Muwaffaq Safiyanu Labbo, Xinguo Jiang, Gatesi Jean de Dieu
Accurate traffic crash prediction is crucial for implementing effective road safety measures. This study compares the performance of Long Short-Term Memory (LSTM) and Multivariate LSTM (MLSTM) models in forecasting total crash count data in Kano State, Nigeria. Human and vehicle factors, including speed violation, tire burst, brake failure, sign light violation, and phone use while driving, are incorporated as covariates in the MLSTM model. An ARIMAX model is employed to investigate the effects of the covariates. The MLSTM model outperforms both the basic LSTM model and individual covariate models, emphasizing the synergistic effect of considering a broad range of factors. The ARIMAX model results reveal that speed violation is significantly positively correlated with total crashes, while other covariates show positive correlations but do not reach the statistical significance. The findings underscore the importance of a multivariate approach in enhancing traffic crash prediction. The MLSTM model's superior performance highlights the value of considering a comprehensive range of factors that influence crash occurrence to achieve more accurate predictions. Practical applications of these models could involve leveraging them for proactive traffic safety measures, which include increased enforcement of traffic rules, targeted driver education and campaigns, and improvements to road infrastructure.
{"title":"Traffic crash prediction model in Kano State, Nigeria: a multivariate LSTM approach","authors":"Muwaffaq Safiyanu Labbo, Xinguo Jiang, Gatesi Jean de Dieu","doi":"10.1680/jtran.24.00003","DOIUrl":"https://doi.org/10.1680/jtran.24.00003","url":null,"abstract":"Accurate traffic crash prediction is crucial for implementing effective road safety measures. This study compares the performance of Long Short-Term Memory (LSTM) and Multivariate LSTM (MLSTM) models in forecasting total crash count data in Kano State, Nigeria. Human and vehicle factors, including speed violation, tire burst, brake failure, sign light violation, and phone use while driving, are incorporated as covariates in the MLSTM model. An ARIMAX model is employed to investigate the effects of the covariates. The MLSTM model outperforms both the basic LSTM model and individual covariate models, emphasizing the synergistic effect of considering a broad range of factors. The ARIMAX model results reveal that speed violation is significantly positively correlated with total crashes, while other covariates show positive correlations but do not reach the statistical significance. The findings underscore the importance of a multivariate approach in enhancing traffic crash prediction. The MLSTM model's superior performance highlights the value of considering a comprehensive range of factors that influence crash occurrence to achieve more accurate predictions. Practical applications of these models could involve leveraging them for proactive traffic safety measures, which include increased enforcement of traffic rules, targeted driver education and campaigns, and improvements to road infrastructure.","PeriodicalId":49670,"journal":{"name":"Proceedings of the Institution of Civil Engineers-Transport","volume":"104 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140931919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mehmet Rizelioğlu, Şerife Gülsüm Demir, Turan Arslan
The study examines how the COVID-19 pandemic impacted public transportation demand in Bursa province, Turkey. It assessed pre- and during-pandemic usage of major transit systems—buses, trams, and light rail. Through an online survey of 767 participants from key activity centers, it captured changes in public transportation behavior. Using two Ordinal Logit Models, it identified factors influencing preferences, revealing that age, gender, and home-to-work distance mattered before the pandemic, while educational status and distance became crucial during the pandemic. The research further explored the tendency to walk, cycle, or e-scooter use if safe, separated non-motorized transportation routes had been provided, considering various scenarios of distance between homes and workplaces. This study will shed light on decision makers to make sustainable transportation plans by taking into account such catastrophic periods as the pandemic.
