際際滷shows by User: paveman / http://www.slideshare.net/images/logo.gif 際際滷shows by User: paveman / Mon, 02 Mar 2020 22:08:52 GMT 際際滷Share feed for 際際滷shows by User: paveman Time series forecasting with machine learning /slideshow/time-series-forecasting-with-machine-learning/229544702 timeseriesforecastingwithmachinelearning-200302220852
An introduction of developing and application time series forecast models with both traditional time series methods and machine learning techniques. Case study for a challenging very short-term electrical price forecasting project was presented.]]>
An introduction of developing and application time series forecast models with both traditional time series methods and machine learning techniques. Case study for a challenging very short-term electrical price forecasting project was presented.]]> Mon, 02 Mar 2020 22:08:52 GMT /slideshow/time-series-forecasting-with-machine-learning/229544702 paveman@slideshare.net(paveman) Time series forecasting with machine learning paveman An introduction of developing and application time series forecast models with both traditional time series methods and machine learning techniques. Case study for a challenging very short-term electrical price forecasting project was presented. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/timeseriesforecastingwithmachinelearning-200302220852-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> An introduction of developing and application time series forecast models with both traditional time series methods and machine learning techniques. Case study for a challenging very short-term electrical price forecasting project was presented.
Time series forecasting with machine learning from Dr Wei Liu
]]> 1663 1 https://cdn.slidesharecdn.com/ss_thumbnails/timeseriesforecastingwithmachinelearning-200302220852-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Application of multi criteria optimisation and trade-off analysis (dr wei liu) /slideshow/application-of-multi-criteria-optimisation-and-tradeoff-analysis-dr-wei-liu/73921813 applicationofmulti-criteriaoptimisationandtrade-offanalysisdrweiliu-170330023136
Road asset management is, at its core, a process of resource (e.g., budget, manpower, and facility) allocation and utilization. In order for efficient and cost-effective preservation and operation of assets both holistically and individually, it is necessary to incorporate relevant analytical tools for rational and integrated asset management decision-making. Accordingly, specialized tools for resource allocations at various working levels that characterize different features are required. The use of optimization approaches for managing infrastructure assets has received increasing attention in the last few decades due to tighter budgets, increasing demands, and stricter accountability in transportation investments and policies decisions. Traditional single-criteria optimization approaches have been implemented in many infrastructure asset management systems such as IBC optimisation in dTIMS. Single-criteria optimization identifies the best feasible solution in terms of a single measure of value. However, it is often not a holistic approach to make a decision based on one evaluation criterion only. This is especially true in an infrastructure management decision-making context. In this paper, a multi-criteria cross-asset optimisation and trade-off analysis framework is introduced and the prototype tool developed for the framework provides an implementable method that can be used to directly link planning, resource allocation, and programming to achieve agencys multiple performance goals. A case study was carried out to demonstrate how the framework and tool together with dTIMS modelling analysis can be used to develop optimised long-term forward work programmes for different objective functions as well as simultaneously satisfying various budgets and ONRC-oriented performance constraints. ]]>
Road asset management is, at its core, a process of resource (e.g., budget, manpower, and facility) allocation and utilization. In order for efficient and cost-effective preservation and operation of assets both holistically and individually, it is necessary to incorporate relevant analytical tools for rational and integrated asset management decision-making. Accordingly, specialized tools for resource allocations at various working levels that characterize different features are required. The use of optimization approaches for managing infrastructure assets has received increasing attention in the last few decades due to tighter budgets, increasing demands, and stricter accountability in transportation investments and policies decisions. Traditional single-criteria optimization approaches have been implemented in many infrastructure asset management systems such as IBC optimisation in dTIMS. Single-criteria optimization identifies the best feasible solution in terms of a single measure of value. However, it is often not a holistic approach to make a decision based on one evaluation criterion only. This is especially true in an infrastructure management decision-making context. In this paper, a multi-criteria cross-asset optimisation and trade-off analysis framework is introduced and the prototype tool developed for the framework provides an implementable method that can be used to directly link planning, resource allocation, and programming to achieve agencys multiple performance goals. A case study was carried out to demonstrate how the framework and tool together with dTIMS modelling analysis can be used to develop optimised long-term forward work programmes for different objective functions as well as simultaneously satisfying various budgets and ONRC-oriented performance constraints. ]]> Thu, 30 Mar 2017 02:31:36 GMT /slideshow/application-of-multi-criteria-optimisation-and-tradeoff-analysis-dr-wei-liu/73921813 paveman@slideshare.net(paveman) Application of multi criteria optimisation and trade-off analysis (dr wei liu) paveman Road asset management is, at its core, a process of resource (e.g., budget, manpower, and facility) allocation and utilization. In order for efficient and cost-effective preservation and operation of assets both holistically and individually, it is necessary to incorporate relevant analytical tools for rational and integrated asset management decision-making. Accordingly, specialized tools for resource allocations at various working levels