According to the study made by United Nation Economic Commission for Africa, Ethiopia stands as one of the worst countries with respect to road safety performance in terms of traffic accident fatalities per 10,000 veh...According to the study made by United Nation Economic Commission for Africa, Ethiopia stands as one of the worst countries with respect to road safety performance in terms of traffic accident fatalities per 10,000 vehicles (i.e. 95 in 2007/8). Road safety generally depends on humans, vehicles, and highway conditions. These factors influence road safety separately or in combination. One of the basic means to improve road safety is to reduce hazardous conditions of roads. The main objective of this study is to identify and rank hazardous locations and propose appropriate simple and inexpensive countermeasures along Hawassa-Shashemene-Bulbula main two-lane rural road. Accordingly, the road and traffic data were collected from field investigation and Ethiopian Road Authority and accident data were gathered from police stations. Then, the study road equally divided into short sections of 1.5 km and traffic volume and accident frequencies assigned for each road site to predict theoretical frequencies of accident. Empirical Bayes method and Safety Performance Function have been used to estimate an index known as Potential for Safety Improvement (PSI) for each site of the study area to identify and rank road sites. The result showed that out of 43 road segments 22 of them were identified as dangerous road segments. Moreover, based on further criterion established for screening the ranked road sections 8 road segments were found the most dangerous road segments as they have contributed 76% of total PSI values. The degree of haphazardness of a given road segment in the study area has directly associated with the availability of risk indicating road and traffic factors. Finally, it recommends that regulatory body of road safety in the study area should give high priority and immediate response for the improvement of most dangerous road segments.展开更多
The permanent deformation (rutting) of pavement is a major distress in flexible pavement. It is related to vehicles properties and/or pavement materials and conditions. This article presents an extensive experimental ...The permanent deformation (rutting) of pavement is a major distress in flexible pavement. It is related to vehicles properties and/or pavement materials and conditions. This article presents an extensive experimental investigation in order to compare between the aggregate gradation according to Superpave and Marshall methods of asphalt concrete mix design on pavement rutting and to examine the sensitivity of rutting resistance to aggregate gradation. A wheel truck machine has been used for measurement of pavement rutting (permanent deformation). The tests were carried out at two controlled different air temperature 55℃ and 25℃. The results obtained showed that the adopting of aggregate gradation procedure of Superpave method of pavement mix design for Marshall method of asphalt concrete mix design can reduce the pavement rutting by about 50%. This achievement may be related to missing of three sieves in aggregate gradation procedure of Marshall method which controls rounded and finer aggregate particles. These sieves provide more continuity for aggregate gradation to ensure filling unnecessary gaps and produce more contact points between the aggregates in Hot Mix Asphalt (HMA). The outputs of the research support modifying Marshall method of asphalt concrete mix design by adopting aggregate gradation proposed in Superpave method. The results of study also showed that the coarser aggregate provided more resistance to pavement rutting.展开更多
Ensuring the reliability of pipe pile designs under earthquake loading necessitates an accurate determination of lateral displacement and bending moment,typically achieved through complex numerical modeling to address...Ensuring the reliability of pipe pile designs under earthquake loading necessitates an accurate determination of lateral displacement and bending moment,typically achieved through complex numerical modeling to address the intricacies of soil-pile interaction.Despite recent advancements in machine learning techniques,there is a persistent need to establish data-driven models that can predict these parameters without using numerical simulations due to the difficulties in conducting correct numerical simulations and the need for constitutive modelling parameters that are not readily available.This research presents novel lateral displacement and bending moment predictive models for closed and open-ended pipe piles,employing a Genetic Programming(GP)approach.Utilizing a soil dataset extracted from existing literature,comprising 392 data points for both pile types embedded in cohesionless soil and subjected to earthquake loading,the study intentionally limited input parameters to three features to enhance model simplicity:Standard Penetration Test(SPT)corrected blow count(N60),Peak Ground Acceleration(PGA),and pile slenderness ratio(L/D).Model performance was assessed via coefficient of determination(R^(2)),Root Mean Squared Error(RMSE),and Mean Absolute Error(MAE),with R^(2) values ranging from 0.95 to 0.99 for the training set,and from 0.92 to 0.98 for the testing set,which indicate of high accuracy of prediction.Finally,the study concludes with a sensitivity analysis,evaluating the influence of each input parameter across different pile types.展开更多