{"title":"Pandemic insights: analyzing public transport with logit models","authors":"Mehmet Rizelioğlu, Şerife Gülsüm Demir, Turan Arslan","doi":"10.1680/jtran.23.00075","DOIUrl":"https://doi.org/10.1680/jtran.23.00075","url":null,"abstract":"The study examines how the COVID-19 pandemic impacted public transportation demand in Bursa province, Turkey. It assessed pre- and during-pandemic usage of major transit systems—buses, trams, and light rail. Through an online survey of 767 participants from key activity centers, it captured changes in public transportation behavior. Using two Ordinal Logit Models, it identified factors influencing preferences, revealing that age, gender, and home-to-work distance mattered before the pandemic, while educational status and distance became crucial during the pandemic. The research further explored the tendency to walk, cycle, or e-scooter use if safe, separated non-motorized transportation routes had been provided, considering various scenarios of distance between homes and workplaces. This study will shed light on decision makers to make sustainable transportation plans by taking into account such catastrophic periods as the pandemic.","PeriodicalId":49670,"journal":{"name":"Proceedings of the Institution of Civil Engineers-Transport","volume":"39 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140833063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The transportation industry plays an important role in shaping today's economy and society with a large impact on growth and development. However, the unprecedented outbreak of COVID-19 in 2020 and the prompt measures adopted by state governments to contain the spread of the virus drastically reduced services provided by transportation systems. A burgeoning literature is growing already in developed countries evaluating the effect of the pandemic on the transport sector and proposing sustainable measures to cope with future pandemics is underway. Conversely, there is nuanced understanding of the COVID-19 response to the urban transport sector in developing countries of Sub-Saharan Africa where urban population concentration and mobility patterns are at their peak. Though marred by scanty evidence to document and assess the situation in Sub-Saharan Africa, Cameroon represents a useful “study ground” to conduct such findings. Using Douala city in Cameroon as a case study, this paper examines the effect of COVID-19 restrictions on (i) mobility patterns, (ii) transport operators and (iii) to provide evidence-based recommendations to transport authorities in responding to future pandemics in the sub-continent. A sample of 190 key informants (taxi drivers, bus drivers and bike riders) were interviewed using questionnaires complemented by focus group discussions with relevant transport authorities notably transport agency operators, transport delegates, traffic police department and transport syndicate leaders. The key findings reveal a drastic decline in passenger demand for public transport during the pandemic period, a drastic reduction in bus/taxi occupancy leading to a steep decline in intra-urban and inter-urban mobility, and a drop in the income situation of bus and taxi drivers. Transport operators lay off workers at bus terminals stemming from low turnovers. The outcome pushes for policy options for extensive collaboration among various transport-related stakeholders, consultation and effective involvement of public transport operators in decision-making that may create sustainable pathways to cope future pandemic waves.
{"title":"Impact of COVID-19 restriction measures on transport sector in Sub-Saharan Africa: insights from Douala City, Cameroon","authors":"Chianebeng Japhet Kuma, Chia Elvis Ngwah","doi":"10.1680/jtran.23.00093","DOIUrl":"https://doi.org/10.1680/jtran.23.00093","url":null,"abstract":"The transportation industry plays an important role in shaping today's economy and society with a large impact on growth and development. However, the unprecedented outbreak of COVID-19 in 2020 and the prompt measures adopted by state governments to contain the spread of the virus drastically reduced services provided by transportation systems. A burgeoning literature is growing already in developed countries evaluating the effect of the pandemic on the transport sector and proposing sustainable measures to cope with future pandemics is underway. Conversely, there is nuanced understanding of the COVID-19 response to the urban transport sector in developing countries of Sub-Saharan Africa where urban population concentration and mobility patterns are at their peak. Though marred by scanty evidence to document and assess the situation in Sub-Saharan Africa, Cameroon represents a useful “study ground” to conduct such findings. Using Douala city in Cameroon as a case study, this paper examines the effect of COVID-19 restrictions on (i) mobility patterns, (ii) transport operators and (iii) to provide evidence-based recommendations to transport authorities in responding to future pandemics in the sub-continent. A sample of 190 key informants (taxi drivers, bus drivers and bike riders) were interviewed using questionnaires complemented by focus group discussions with relevant transport authorities notably transport agency operators, transport delegates, traffic police department and transport syndicate leaders. The key findings reveal a drastic decline in passenger demand for public transport during the pandemic period, a drastic reduction in bus/taxi occupancy leading to a steep decline in intra-urban and inter-urban mobility, and a drop in the income situation of bus and taxi drivers. Transport operators lay off workers at bus terminals stemming from low turnovers. The outcome pushes for policy options for extensive collaboration among various transport-related stakeholders, consultation and effective involvement of public transport operators in decision-making that may create sustainable pathways to cope future pandemic waves.","PeriodicalId":49670,"journal":{"name":"Proceedings of the Institution of Civil Engineers-Transport","volume":"164 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140203857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hongjun Cui, Qihang Zhou, Minqing Zhu, Fei Wang, Guohui Liu
The contemporary prevalence of car ownership has presented urban intersections with burgeoning challenges in terms of motorization and congestion. Traditional signal timing schemes often encounter difficulties in handling the resulting vehicle conflicts within the physical area of the intersection. Accordingly, an intersection traffic signal timing method based on Intersection Physical Zone Traffic Status (IPAT-ATS) is proposed in this study. Different types of intersection anomalies are defined based on the impact of various abnormal vehicle operating states on vehicle flow in each entrance and exit lane. Using a traffic efficiency model, the flow state of intersections under different abnormal states is analyzed to optimally allocate traffic flows, thereby maximizing traffic efficiency under various abnormal states. The proposed method undergoes testing through simulations at the Weidi Road and Youyi Road intersection in Tianjin. Compared with the current signal timing scheme, this method significantly enhances the intersection efficiency for different traffic anomalies.