that characterize different features are required. The use of optimization approaches for managing infrastructure assets has received increasing attention in the last few decades due to tighter budgets, increasing demands, and stricter accountability in transportation investments and policies decisions. Traditional single-criteria optimization approaches have been implemented in many infrastructure asset management systems such as IBC optimisation in dTIMS. Single-criteria optimization identifies the best feasible solution in terms of a single measure of value. However, it is often not a holistic approach to make a decision based on one evaluation criterion only. This is especially true in an infrastructure management decision-making context. In this paper, a multi-criteria cross-asset optimisation and trade-off analysis framework is introduced and the prototype tool developed for the framework provides an implementable method that can be used to directly link planning, resource allocation, and programming to achieve agencys multiple performance goals. A case study was carried out to demonstrate how the framework and tool together with dTIMS modelling analysis can be used to develop optimised long-term forward work programmes for different objective functions as well as simultaneously satisfying various budgets and ONRC-oriented performance constraints. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/applicationofmulti-criteriaoptimisationandtrade-offanalysisdrweiliu-170330023136-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Road asset management is, at its core, a process of resource (e.g., budget, manpower, and facility) allocation and utilization. In order for efficient and cost-effective preservation and operation of assets both holistically and individually, it is necessary to incorporate relevant analytical tools for rational and integrated asset management decision-making. Accordingly, specialized tools for resource allocations at various working levels that characterize different features are required. The use of optimization approaches for managing infrastructure assets has received increasing attention in the last few decades due to tighter budgets, increasing demands, and stricter accountability in transportation investments and policies decisions. Traditional single-criteria optimization approaches have been implemented in many infrastructure asset management systems such as IBC optimisation in dTIMS. Single-criteria optimization identifies the best feasible solution in terms of a single measure of value. However, it is often not a holistic approach to make a decision based on one evaluation criterion only. This is especially true in an infrastructure management decision-making context. In this paper, a multi-criteria cross-asset optimisation and trade-off analysis framework is introduced and the prototype tool developed for the framework provides an implementable method that can be used to directly link planning, resource allocation, and programming to achieve agencys multiple performance goals. A case study was carried out to demonstrate how the framework and tool together with dTIMS modelling analysis can be used to develop optimised long-term forward work programmes for different objective functions as well as simultaneously satisfying various budgets and ONRC-oriented performance constraints.
Application of multi criteria optimisation and trade-off analysis (dr wei liu) from Dr Wei Liu
]]> 327 3 https://cdn.slidesharecdn.com/ss_thumbnails/applicationofmulti-criteriaoptimisationandtrade-offanalysisdrweiliu-170330023136-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Optimisation and Tradeoff Analysis Tool for Asset Management Planning and Programming /slideshow/optimisation-and-tradeoff-analysis-tool-for-asset-management-planning-and-programming/69375890 nams2016ghddrweiliu-optimisationandtradeoffanalysisforam-161121220629
This presentation introduces key concepts around cross-asset optimization and trade-off analysis, the framework and tool developed by the author for applying cross-asset optimization and trade-off analysis in infrastructure asset management planning and programming, and discuss various ways and potential benefits of implementing optimization and trade-off analysis in asset management decision making]]>
This presentation introduces key concepts around cross-asset optimization and trade-off analysis, the framework and tool developed by the author for applying cross-asset optimization and trade-off analysis in infrastructure asset management planning and programming, and discuss various ways and potential benefits of implementing optimization and trade-off analysis in asset management decision making]]> Mon, 21 Nov 2016 22:06:29 GMT /slideshow/optimisation-and-tradeoff-analysis-tool-for-asset-management-planning-and-programming/69375890 paveman@slideshare.net(paveman) Optimisation and Tradeoff Analysis Tool for Asset Management Planning and Programming paveman This presentation introduces key concepts around cross-asset optimization and trade-off analysis, the framework and tool developed by the author for applying cross-asset optimization and trade-off analysis in infrastructure asset management planning and programming, and discuss various ways and potential benefits of implementing optimization and trade-off analysis in asset management decision making <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/nams2016ghddrweiliu-optimisationandtradeoffanalysisforam-161121220629-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This presentation introduces key concepts around cross-asset optimization and trade-off analysis, the framework and tool developed by the author for applying cross-asset optimization and trade-off analysis in infrastructure asset management planning and programming, and discuss various ways and potential benefits of implementing optimization and trade-off analysis in asset management decision making
Optimisation and Tradeoff Analysis Tool for Asset Management Planning and Programming from Dr Wei Liu
]]> 620 5 https://cdn.slidesharecdn.com/ss_thumbnails/nams2016ghddrweiliu-optimisationandtradeoffanalysisforam-161121220629-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 ENHANCING ROAD SAFETY MANAGEMENT WITH GIS MAPPING AND GEOSPATIAL DATABASE /slideshow/enhancing-road-safety-management-with-gis-mapping-and-geospatial-database/23812530 weiliupaper210saturday15june1410-130702164515-phpapp02