In this investigation,the bearing capacity solution of a strip footing in anisotropic clay under inclined and eccentric load is analyzed using the numerical simulation model.The lower and upper bound finite element li...In this investigation,the bearing capacity solution of a strip footing in anisotropic clay under inclined and eccentric load is analyzed using the numerical simulation model.The lower and upper bound finite element limit analysis(FELA)approaches are utilized to establish precise modeling and derive the numerical outcomes of a strip footing’s bearing capacity.All analyses use effective automated adaptive meshes with three iteration stages to enhance the accuracy of the outcomes.The parametric analysis is performed to examine the influence of four dimensionless parameters which are taken into account in this study,namely the anisotropic strength ratio,the dimensionless eccentricity,the load inclination angle,and the adhesion factor to the bearing capacity factor.Furthermore,a new model has been proposed to predict the bearing capacity factor for the calculation of the undrained bearing capacity for footings resting on an anisotropic clay using an advanced data-driven method(MOGA-EPR).The new model takes into account the anisotropy,eccentricity,and inclination of the applied load and could be used with confidence in routine designs of shallow foundations in undrained conditions with the consideration of the anisotropic strengths of clays.展开更多
Despite the importance of heavy vehicles in Australia’s transportation system,little is known on the factors influencing injury severity from accidents involving a single heavy vehicle.Heavy vehicular crashes have be...Despite the importance of heavy vehicles in Australia’s transportation system,little is known on the factors influencing injury severity from accidents involving a single heavy vehicle.Heavy vehicular crashes have been one of the main causes of fatal injuries in Australia,and this raises safety concerns for transport authorities,insurance companies,and emergency services.Although there have been several potential attempts to identify the factors contributing to heavy vehicle crashes and injury severity,it is still necessary to reduce the number of traffic crashes and lower the fatality rate involving heavy vehicles.The aims of this study were investigating the effects of heavy trucks’presence in accidents on the injury severity level sustained by the vehicle driver and detecting the contributing factors that lead to specific injury severity levels.Fixed-and random-parameter ordered probit and logit models were applied for predicting the likelihood of three injury severity categories severe,moderate,and no injury based on data from crashes caused by heavy trucks in Victoria,Australia in 2012-2017.The results showed that the random-parameter ordered probit model performed better than the other models did.Twenty variables(i.e.,factors)were found to be significant,and 12 of them were found to have random parameters that were normally distributed.Since some of the investigated factors had different effects on the type of injury severity in Australia,this paper does not recommend generalizing the findings from other case studies.Based on the findings,Victoria state authorities can have insight and enhanced understanding of the specific factors that lead to various types of injury severity involving heavy trucks.Consequently,the safety of all road users,including heavy vehicle drivers,can be enhanced.展开更多
文摘According to the study made by United Nation Economic Commission for Africa, Ethiopia stands as one of the worst countries with respect to road safety performance in terms of traffic accident fatalities per 10,000 vehicles (i.e. 95 in 2007/8). Road safety generally depends on humans, vehicles, and highway conditions. These factors influence road safety separately or in combination. One of the basic means to improve road safety is to reduce hazardous conditions of roads. The main objective of this study is to identify and rank hazardous locations and propose appropriate simple and inexpensive countermeasures along Hawassa-Shashemene-Bulbula main two-lane rural road. Accordingly, the road and traffic data were collected from field investigation and Ethiopian Road Authority and accident data were gathered from police stations. Then, the study road equally divided into short sections of 1.5 km and traffic volume and accident frequencies assigned for each road site to predict theoretical frequencies of accident. Empirical Bayes method and Safety Performance Function have been used to estimate an index known as Potential for Safety Improvement (PSI) for each site of the study area to identify and rank road sites. The result showed that out of 43 road segments 22 of them were identified as dangerous road segments. Moreover, based on further criterion established for screening the ranked road sections 8 road segments were found the most dangerous road segments as they have contributed 76% of total PSI values. The degree of haphazardness of a given road segment in the study area has directly associated with the availability of risk indicating road and traffic factors. Finally, it recommends that regulatory body of road safety in the study area should give high priority and immediate response for the improvement of most dangerous road segments.