{"title":"Intersection optimization study based on traffic conditions in the physical area of intersections","authors":"Hongjun Cui, Qihang Zhou, Minqing Zhu, Fei Wang, Guohui Liu","doi":"10.1680/jtran.23.00106","DOIUrl":"https://doi.org/10.1680/jtran.23.00106","url":null,"abstract":"The contemporary prevalence of car ownership has presented urban intersections with burgeoning challenges in terms of motorization and congestion. Traditional signal timing schemes often encounter difficulties in handling the resulting vehicle conflicts within the physical area of the intersection. Accordingly, an intersection traffic signal timing method based on Intersection Physical Zone Traffic Status (IPAT-ATS) is proposed in this study. Different types of intersection anomalies are defined based on the impact of various abnormal vehicle operating states on vehicle flow in each entrance and exit lane. Using a traffic efficiency model, the flow state of intersections under different abnormal states is analyzed to optimally allocate traffic flows, thereby maximizing traffic efficiency under various abnormal states. The proposed method undergoes testing through simulations at the Weidi Road and Youyi Road intersection in Tianjin. Compared with the current signal timing scheme, this method significantly enhances the intersection efficiency for different traffic anomalies.","PeriodicalId":49670,"journal":{"name":"Proceedings of the Institution of Civil Engineers-Transport","volume":"7 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140203704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recent developments in digital technologies, including big data and internet of things concepts, have shown promising results in achieving more appropriate and effective pavement engineering through a proactive asset management approach. This could be achieved through an early diagnosis of defects and selection of an appropriate maintenance strategy informed by more granular data by the utilisation of advanced sensing systems. Such a sensing system for pavement, which should include a combination of embedded sensors and surface data sensors (e.g. cameras) would require electricity, which could be problematic when considering the growing demand for electricity around the globe. Harvesting electricity from the pavement, for example from traffic noise, which is the focus of this article, could bring new hope for achieving self-efficient and sustainable sensing systems for roads. If effective, an electricity generation system from road traffic noise could be counted towards the net zero carbon dioxide target set by road authorities. A review of the literature revealed that the noise of air pumping between the tyre and the pavement surface is the critical noise source with the highest potential for electricity generation. Harvesting, storage and conversion of noise energy to electrical energy are still in the preliminary stages.
{"title":"Harvesting electricity from road traffic noise energy – a literature review","authors":"Rashid Tanzadeh, Mehran Eskandari Torbaghan, Nikolaos Venetsaneas, Fereidoon Moghadas Nejad","doi":"10.1680/jtran.23.00057","DOIUrl":"https://doi.org/10.1680/jtran.23.00057","url":null,"abstract":"Recent developments in digital technologies, including big data and internet of things concepts, have shown promising results in achieving more appropriate and effective pavement engineering through a proactive asset management approach. This could be achieved through an early diagnosis of defects and selection of an appropriate maintenance strategy informed by more granular data by the utilisation of advanced sensing systems. Such a sensing system for pavement, which should include a combination of embedded sensors and surface data sensors (e.g. cameras) would require electricity, which could be problematic when considering the growing demand for electricity around the globe. Harvesting electricity from the pavement, for example from traffic noise, which is the focus of this article, could bring new hope for achieving self-efficient and sustainable sensing systems for roads. If effective, an electricity generation system from road traffic noise could be counted towards the net zero carbon dioxide target set by road authorities. A review of the literature revealed that the noise of air pumping between the tyre and the pavement surface is the critical noise source with the highest potential for electricity generation. Harvesting, storage and conversion of noise energy to electrical energy are still in the preliminary stages.","PeriodicalId":49670,"journal":{"name":"Proceedings of the Institution of Civil Engineers-Transport","volume":"31 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2024-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140203715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To enhance the management of parking–charging behaviours for electric vehicles (EVs) and promote the development of vehicle–grid interaction technology, the interrelation between parking and charging behaviours among EV users should be investigated further. This study, based in Changshu City, Suzhou, China, established a data linkage mechanism for parking–charging platforms and developed an EV parking–charging behaviour database, considering critical metrics like charging start time, initial and final state of charge, and charging duration. Employing the K–S test and K-means clustering methods, the diversity in parking–charging preferences between pure and plug-in hybrid EV users is explored. Results indicate that pure EVs’ parking–charging behaviours can be categorised into five distinct groups using a classification model, while those of plug-in hybrid EVs can be grouped into four categories. Both user groups include behaviours with low range anxiety, such as complete charging during special journeys, at the destination, or partial charging. Both groups also exhibit high-range-anxiety behaviours, with pure EV users favouring specific journey complete charging and plug-in hybrid EV users preferring complete charging. Notably, pure EV users also show a significant inclination towards nighttime complete charging. These insights are valuable for efficient planning and management of integrated EV facilities.
