquefaction is considered one of the most prevalent types of ground failure. Hence, precautions to reduce and/or prevent soil liquefaction are essential and required. One of the countermeasures to avoid soil liquefaction is the introduction of fibers in the soil since fibers can act as reinforcement by enhancing the soil’s strength and resistance to liquefaction. The process of including fibers into the soil is known as soil stabilization and is considered one of the ground improvement techniques. Therefore, this paper aims to summarize and review the consequences of adding fiber as a reinforcement technique to overcome the issue of soil liquefaction.","PeriodicalId":15716,"journal":{"name":"Journal of Engineering","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134885943","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}
This paper considers the current market pace, which requires a corresponding competitive advantage. This study forecasted the cost of heavy machinery depending on geolocation and essential characteristics by the field of activity. This study analyzes specific categories of heavy machinery for important price characteristics. The study classified them by keywords in the text description as essential characteristics. Accordingly, a dataset was formed based on the data obtained. The research objective is to collect and structure data from web resources for the sale of heavy equipment. This paper describes in detail the preliminary data processing. The main stages of preprocessing are presented in detail: detection and processing of missing data, removing anomalous data, coding of categorical data, and scaling. The method of the average value of a specific grouped set was applied to fill in the gaps according to the characteristics and available data. The mode value from the grouped items was used to fill in the gaps. The interquartile range and standard deviation were used to detect anomalies. We used the Kolmogorov–Smirnov, KS_Test, and Lilliefors tests to check the data for normality. In this study, the assessment of abnormal data was applied separately to each set of grouped data with the same parameters. The study built and analyzed models using machine learning methods (linear and polynomial regression, decision trees, random forest, support vector machine, and neural network). Two data encoding methods were used to achieve maximum model accuracy: Label Encoder and One Hot Encoder. The work of each algorithm is considered on the example of the created dataset. In this study, the parameter used for coding was the geolocation of heavy equipment. The study pays additional attention to the specific characteristics of heavy machinery by the sector of the economy. The existing methods and tools for price forecasting, depending on the specific characteristics of the equipment, were analyzed. The practical significance of this work lies in developing an algorithm for predicting the cost of heavy machinery by assessing several parameters.
{"title":"Methodology for Estimating the Cost of Construction Equipment Based on the Analysis of Important Characteristics Using Machine Learning Methods","authors":"Nataliya Boyko, Oleksii Lukash","doi":"10.1155/2023/8833753","DOIUrl":"https://doi.org/10.1155/2023/8833753","url":null,"abstract":"This paper considers the current market pace, which requires a corresponding competitive advantage. This study forecasted the cost of heavy machinery depending on geolocation and essential characteristics by the field of activity. This study analyzes specific categories of heavy machinery for important price characteristics. The study classified them by keywords in the text description as essential characteristics. Accordingly, a dataset was formed based on the data obtained. The research objective is to collect and structure data from web resources for the sale of heavy equipment. This paper describes in detail the preliminary data processing. The main stages of preprocessing are presented in detail: detection and processing of missing data, removing anomalous data, coding of categorical data, and scaling. The method of the average value of a specific grouped set was applied to fill in the gaps according to the characteristics and available data. The mode value from the grouped items was used to fill in the gaps. The interquartile range and standard deviation were used to detect anomalies. We used the Kolmogorov–Smirnov, KS_Test, and Lilliefors tests to check the data for normality. In this study, the assessment of abnormal data was applied separately to each set of grouped data with the same parameters. The study built and analyzed models using machine learning methods (linear and polynomial regression, decision trees, random forest, support vector machine, and neural network). Two data encoding methods were used to achieve maximum model accuracy: Label Encoder and One Hot Encoder. The work of each algorithm is considered on the example of the created dataset. In this study, the parameter used for coding was the geolocation of heavy equipment. The study pays additional attention to the specific characteristics of heavy machinery by the sector of the economy. The existing methods and tools for price forecasting, depending on the specific characteristics of the equipment, were analyzed. The practical significance of this work lies in developing an algorithm for predicting the cost of heavy machinery by assessing several parameters.","PeriodicalId":15716,"journal":{"name":"Journal of Engineering","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136015482","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 : 2023-07-10DOI: 10.31026/j.eng.2013.12.06
A. H. Sulaymon, Muna Y. Abdul-ahad, Roaa A. Mahmood
During the last decade, there has been a concern about the relation between aluminum residuals in treated water and Alzheimer disease, and more interest has been considered on the development of natural coagulants. The present study aimed to investigate the efficiency of alum as a primary coagulant in conjunction with mallow, Arabic gum and okra as coagulant aids for the treatment of water samples containing synthetic turbidity of kaolin. Jar test experiments were carried out for initial raw water turbidities 100, 200 and 500 (NTU). The optimum doses of alum, mallow, Arabic gum and okra were 20, 2, 1 and 1 mg/L for100 NTU turbidity level, 35, 4, 2 and 3 mg/L , for 200NTU turbidity level and 50, 8, 10 and 8 mg/L for 500 NTU turbidity level, respectively. The optimum pH was 7 for alum, and 7.5 for mallow, Arabic gum and okra. The residual turbidity was 3.34 to 6.81 NTU by using alum as a primary coagulant with mallow, Arabic gum and okra, and pH values of the treated water by the natural coagulants were 6.1 to 7.01. The optimum dose of thenatural coagulants in the present study has higher efficiency in removing high turbidity in comparison with low turbidity.Natural coagulant showed many advantages in coagulation/flocculation process. By using natural coagulants, considerable decreasing in Al2(SO4)3 consumption, and Increasing in the rate of sedimentation can be achieved.