Reliable and accurate data are needed in each stage of road safety management in order to correctly identify problems and risk factors and priority treatments, and to formulate strategy, set targets and monitor performance. Ongoing, data-led diagnosis and management of the leading road safety problems enables appropriate action and resource allocation. Data relevant to road safety are collected every day, but for these data to be useful for informing road safety practice, they must be properly coded and visualized, processed and analysed in a systematic way. With the road system management strategy switching from 3Es to Safe System, there is additional challenges and demand for the integrated use of various types of road safety data. In this paper, the development of an integrated road safety data management system is introduced. The road safety data management system consists of a geospatial database for storing, querying and analysing road safety related data including crash records, road safety deficiency records, carriageway, surfacing, geometry, skid resistance, signs and road marking etc, and a GIS mapping platform for visualizing and integrating different data sets. Data from a State Highway network is used to demonstrate the potential application areas of the information system developed, such as identification of crash hot-spots, development of safety intervention and safety management strategies, planning of minor safety improvement programme, and investigation of severe and fatal crashes.]]>
Reliable and accurate data are needed in each stage of road safety management in order to correctly identify problems and risk factors and priority treatments, and to formulate strategy, set targets and monitor performance. Ongoing, data-led diagnosis and management of the leading road safety problems enables appropriate action and resource allocation. Data relevant to road safety are collected every day, but for these data to be useful for informing road safety practice, they must be properly coded and visualized, processed and analysed in a systematic way. With the road system management strategy switching from 3Es to Safe System, there is additional challenges and demand for the integrated use of various types of road safety data. In this paper, the development of an integrated road safety data management system is introduced. The road safety data management system consists of a geospatial database for storing, querying and analysing road safety related data including crash records, road safety deficiency records, carriageway, surfacing, geometry, skid resistance, signs and road marking etc, and a GIS mapping platform for visualizing and integrating different data sets. Data from a State Highway network is used to demonstrate the potential application areas of the information system developed, such as identification of crash hot-spots, development of safety intervention and safety management strategies, planning of minor safety improvement programme, and investigation of severe and fatal crashes.]]> Tue, 02 Jul 2013 16:45:15 GMT /slideshow/enhancing-road-safety-management-with-gis-mapping-and-geospatial-database/23812530 paveman@slideshare.net(paveman) ENHANCING ROAD SAFETY MANAGEMENT WITH GIS MAPPING AND GEOSPATIAL DATABASE paveman Reliable and accurate data are needed in each stage of road safety management in order to correctly identify problems and risk factors and priority treatments, and to formulate strategy, set targets and monitor performance. Ongoing, data-led diagnosis and management of the leading road safety problems enables appropriate action and resource allocation. Data relevant to road safety are collected every day, but for these data to be useful for informing road safety practice, they must be properly coded and visualized, processed and analysed in a systematic way. With the road system management strategy switching from 3Es to Safe System, there is additional challenges and demand for the integrated use of various types of road safety data. In this paper, the development of an integrated road safety data management system is introduced. The road safety data management system consists of a geospatial database for storing, querying and analysing road safety related data including crash records, road safety deficiency records, carriageway, surfacing, geometry, skid resistance, signs and road marking etc, and a GIS mapping platform for visualizing and integrating different data sets. Data from a State Highway network is used to demonstrate the potential application areas of the information system developed, such as identification of crash hot-spots, development of safety intervention and safety management strategies, planning of minor safety improvement programme, and investigation of severe and fatal crashes. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/weiliupaper210saturday15june1410-130702164515-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Reliable and accurate data are needed in each stage of road safety management in order to correctly identify problems and risk factors and priority treatments, and to formulate strategy, set targets and monitor performance. Ongoing, data-led diagnosis and management of the leading road safety problems enables appropriate action and resource allocation. Data relevant to road safety are collected every day, but for these data to be useful for informing road safety practice, they must be properly coded and visualized, processed and analysed in a systematic way. With the road system management strategy switching from 3Es to Safe System, there is additional challenges and demand for the integrated use of various types of road safety data. In this paper, the development of an integrated road safety data management system is introduced. The road safety data management system consists of a geospatial database for storing, querying and analysing road safety related data including crash records, road safety deficiency records, carriageway, surfacing, geometry, skid resistance, signs and road marking etc, and a GIS mapping platform for visualizing and integrating different data sets. Data from a State Highway network is used to demonstrate the potential application areas of the information system developed, such as identification of crash hot-spots, development of safety intervention and safety management strategies, planning of minor safety improvement programme, and investigation of severe and fatal crashes.