{"title":"Exploring correlated parking–charging behaviours in electric vehicles: a data-driven study","authors":"Xizhen Zhou, Yanjie Ji, Chaoyu Chen, Xudan Liu","doi":"10.1680/jtran.23.00096","DOIUrl":"https://doi.org/10.1680/jtran.23.00096","url":null,"abstract":"To enhance the management of parking–charging behaviours for electric vehicles (EVs) and promote the development of vehicle–grid interaction technology, the interrelation between parking and charging behaviours among EV users should be investigated further. This study, based in Changshu City, Suzhou, China, established a data linkage mechanism for parking–charging platforms and developed an EV parking–charging behaviour database, considering critical metrics like charging start time, initial and final state of charge, and charging duration. Employing the K–S test and <i>K</i>-means clustering methods, the diversity in parking–charging preferences between pure and plug-in hybrid EV users is explored. Results indicate that pure EVs’ parking–charging behaviours can be categorised into five distinct groups using a classification model, while those of plug-in hybrid EVs can be grouped into four categories. Both user groups include behaviours with low range anxiety, such as complete charging during special journeys, at the destination, or partial charging. Both groups also exhibit high-range-anxiety behaviours, with pure EV users favouring specific journey complete charging and plug-in hybrid EV users preferring complete charging. Notably, pure EV users also show a significant inclination towards nighttime complete charging. These insights are valuable for efficient planning and management of integrated EV facilities.","PeriodicalId":49670,"journal":{"name":"Proceedings of the Institution of Civil Engineers-Transport","volume":"234 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2024-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140316611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gatesi Jean de Dieu, Shuai Bin, Wencheng Huang, Ntakiyemungu Mathieu
As the country developed, and there was a rapid growth of various modes of transport, as well as the occurrence of road traffic crashes. Rwanda also faced the same challenges of road traffic crash severity, in which every year the number of fatalities increased progressively. To overcome these challenges, the study has focused on comparing the classification performance of eight supervised machine learning algorithms in order to visualize which is the best to predict crash severity and identify the potential crash-influential factors in Rwanda. The quantitative datasets of road traffic crashes, registered vehicles, and AADT have been used from 2010 to 2022. The ML algorithms, including LR, SVM, NB, K-NN, RF, DT, LBR, and J48, have been employed. The model results indicated that five algorithms, including RF, DT, J48, LBR, and K-NN classifiers, have shown better accuracy, greater than 80%. The RF had the highest ability to predict the crash severity in Rwanda, with an accuracy greater than 97%. The most identified influential factors were AADT, registered vehicles, causes of crashes, and vehicles involved. The model results can be applied to provide useful information to road safety decision-makers during the planning and design of road infrastructure.
{"title":"Comparison of supervised machine learning algorithms for road traffic crash prediction models in Rwanda","authors":"Gatesi Jean de Dieu, Shuai Bin, Wencheng Huang, Ntakiyemungu Mathieu","doi":"10.1680/jtran.23.00078","DOIUrl":"https://doi.org/10.1680/jtran.23.00078","url":null,"abstract":"As the country developed, and there was a rapid growth of various modes of transport, as well as the occurrence of road traffic crashes. Rwanda also faced the same challenges of road traffic crash severity, in which every year the number of fatalities increased progressively. To overcome these challenges, the study has focused on comparing the classification performance of eight supervised machine learning algorithms in order to visualize which is the best to predict crash severity and identify the potential crash-influential factors in Rwanda. The quantitative datasets of road traffic crashes, registered vehicles, and AADT have been used from 2010 to 2022. The ML algorithms, including LR, SVM, NB, K-NN, RF, DT, LBR, and J48, have been employed. The model results indicated that five algorithms, including RF, DT, J48, LBR, and K-NN classifiers, have shown better accuracy, greater than 80%. The RF had the highest ability to predict the crash severity in Rwanda, with an accuracy greater than 97%. The most identified influential factors were AADT, registered vehicles, causes of crashes, and vehicles involved. The model results can be applied to provide useful information to road safety decision-makers during the planning and design of road infrastructure.","PeriodicalId":49670,"journal":{"name":"Proceedings of the Institution of Civil Engineers-Transport","volume":"20 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138520138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The change of the dynamic properties of high-speed rail (HSR) slab track structure can have a great impact on the ride quality and safety of the trains. However, the dynamic response of the wheel-rail system, which is related to operational safety has rarely