ºÝºÝߣshows by User: AJAYISAMUEL / http://www.slideshare.net/images/logo.gif ºÝºÝߣshows by User: AJAYISAMUEL / Sat, 07 Dec 2019 17:58:31 GMT ºÝºÝߣShare feed for ºÝºÝߣshows by User: AJAYISAMUEL Response Surface Analysis of the Compressive Strength of Self-Compacting Concrete Incorporating Metakaolin /slideshow/response-surface-analysis-of-the-compressive-strength-of-selfcompacting-concrete-incorporating-metakaolin/202834977 busari-191207175831
This research developed a mathematical model and optimization of materials for the development of metakaolin self-compacting concrete. This is in a bid to reduce the overall material quantity and cost towards sustainable infrastructural construction. To achieve the aim of this research, Response Surface Analysis (RSM) was used. Kaolinitic clay was De-hydroxylated at 750°C to form metakaolin. This was used as a partial replacement for cement at 0%, 5%, 10%, 15%, 20% and 25% weight of Portland limestone cement. Both strength and rheology properties of the developed metakaolin self-compacting concrete were assessed. To this end, slump flow, L-Box test and V-funnel test were carried out alongside the compressive strength using relevant standard. The result of the research revealed that at 15% addition of metakaolin the slump flow, passing ability and filling ability was unsatisfactory according to EFNARC standard. From the numerical optimization of the compressive strength, the maximum predicted compressive strength of 44.35 N/mm2 was obtained. At a low value of metakaolin addition (5–15%), the compressive strength increased as the age of the concrete increased from 3–150 days. The age with the optimum mechanical strength formation was 110 days with metakaolin addition of 52.73 kg. The result of this research provide a database for Engineers, Researchers and Construction workers on the optimum metakaolin required to achieve satisfactory mechanical strength in metakaolin self-compacting concrete. ]]>
This research developed a mathematical model and optimization of materials for the development of metakaolin self-compacting concrete. This is in a bid to reduce the overall material quantity and cost towards sustainable infrastructural construction. To achieve the aim of this research, Response Surface Analysis (RSM) was used. Kaolinitic clay was De-hydroxylated at 750°C to form metakaolin. This was used as a partial replacement for cement at 0%, 5%, 10%, 15%, 20% and 25% weight of Portland limestone cement. Both strength and rheology properties of the developed metakaolin self-compacting concrete were assessed. To this end, slump flow, L-Box test and V-funnel test were carried out alongside the compressive strength using relevant standard. The result of the research revealed that at 15% addition of metakaolin the slump flow, passing ability and filling ability was unsatisfactory according to EFNARC standard. From the numerical optimization of the compressive strength, the maximum predicted compressive strength of 44.35 N/mm2 was obtained. At a low value of metakaolin addition (5–15%), the compressive strength increased as the age of the concrete increased from 3–150 days. The age with the optimum mechanical strength formation was 110 days with metakaolin addition of 52.73 kg. The result of this research provide a database for Engineers, Researchers and Construction workers on the optimum metakaolin required to achieve satisfactory mechanical strength in metakaolin self-compacting concrete. ]]> Sat, 07 Dec 2019 17:58:31 GMT /slideshow/response-surface-analysis-of-the-compressive-strength-of-selfcompacting-concrete-incorporating-metakaolin/202834977 AJAYISAMUEL@slideshare.net(AJAYISAMUEL) Response Surface Analysis of the Compressive Strength of Self-Compacting Concrete Incorporating Metakaolin AJAYISAMUEL This research developed a mathematical model and optimization of materials for the development of metakaolin self-compacting concrete. This is in a bid to reduce the overall material quantity and cost towards sustainable infrastructural construction. To achieve the aim of this research, Response Surface Analysis (RSM) was used. Kaolinitic clay was De-hydroxylated at 750°C to form metakaolin. This was used as a partial replacement for cement at 0%, 5%, 10%, 15%, 20% and 25% weight of Portland limestone cement. Both strength and rheology properties of the developed metakaolin self-compacting concrete were assessed. To this end, slump flow, L-Box test and V-funnel test were carried out alongside the compressive strength using relevant standard. The result of the research revealed that at 15% addition of metakaolin the slump flow, passing ability and filling ability was unsatisfactory according to EFNARC standard. From the numerical optimization of the compressive strength, the maximum predicted compressive strength of 44.35 N/mm2 was obtained. At a low value of metakaolin addition (5–15%), the compressive strength increased as the age of the concrete increased from 3–150 days. The age with the optimum mechanical strength formation was 110 days with metakaolin addition of 52.73 kg. The result of this research provide a database for Engineers, Researchers and Construction workers on the optimum metakaolin required to achieve satisfactory mechanical strength in metakaolin self-compacting concrete. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/busari-191207175831-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This research developed a mathematical model and optimization of materials for the development of metakaolin self-compacting concrete. This is in a bid to reduce the overall material quantity and cost towards sustainable infrastructural construction. To achieve the aim of this research, Response Surface Analysis (RSM) was used. Kaolinitic clay was De-hydroxylated at 750°C to form metakaolin. This was used as a partial replacement for cement at 0%, 5%, 10%, 15%, 20% and 25% weight of Portland limestone cement. Both strength and rheology properties of the developed metakaolin self-compacting concrete were assessed. To this end, slump flow, L-Box test and V-funnel test were carried out alongside the compressive strength using relevant standard. The result of the research revealed that at 15% addition of metakaolin the slump flow, passing ability and filling ability was unsatisfactory according to EFNARC standard. From the numerical optimization of the compressive strength, the maximum predicted compressive strength of 44.35 N/mm2 was obtained. At a low value of metakaolin addition (5–15%), the compressive strength increased as the age of the concrete increased from 3–150 days. The age with the optimum mechanical strength formation was 110 days with metakaolin addition of 52.73 kg. The result of this research provide a database for Engineers, Researchers and Construction workers on the optimum metakaolin required to achieve satisfactory mechanical strength in metakaolin self-compacting concrete.
Response Surface Analysis of the Compressive Strength of Self-Compacting Concrete Incorporating Metakaolin from AJAYI SAMUEL
]]> 37 1 https://cdn.slidesharecdn.com/ss_thumbnails/busari-191207175831-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Predictive modeling of travel time /slideshow/predictive-modeling-of-travel-time/202833140 predictivemodelingoftraveltime-191207175206
Large cities in developing countries are characterized by growth in automobile ownership, insufficient transportation infrastructure and service development. These cities often suffer from congestion, poor mobility and accessibility, significant economic waste, adverse environmental impact and safety problems. This paper focuses on identification of travel time characteristics and other traffic parameters and to develop a predictive model for travel time on Akure major roads. Data on travel time were collected for vehicles during the morning and evening peak periods using floating car technique. The data was analyzed using Statistical Packages for Social Sciences (SPSS) and fitted into Multiple Regression model to establish a relationship between the Travel Time and other road traffic parameters. Travel time (Tt) was modeled as a function of section length (X1), number of intersections (X2), pedestrian/ economic activities (X3), Traffic volume (X4), enforcement agency (X5) and road width (X6). The Coefficient of multiple determination R2 was 0.702 which means that there is 70.2% of the dependent variable (travel time) in the forward direction as explained (accounted) by the independent variables and 72.2% in the opposite direction. The result revealed that section length, pedestrian economic activity and traffic volume were all significant at 5% level and has a positive relationship with travel time in both forward and reverse direction. The model identifies the impact of these traffic parameters on travel time and recommend measures for improvement. ]]>
Large cities in developing countries are characterized by growth in automobile ownership, insufficient transportation infrastructure and service development. These cities often suffer from congestion, poor mobility and accessibility, significant economic waste, adverse environmental impact and safety problems. This paper focuses on identification of travel time characteristics and other traffic parameters and to develop a predictive model for travel time on Akure major roads. Data on travel time were collected for vehicles during the morning and evening peak periods using floating car technique. The data was analyzed using Statistical Packages for Social Sciences (SPSS) and fitted into Multiple Regression model to establish a relationship between the Travel Time and other road traffic parameters. Travel time (Tt) was modeled as a function of section length (X1), number of intersections (X2), pedestrian/ economic activities (X3), Traffic volume (X4), enforcement agency (X5) and road width (X6). The Coefficient of multiple determination R2 was 0.702 which means that there is 70.2% of the dependent variable (travel time) in the forward direction as explained (accounted) by the independent variables and 72.2% in the opposite direction. The result revealed that