ENHANCING ROAD SAFETY MANAGEMENT WITH GIS MAPPING AND GEOSPATIAL DATABASE from Dr Wei Liu
]]> 2192 4 https://cdn.slidesharecdn.com/ss_thumbnails/weiliupaper210saturday15june1410-130702164515-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 PMS Development In China (Presentation In 10th Spt) /slideshow/pms-development-in-china-presentation-in-10th-spt/2755477 pmsdevelopmentinchinapresentationin10thspt-1261356783911-phpapp02
]]>
]]> Sun, 20 Dec 2009 18:53:51 GMT /slideshow/pms-development-in-china-presentation-in-10th-spt/2755477 paveman@slideshare.net(paveman) PMS Development In China (Presentation In 10th Spt) paveman <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/pmsdevelopmentinchinapresentationin10thspt-1261356783911-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br>
PMS Development In China (Presentation In 10th Spt) from Dr Wei Liu
]]> 469 4 https://cdn.slidesharecdn.com/ss_thumbnails/pmsdevelopmentinchinapresentationin10thspt-1261356783911-phpapp02-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 Screening Hydroplaning Risk Area By Hsd Data /slideshow/screening-hydroplaning-risk-area-by-hsd-data/2237766 screeninghydroplaningriskareabyhsddata-12556546227901-phpapp03
According to road safety reports from various RCAs, about 30%-40% of road crashes occurred in wet conditions and among these wet road crashes, at least 50% of the drivers experienced loss-of-control of the vehicle in a partial or full degree, an indication of hydroplaning, which often resulted in serious injury or even fatal accidents. Unfortunately, risk of hydroplaning is often only considered in the design stage of roads and highways by providing sufficient drainage and proper selection of surface materials. During the operation and maintenance of roads and highways, there is still no direct and practical method to quantify hydroplaning risk for existing roads and highways. Although several vehicle, roadway, and environmental factors affect the probability of hydroplaning, a general rule of thumb for highways is that hydroplaning can be expected for speeds above 70kph where water ponds to a depth of 2.5mm or greater over a distance of 10m or greater. In other words, for any set of driver inputs, tyre conditions and surfacing material, hydroplaning is only a function of water depth and vehicle speed. In this paper, a methodology for identifying and screening of hydroplaning risk area through analyzing the pavement transverse and longitudinal profile measurement from HSD survey will be introduced. Examples will be given to demonstrate that it will be a useful tool for road controlling agencies to evaluate the risk of hydroplaning for their road networks and to plan and carry out necessary maintenance actions to provide a safer road network for the public.]]>
According to road safety reports from various RCAs, about 30%-40% of road crashes occurred in wet conditions and among these wet road crashes, at least 50% of the drivers experienced loss-of-control of the vehicle in a partial or full degree, an indication of hydroplaning, which often resulted in serious injury or even fatal accidents. Unfortunately, risk of hydroplaning is often only considered in the design stage of roads and highways by providing sufficient drainage and proper selection of surface materials. During the operation and maintenance of roads and highways, there is still no direct and practical method to quantify hydroplaning risk for existing roads and highways. Although several vehicle, roadway, and environmental factors affect the probability of hydroplaning, a general rule of thumb for highways is that hydroplaning can be expected for speeds above 70kph where water ponds to a depth of 2.5mm or greater over a distance of 10m or greater. In other words, for any set of driver inputs, tyre conditions and surfacing material, hydroplaning is only a function of water depth and vehicle speed. In this paper, a methodology for identifying and screening of hydroplaning risk area through analyzing the pavement transverse and longitudinal profile measurement from HSD survey will be introduced. Examples will be given to demonstrate that it will be a useful tool for road controlling agencies to evaluate the risk of hydroplaning for their road networks and to plan and carry out necessary maintenance actions to provide a safer road network for the public.]]> Thu, 15 Oct 2009 19:57:52 GMT /slideshow/screening-hydroplaning-risk-area-by-hsd-data/2237766 paveman@slideshare.net(paveman) Screening Hydroplaning Risk Area By Hsd Data paveman According to road safety reports from various RCAs, about 30%-40% of road crashes occurred in wet conditions and among these wet road crashes, at least 50% of the drivers experienced loss-of-control of the vehicle in a partial or full degree, an indication of hydroplaning, which often resulted in serious injury or even fatal accidents. Unfortunately, risk of hydroplaning is often only considered in the design stage of roads and highways by providing sufficient drainage and proper selection of surface materials. During the operation and maintenance of roads and highways, there is still no direct and practical method to quantify hydroplaning risk for existing