{"title":"Removal of Water Turbidity by Different Coagulants","authors":"A. H. Sulaymon, Muna Y. Abdul-ahad, Roaa A. Mahmood","doi":"10.31026/j.eng.2013.12.06","DOIUrl":"https://doi.org/10.31026/j.eng.2013.12.06","url":null,"abstract":"During the last decade, there has been a concern about the relation between aluminum residuals in treated water and Alzheimer disease, and more interest has been considered on the development of natural coagulants. The present study aimed to investigate the efficiency of alum as a primary coagulant in conjunction with mallow, Arabic gum and okra as coagulant aids for the treatment of water samples containing synthetic turbidity of kaolin. Jar test experiments were carried out for initial raw water turbidities 100, 200 and 500 (NTU). The optimum doses of alum, mallow, Arabic gum and okra were 20, 2, 1 and 1 mg/L for100 NTU turbidity level, 35, 4, 2 and 3 mg/L , for 200NTU turbidity level and 50, 8, 10 and 8 mg/L for 500 NTU turbidity level, respectively. The optimum pH was 7 for alum, and 7.5 for mallow, Arabic gum and okra. The residual turbidity was 3.34 to 6.81 NTU by using alum as a primary coagulant with mallow, Arabic gum and okra, and pH values of the treated water by the natural coagulants were 6.1 to 7.01. The optimum dose of thenatural coagulants in the present study has higher efficiency in removing high turbidity in comparison with low turbidity.Natural coagulant showed many advantages in coagulation/flocculation process. By using natural coagulants, considerable decreasing in Al2(SO4)3 consumption, and Increasing in the rate of sedimentation can be achieved.","PeriodicalId":15716,"journal":{"name":"Journal of Engineering","volume":"43 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139360659","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}
Rose Alegewe, Abdul-Ganiyu Shaibu, Yakubu Saaka Zakaria
This study aimed to determine the optimum levels of irrigation regime and irrigation schedule based on crop water productivity for the sustainable production of green pepper in a water-limiting tropical savannah agroecological zone. The study was conducted at the Hydro Farm of MotorKing Company Limited in the Tamale Metropolis, Northern Region, Ghana. The experimental design was a 2 × 3 factorial experiment laid out in a randomized complete block and replicated five times. Irrigation schedule at two levels (one-time daily application and split daily application at 60% morning and 40% evening) and irrigation regime at three levels (100% ETc, 80% ETc, and 60% ETc) were the factors. The “Yolo Wonder” variety of green pepper was the test crop. The crop was planted at a planting distance of 0.3 m within rows and 0.5 m between rows. Treatments were applied using a drip irrigation system. Crop water requirements (ETc) of green pepper were estimated using the CROPWAT model. Crop yield and water applied under each treatment were determined. Crop yield was measured at harvest as the total weight of fruits per hectare. Crop water productivity was determined under each treatment as crop yield per unit of water consumed. Data analysis was done in Genstat (12th edition). Analysis of variance (ANOVA) and Duncan’s multiple range test at 5% level of significance were employed to separate differences in treatment means. The results suggest that both irrigation regime and irrigation schedule have significant influence on the yield and crop water productivity of green pepper. Irrigating at 60% ETc and split irrigation (60% morning and 40% evening) gave significantly higher yields and crop water productivity compared to the other levels of the factors. This study demonstrated that irrigation schedule and irrigation regime are important factors to consider in the optimization of water management for green pepper; however, further research is needed to identify the optimal levels of these factors and the most effective irrigation strategies for the crop in different environments.
{"title":"Optimization of Water Management for Green Pepper Production in a Water-Limiting Tropical Savanna Agroecological Zone Based on Crop Water Productivity","authors":"Rose Alegewe, Abdul-Ganiyu Shaibu, Yakubu Saaka Zakaria","doi":"10.1155/2023/9970714","DOIUrl":"https://doi.org/10.1155/2023/9970714","url":null,"abstract":"This study aimed to determine the optimum levels of irrigation regime and irrigation schedule based on crop water productivity for the sustainable production of green pepper in a water-limiting tropical savannah agroecological zone. The study was conducted at the Hydro Farm of MotorKing Company Limited in the Tamale Metropolis, Northern Region, Ghana. The experimental design was a 2 × 3 factorial experiment laid out in a randomized complete block and replicated five times. Irrigation schedule at two levels (one-time daily application and split daily application at 60% morning and 40% evening) and irrigation regime at three levels (100% ETc, 80% ETc, and 60% ETc) were the factors. The “Yolo Wonder” variety of green pepper was the test crop. The crop was planted at a planting distance of 0.3 m within rows and 0.5 m between rows. Treatments were applied using a drip irrigation system. Crop water requirements (ETc) of green pepper were estimated using the CROPWAT model. Crop yield and water applied under each treatment were determined. Crop yield was measured at harvest as the total weight of fruits per hectare. Crop water productivity was determined under each treatment as crop yield per unit of water consumed. Data analysis was done in Genstat (12th edition). Analysis of variance (ANOVA) and Duncan’s multiple range test at 5% level of significance were employed to separate differences in treatment means. The results suggest that both irrigation regime and irrigation schedule have significant influence on the yield and crop water productivity of green pepper. Irrigating at 60% ETc and split irrigation (60% morning and 40% evening) gave significantly higher yields and crop water productivity compared to the other levels of the factors. This study demonstrated that irrigation schedule and irrigation regime are important factors to consider in the optimization of water management for green pepper; however, further research is needed to identify the optimal levels of these factors and the most effective irrigation strategies for the crop in different environments.","PeriodicalId":15716,"journal":{"name":"Journal of Engineering","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135832565","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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