文摘The permanent deformation (rutting) of pavement is a major distress in flexible pavement. It is related to vehicles properties and/or pavement materials and conditions. This article presents an extensive experimental investigation in order to compare between the aggregate gradation according to Superpave and Marshall methods of asphalt concrete mix design on pavement rutting and to examine the sensitivity of rutting resistance to aggregate gradation. A wheel truck machine has been used for measurement of pavement rutting (permanent deformation). The tests were carried out at two controlled different air temperature 55℃ and 25℃. The results obtained showed that the adopting of aggregate gradation procedure of Superpave method of pavement mix design for Marshall method of asphalt concrete mix design can reduce the pavement rutting by about 50%. This achievement may be related to missing of three sieves in aggregate gradation procedure of Marshall method which controls rounded and finer aggregate particles. These sieves provide more continuity for aggregate gradation to ensure filling unnecessary gaps and produce more contact points between the aggregates in Hot Mix Asphalt (HMA). The outputs of the research support modifying Marshall method of asphalt concrete mix design by adopting aggregate gradation proposed in Superpave method. The results of study also showed that the coarser aggregate provided more resistance to pavement rutting.
文摘Ensuring the reliability of pipe pile designs under earthquake loading necessitates an accurate determination of lateral displacement and bending moment,typically achieved through complex numerical modeling to address the intricacies of soil-pile interaction.Despite recent advancements in machine learning techniques,there is a persistent need to establish data-driven models that can predict these parameters without using numerical simulations due to the difficulties in conducting correct numerical simulations and the need for constitutive modelling parameters that are not readily available.This research presents novel lateral displacement and bending moment predictive models for closed and open-ended pipe piles,employing a Genetic Programming(GP)approach.Utilizing a soil dataset extracted from existing literature,comprising 392 data points for both pile types embedded in cohesionless soil and subjected to earthquake loading,the study intentionally limited input parameters to three features to enhance model simplicity:Standard Penetration Test(SPT)corrected blow count(N60),Peak Ground Acceleration(PGA),and pile slenderness ratio(L/D).Model performance was assessed via coefficient of determination(R^(2)),Root Mean Squared Error(RMSE),and Mean Absolute Error(MAE),with R^(2) values ranging from 0.95 to 0.99 for the training set,and from 0.92 to 0.98 for the testing set,which indicate of high accuracy of prediction.Finally,the study concludes with a sensitivity analysis,evaluating the influence of each input parameter across different pile types.
基金financially supported by Office of the Permanent Secretary,Ministry of Higher Education,Science,Research and Innovation under Research Grant for New Scholar(RGNS 65-112).
文摘In this investigation,the bearing capacity solution of a strip footing in anisotropic clay under inclined and eccentric load is analyzed using the numerical simulation model.The lower and upper bound finite element limit analysis(FELA)approaches are utilized to establish precise modeling and derive the numerical outcomes of a strip footing’s bearing capacity.All analyses use effective automated adaptive meshes with three iteration stages to enhance the accuracy of the outcomes.The parametric analysis is performed to examine the influence of four dimensionless parameters which are taken into account in this study,namely the anisotropic strength ratio,the dimensionless eccentricity,the load inclination angle,and the adhesion factor to the bearing capacity factor.Furthermore,a new model has been proposed to predict the bearing capacity factor for the calculation of the undrained bearing capacity for footings resting on an anisotropic clay using an advanced data-driven method(MOGA-EPR).The new model takes into account the anisotropy,eccentricity,and inclination of the applied load and could be used with confidence in routine designs of shallow foundations in undrained conditions with the consideration of the anisotropic strengths of clays.
文摘Despite the importance of heavy vehicles in Australia’s transportation system,little is known on the factors influencing injury severity from accidents involving a single heavy vehicle.Heavy vehicular crashes have been one of the main causes of fatal injuries in Australia,and this raises safety concerns for transport authorities,insurance companies,and emergency services.Although there have been several potential attempts to identify the factors contributing to heavy vehicle crashes and injury severity,it is still necessary to reduce the number of traffic crashes and lower the fatality rate involving heavy vehicles.The aims of this study were investigating the effects of heavy trucks’presence in accidents on the injury severity level sustained by the vehicle driver and detecting the contributing factors that lead to specific injury severity levels.Fixed-and random-parameter ordered probit and logit models were applied for predicting the likelihood of three injury severity categories severe,moderate,and no injury based on data from crashes caused by heavy trucks in Victoria,Australia in 2012-2017.The results showed that the random-parameter ordered probit model performed better than the other models did.Twenty variables(i.e.,factors)were found to be significant,and 12 of them were found to have random parameters that were normally distributed.Since some of the investigated factors had different effects on the type of injury severity in Australia,this paper does not recommend generalizing the findings from other case studies.Based on the findings,Victoria state authorities can have insight and enhanced understanding of the specific factors that lead to various types of injury severity involving heavy trucks.Consequently,the safety of all road users,including heavy vehicle drivers,can be enhanced.