roads and highways. Although several vehicle, roadway, and environmental factors affect the probability of hydroplaning, a general rule of thumb for highways is that hydroplaning can be expected for speeds above 70kph where water ponds to a depth of 2.5mm or greater over a distance of 10m or greater. In other words, for any set of driver inputs, tyre conditions and surfacing material, hydroplaning is only a function of water depth and vehicle speed. In this paper, a methodology for identifying and screening of hydroplaning risk area through analyzing the pavement transverse and longitudinal profile measurement from HSD survey will be introduced. Examples will be given to demonstrate that it will be a useful tool for road controlling agencies to evaluate the risk of hydroplaning for their road networks and to plan and carry out necessary maintenance actions to provide a safer road network for the public. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/screeninghydroplaningriskareabyhsddata-12556546227901-phpapp03-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> According to road safety reports from various RCAs, about 30%-40% of road crashes occurred in wet conditions and among these wet road crashes, at least 50% of the drivers experienced loss-of-control of the vehicle in a partial or full degree, an indication of hydroplaning, which often resulted in serious injury or even fatal accidents. Unfortunately, risk of hydroplaning is often only considered in the design stage of roads and highways by providing sufficient drainage and proper selection of surface materials. During the operation and maintenance of roads and highways, there is still no direct and practical method to quantify hydroplaning risk for existing roads and highways. Although several vehicle, roadway, and environmental factors affect the probability of hydroplaning, a general rule of thumb for highways is that hydroplaning can be expected for speeds above 70kph where water ponds to a depth of 2.5mm or greater over a distance of 10m or greater. In other words, for any set of driver inputs, tyre conditions and surfacing material, hydroplaning is only a function of water depth and vehicle speed. In this paper, a methodology for identifying and screening of hydroplaning risk area through analyzing the pavement transverse and longitudinal profile measurement from HSD survey will be introduced. Examples will be given to demonstrate that it will be a useful tool for road controlling agencies to evaluate the risk of hydroplaning for their road networks and to plan and carry out necessary maintenance actions to provide a safer road network for the public.
Screening Hydroplaning Risk Area By Hsd Data from Dr Wei Liu
]]> 463 3 https://cdn.slidesharecdn.com/ss_thumbnails/screeninghydroplaningriskareabyhsddata-12556546227901-phpapp03-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Risk Based Pavement Structural Evaluation And Rehabilitation Design /slideshow/risk-based-pavement-structural-evaluation-and-rehabilitation-design/2237758 riskbasedpavementstructuralevaluationandrehabilitationdesign-12556545002796-phpapp03
The risk concept provides a means of incorporating some degree of certainty into the process to ensure that the outcomes of the process will provide acceptable levels of service until the end of the intended design life. In pavement design and evaluation, the risk concept is applicable for the input parameters with a high degree of uncertainty and that have an impact on the final outcome of the design process. The 2004 AUSTROADS Pavement Design Guide emphasized that much of the misunderstanding of pavement design, and resulting pavement failures over the past 20 years has been associated with uncertainty and resulting lack of reliability in design. Pavement structural evaluation and rehabilitation designs are highly dependent on the in-situ layer properties. Pavement layer thickness is an essential input in backcalculation analysis performed on measured surface deflections by Falling Weight Deflectometer (FWD) survey. Inaccurate layer thickness information may lead to significant errors in the backcalculated layer moduli and hence in the rehabilitation design. Since the pavement layer thickness has some degree of variability, it is important to consider this variability in the backcalculation analysis and rehabilitation design. In this paper, a risk-based pavement evaluation methodology will be introduced to account for the variability of pavement layer thickness through integration of FWD and GPR data. It is be demonstrated that the proposed methodology can help RCAs more accurately assess the pavement structural condition of road network with more confidence. The proposed procedure is also applicable in project level for the construction acceptance testing of new or rehabilitation pavement.]]>