section length, pedestrian economic activity and traffic volume were all significant at 5% level and has a positive relationship with travel time in both forward and reverse direction. The model identifies the impact of these traffic parameters on travel time and recommend measures for improvement. ]]> Sat, 07 Dec 2019 17:52:06 GMT /slideshow/predictive-modeling-of-travel-time/202833140 AJAYISAMUEL@slideshare.net(AJAYISAMUEL) Predictive modeling of travel time AJAYISAMUEL Large cities in developing countries are characterized by growth in automobile ownership, insufficient transportation infrastructure and service development. These cities often suffer from congestion, poor mobility and accessibility, significant economic waste, adverse environmental impact and safety problems. This paper focuses on identification of travel time characteristics and other traffic parameters and to develop a predictive model for travel time on Akure major roads. Data on travel time were collected for vehicles during the morning and evening peak periods using floating car technique. The data was analyzed using Statistical Packages for Social Sciences (SPSS) and fitted into Multiple Regression model to establish a relationship between the Travel Time and other road traffic parameters. Travel time (Tt) was modeled as a function of section length (X1), number of intersections (X2), pedestrian/ economic activities (X3), Traffic volume (X4), enforcement agency (X5) and road width (X6). The Coefficient of multiple determination R2 was 0.702 which means that there is 70.2% of the dependent variable (travel time) in the forward direction as explained (accounted) by the independent variables and 72.2% in the opposite direction. The result revealed that section length, pedestrian economic activity and traffic volume were all significant at 5% level and has a positive relationship with travel time in both forward and reverse direction. The model identifies the impact of these traffic parameters on travel time and recommend measures for improvement. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/predictivemodelingoftraveltime-191207175206-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Large cities in developing countries are characterized by growth in automobile ownership, insufficient transportation infrastructure and service development. These cities often suffer from congestion, poor mobility and accessibility, significant economic waste, adverse environmental impact and safety problems. This paper focuses on identification of travel time characteristics and other traffic parameters and to develop a predictive model for travel time on Akure major roads. Data on travel time were collected for vehicles during the morning and evening peak periods using floating car technique. The data was analyzed using Statistical Packages for Social Sciences (SPSS) and fitted into Multiple Regression model to establish a relationship between the Travel Time and other road traffic parameters. Travel time (Tt) was modeled as a function of section length (X1), number of intersections (X2), pedestrian/ economic activities (X3), Traffic volume (X4), enforcement agency (X5) and road width (X6). The Coefficient of multiple determination R2 was 0.702 which means that there is 70.2% of the dependent variable (travel time) in the forward direction as explained (accounted) by the independent variables and 72.2% in the opposite direction. The result revealed that section length, pedestrian economic activity and traffic volume were all significant at 5% level and has a positive relationship with travel time in both forward and reverse direction. The model identifies the impact of these traffic parameters on travel time and recommend measures for improvement.
Predictive modeling of travel time from AJAYI SAMUEL
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]]> Wed, 07 Dec 2016 13:29:20 GMT /slideshow/paper-170/69914388 AJAYISAMUEL@slideshare.net(AJAYISAMUEL) paper 170 AJAYISAMUEL <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/439866d5-8478-4ce7-8932-1cba2c6212d4-161207132920-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br>
paper 170 from AJAYI SAMUEL
]]> 127 3 https://cdn.slidesharecdn.com/ss_thumbnails/439866d5-8478-4ce7-8932-1cba2c6212d4-161207132920-thumbnail.jpg?width=120&height=120&fit=bounds document 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-AJAYISAMUEL-48x48.jpg?cb=1575741126 https://cdn.slidesharecdn.com/ss_thumbnails/busari-191207175831-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/response-surface-analysis-of-the-compressive-strength-of-selfcompacting-concrete-incorporating-metakaolin/202834977 Response Surface Analy... https://cdn.slidesharecdn.com/ss_thumbnails/predictivemodelingoftraveltime-191207175206-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/predictive-modeling-of-travel-time/202833140 Predictive modeling of... https://cdn.slidesharecdn.com/ss_thumbnails/439866d5-8478-4ce7-8932-1cba2c6212d4-161207132920-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/paper-170/69914388 paper 170