The risk concept provides a means of incorporating some degree of certainty into the process to ensure that the outcomes of the process will provide acceptable levels of service until the end of the intended design life. In pavement design and evaluation, the risk concept is applicable for the input parameters with a high degree of uncertainty and that have an impact on the final outcome of the design process. The 2004 AUSTROADS Pavement Design Guide emphasized that much of the misunderstanding of pavement design, and resulting pavement failures over the past 20 years has been associated with uncertainty and resulting lack of reliability in design. Pavement structural evaluation and rehabilitation designs are highly dependent on the in-situ layer properties. Pavement layer thickness is an essential input in backcalculation analysis performed on measured surface deflections by Falling Weight Deflectometer (FWD) survey. Inaccurate layer thickness information may lead to significant errors in the backcalculated layer moduli and hence in the rehabilitation design. Since the pavement layer thickness has some degree of variability, it is important to consider this variability in the backcalculation analysis and rehabilitation design. In this paper, a risk-based pavement evaluation methodology will be introduced to account for the variability of pavement layer thickness through integration of FWD and GPR data. It is be demonstrated that the proposed methodology can help RCAs more accurately assess the pavement structural condition of road network with more confidence. The proposed procedure is also applicable in project level for the construction acceptance testing of new or rehabilitation pavement.]]> Thu, 15 Oct 2009 19:56:28 GMT /slideshow/risk-based-pavement-structural-evaluation-and-rehabilitation-design/2237758 paveman@slideshare.net(paveman) Risk Based Pavement Structural Evaluation And Rehabilitation Design paveman The risk concept provides a means of incorporating some degree of certainty into the process to ensure that the outcomes of the process will provide acceptable levels of service until the end of the intended design life. In pavement design and evaluation, the risk concept is applicable for the input parameters with a high degree of uncertainty and that have an impact on the final outcome of the design process. The 2004 AUSTROADS Pavement Design Guide emphasized that much of the misunderstanding of pavement design, and resulting pavement failures over the past 20 years has been associated with uncertainty and resulting lack of reliability in design. Pavement structural evaluation and rehabilitation designs are highly dependent on the in-situ layer properties. Pavement layer thickness is an essential input in backcalculation analysis performed on measured surface deflections by Falling Weight Deflectometer (FWD) survey. Inaccurate layer thickness information may lead to significant errors in the backcalculated layer moduli and hence in the rehabilitation design. Since the pavement layer thickness has some degree of variability, it is important to consider this variability in the backcalculation analysis and rehabilitation design. In this paper, a risk-based pavement evaluation methodology will be introduced to account for the variability of pavement layer thickness through integration of FWD and GPR data. It is be demonstrated that the proposed methodology can help RCAs more accurately assess the pavement structural condition of road network with more confidence. The proposed procedure is also applicable in project level for the construction acceptance testing of new or rehabilitation pavement. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/riskbasedpavementstructuralevaluationandrehabilitationdesign-12556545002796-phpapp03-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The risk concept provides a means of incorporating some degree of certainty into the process to ensure that the outcomes of the process will provide acceptable levels of service until the end of the intended design life. In pavement design and evaluation, the risk concept is applicable for the input parameters with a high degree of uncertainty and that have an impact on the final outcome of the design process. The 2004 AUSTROADS Pavement Design Guide emphasized that much of the misunderstanding of pavement design, and resulting pavement failures over the past 20 years has been associated with uncertainty and resulting lack of reliability in design. Pavement structural evaluation and rehabilitation designs are highly dependent on the in-situ layer properties. Pavement layer thickness is an essential input in backcalculation analysis performed on measured surface deflections by Falling Weight Deflectometer (FWD) survey. Inaccurate layer thickness information may lead to significant errors in the backcalculated layer moduli and hence in the rehabilitation design. Since the pavement layer thickness has some degree of variability, it is important to consider this variability in the backcalculation analysis and rehabilitation design. In this paper, a risk-based pavement evaluation methodology will be introduced to account for the variability of pavement layer thickness through integration of FWD and GPR data. It is be demonstrated that the proposed methodology can help RCAs more accurately assess the pavement structural condition of road network with more confidence. The proposed procedure is also applicable in project level for the construction acceptance testing of new or rehabilitation pavement.
Risk Based Pavement Structural Evaluation And Rehabilitation Design from Dr Wei Liu
]]> 1133 4 https://cdn.slidesharecdn.com/ss_thumbnails/riskbasedpavementstructuralevaluationandrehabilitationdesign-12556545002796-phpapp03-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Heavy Duty Pavement Design /slideshow/heavy-duty-pavement-design-1241375/1241375 heavydutypavementdesign-090402153539-phpapp02
Heavy duty pavements are pavements subjected to the extremely heavy wheel loads associated with freight handling vehicles in industrial facilities, such as container terminals and warehouses. Heavy duty pavement need to handle many types of freight handling vehicles, such as forklifts, straddle carriers, gantry cranes and side loaders. The purpose of pavement design is to determine the number, material composition and thickness of different layers within a pavement structure required to accommodate a given loading condition.]]>
Heavy duty pavements are pavements subjected to the extremely heavy wheel loads associated with freight handling vehicles in industrial facilities, such as container terminals and warehouses. Heavy duty pavement need to handle many types of freight handling vehicles, such as forklifts, straddle carriers, gantry cranes and side loaders. The purpose of pavement design is to determine the number, material composition and thickness of different layers within a pavement structure required to accommodate a given loading condition.]]> Thu, 02 Apr 2009 15:35:35 GMT /slideshow/heavy-duty-pavement-design-1241375/1241375 paveman@slideshare.net(paveman) Heavy Duty Pavement Design paveman Heavy duty pavements are pavements subjected to the extremely heavy wheel loads associated with freight handling vehicles in industrial facilities, such as container terminals and warehouses. Heavy duty pavement need to handle many types of freight handling vehicles, such as forklifts, straddle carriers, gantry cranes and side loaders. The purpose of pavement design is to determine the number, material composition and thickness of different layers within a pavement structure required to accommodate a given loading condition. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/heavydutypavementdesign-090402153539-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Heavy duty pavements are pavements subjected to the extremely heavy wheel loads associated with freight handling vehicles in industrial facilities, such as container terminals and warehouses. Heavy duty pavement need to handle many types of freight handling vehicles, such as forklifts, straddle carriers, gantry cranes and side loaders. The purpose of pavement design is to determine the number, material composition and thickness of different layers within a pavement structure required to accommodate a given loading condition.
Heavy Duty Pavement Design from Dr Wei Liu
]]> 4606 8 https://cdn.slidesharecdn.com/ss_thumbnails/heavydutypavementdesign-090402153539-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Automatic Road Sign Recognition From Video /slideshow/Automatic-Road-Sign-Recognition-from-Video/773841 AutomaticRoadSignRecognitionfromVideo-122723231154-phpapp02
Road signs provide important information for guiding, warning, or regulating the drivers behaviour in order to make driving safer and easier. The Road Sign Recognition (RSR) is a field of applied computer vision research concerned with the automatic detection and classification of traffic signs in traffic scene images acquired from a moving car. Pavement Management Services has developed the first truly spatially registered video system in Australia. The digital video system offers continuous, high resolution video capture of five different views along the roadway. In this paper a road sign recognition system (RS2) for the high resolution roadside video recorded by PMS system will be introduced. The recognition process of RS2 is divided into three distinct parts: detection and location, recognition and classification, and display and record for information of road signs. While lots of attempts at automated sign recognition were based on the detection of shape patterns, the proposed method for PMS Video detects road signs by recognising their patterns in color space. Based on the performance testing of proposed RS2 for the road video collected in state highway network, the proposed approach is found to be robust and fast for detection of most of road signs commonly found in New Zealand, including warning signs, information signs, regulatory signs, and street signs. The sign recognition results include the exact locations of the road sign, types of road sign, and the images containing the road sign detected, which can be presented in various format and be used in sign condition evaluation for asset management.]]>
Road signs provide important information for guiding, warning, or regulating the drivers behaviour in order to make driving safer and easier. The Road Sign Recognition (RSR) is a field of applied computer vision research concerned with the automatic detection and classification of traffic signs in traffic scene images acquired from a moving car. Pavement Management Services has developed the first truly spatially registered video system in Australia. The digital video system offers continuous, high resolution video capture of five different views along the roadway. In this paper a road sign recognition system (RS2) for the high resolution roadside video recorded by PMS system will be introduced. The recognition process of RS2 is divided into three distinct parts: detection and location, recognition and classification, and display and record for information of road signs. While lots of attempts at automated sign recognition were based on the detection of shape patterns, the proposed method for PMS Video detects road signs by recognising their patterns in color space. Based on the performance testing of proposed RS2 for the road video collected in state highway network, the proposed approach is found to be robust and fast for detection of most of road signs commonly found in New Zealand, including warning signs, information signs, regulatory signs, and street signs. The sign recognition results include the exact locations of the road sign, types of road sign, and the images containing the road sign detected, which can be presented in various format and be used in sign condition evaluation for asset management.]]> Thu, 20 Nov 2008 17:52:35 GMT /slideshow/Automatic-Road-Sign-Recognition-from-Video/773841 paveman@slideshare.net(paveman) Automatic Road Sign Recognition From Video paveman Road signs provide important information for guiding, warning, or regulating the drivers behaviour in order to make driving safer and easier. The Road Sign Recognition (RSR) is a field of applied computer vision research concerned with the automatic detection and classification of traffic signs in traffic scene images acquired from a moving car. Pavement Management Services has developed the first truly spatially registered video system in Australia. The digital video system offers continuous, high resolution video capture of five different views along the roadway. In this paper a road sign recognition system (RS2) for the high resolution roadside video recorded by PMS system will be introduced. The recognition process of RS2 is divided into three distinct parts: detection and location, recognition and classification, and display and record for information of road signs. While lots of attempts at automated sign recognition were based on the detection of shape patterns, the proposed method for PMS Video detects road signs by recognising their patterns in color space. Based on the performance testing of proposed RS2 for the road video collected in state highway network, the proposed approach is found to be robust and fast for detection of most of road signs commonly found in New Zealand, including warning signs, information signs, regulatory signs, and street signs. The sign recognition results include the exact locations of the road sign, types of road sign, and the images containing the road sign detected, which can be presented in various format and be used in sign condition evaluation for asset management. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/AutomaticRoadSignRecognitionfromVideo-122723231154-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Road signs provide important information for guiding, warning, or regulating the drivers behaviour in order to make driving safer and easier. The Road Sign Recognition (RSR) is a field of applied computer vision research concerned with the automatic detection and classification of traffic signs in traffic scene images acquired from a moving car. Pavement Management Services has developed the first truly spatially registered video system in Australia. The digital video system offers continuous, high resolution video capture of five different views along the roadway. In this paper a road sign recognition system (RS2) for the high resolution roadside video recorded by PMS system will be introduced. The recognition process of RS2 is divided into three distinct parts: detection and location, recognition and classification, and display and record for information of road signs. While lots of attempts at automated sign recognition were based on the detection of shape patterns, the proposed method for PMS Video detects road signs by recognising their patterns in color space. Based on the performance testing of proposed RS2 for the road video collected in state highway network, the proposed approach is found to be robust and fast for detection of most of road signs commonly found in New Zealand, including warning signs, information signs, regulatory signs, and street signs. The sign recognition results include the exact locations of the road sign, types of road sign, and the images containing the road sign detected, which can be presented in various format and be used in sign condition evaluation for asset management.
Automatic Road Sign Recognition From Video from Dr Wei Liu
]]> 3748 3 https://cdn.slidesharecdn.com/ss_thumbnails/AutomaticRoadSignRecognitionfromVideo-122723231154-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-paveman-48x48.jpg?cb=1724622217 Dr Wei Liu has over 15 years of diverse academic and engineering experience in China, Singapore and New Zealand. He has skills and experiences over many areas of transportation engineering, including highway and airport pavement design, pavement performance characterization and prediction, pavement and road asset management, integration of Graphical Information System (GIS) with pavement and road asset management system, road safety, and transportation sustainability. He has published over 20 technical papers in top-rated transportation engineering journals and peer-reviewed international conference proceedings. Dr Liu is also well known in the industry for his get things done attitu... http://clw1031.googlepages.com https://cdn.slidesharecdn.com/ss_thumbnails/timeseriesforecastingwithmachinelearning-200302220852-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/time-series-forecasting-with-machine-learning/229544702 Time series forecastin... https://cdn.slidesharecdn.com/ss_thumbnails/applicationofmulti-criteriaoptimisationandtrade-offanalysisdrweiliu-170330023136-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/application-of-multi-criteria-optimisation-and-tradeoff-analysis-dr-wei-liu/73921813 Application of multi c... https://cdn.slidesharecdn.com/ss_thumbnails/nams2016ghddrweiliu-optimisationandtradeoffanalysisforam-161121220629-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/optimisation-and-tradeoff-analysis-tool-for-asset-management-planning-and-programming/69375890 Optimisation and Trade...