been considered in the existing rules of service reliability assessment for track structure. To consider the operational safety in reliability assessment for slab track, this paper proposes a hybrid method, in which the serviceability limit state (SLS) is first defined with respect to the derailment coefficient and wheel unloading rate. In reliability index calculation, the response surface method (RSM) and the first-order reliability method (FORM) are employed to solve the implicit expression of wheel-rail force in the SLS equation. To reduce the computation cost in calculating the wheel-rail force, a surrogate model expressing the nonlinear mapping of the wheel-rail interaction based on support vector regression (SVR) is proposed. The performance of the hybrid method is then verified against the Monte Carlo simulation (MCS) method and the BP neural network-based method from the perspective of computation efficiency and accuracy. It is found that the computation time of the hybrid method is reduced to only 1/8.4 of the BP neural network method, while the accuracy of the reliability index can achieve 98% for derailment coefficient and 97% for wheel unloading rate. Lastly, the hybrid method is applied to assess the reliability of a typical slab track structure under the changing stiffness and damping coefficients of the fasteners, cement asphalt (CA) mortar, and foundation. The results show that the stiffness and damping of fasteners have a larger impact on both wheel-rail dynamics and track reliability, compared to those of CA mortar and foundation. This research can provide new insights into the reliability assessment for HSR slab track with respect to the operational safety of the trains.
{"title":"A hybrid method for service reliability assessment of slab track subject to change of structural stiffness and damping","authors":"Zai-Wei Li, Xiao-Zhou Liu, Bin Zhang","doi":"10.1680/jtran.21.00087","DOIUrl":"https://doi.org/10.1680/jtran.21.00087","url":null,"abstract":"The change of the dynamic properties of high-speed rail (HSR) slab track structure can have a great impact on the ride quality and safety of the trains. However, the dynamic response of the wheel-rail system, which is related to operational safety has rarely been considered in the existing rules of service reliability assessment for track structure. To consider the operational safety in reliability assessment for slab track, this paper proposes a hybrid method, in which the serviceability limit state (SLS) is first defined with respect to the derailment coefficient and wheel unloading rate. In reliability index calculation, the response surface method (RSM) and the first-order reliability method (FORM) are employed to solve the implicit expression of wheel-rail force in the SLS equation. To reduce the computation cost in calculating the wheel-rail force, a surrogate model expressing the nonlinear mapping of the wheel-rail interaction based on support vector regression (SVR) is proposed. The performance of the hybrid method is then verified against the Monte Carlo simulation (MCS) method and the BP neural network-based method from the perspective of computation efficiency and accuracy. It is found that the computation time of the hybrid method is reduced to only 1/8.4 of the BP neural network method, while the accuracy of the reliability index can achieve 98% for derailment coefficient and 97% for wheel unloading rate. Lastly, the hybrid method is applied to assess the reliability of a typical slab track structure under the changing stiffness and damping coefficients of the fasteners, cement asphalt (CA) mortar, and foundation. The results show that the stiffness and damping of fasteners have a larger impact on both wheel-rail dynamics and track reliability, compared to those of CA mortar and foundation. This research can provide new insights into the reliability assessment for HSR slab track with respect to the operational safety of the trains.","PeriodicalId":49670,"journal":{"name":"Proceedings of the Institution of Civil Engineers-Transport","volume":"58 5-6","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138520139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The study fabricated a combined wall with the help of lightweight steel structural wall skeleton and foam concrete and designed four sets of strain experiments for walls with different foam concrete densities (FC) and steel content, and analysed the relationship between the wall parameters and shear strength with finite element software. In the displacement results, the higher the density of FC, the higher the load-bearing capacity. When the density of foam concrete is 1000 kg/m 3 and 1600 kg/m 3 , the wall will lose its load carrying capacity after a maximum load of 80 KN and 90 KN respectively. The greater the axial compression ratio of the sample, the greater the shear capacity of the combined wall. When the displacement distance is 30 mm, the maximum load is 162 KN, 110 KN, 94 KN and 85 KN when the shear span ratio is 1.0, 2.0, 3.0 and 4.0 respectively, and the load carrying capacity decreases with the increase of the shear span ratio, and compared with the shear span ratio of 1.0, the load carrying capacity decreases by 23%, 41% and 51% successively. The maximum loads of the combined walls were 88 KN, 79 KN, 81 KN and 62 KN when the densities were 800, 1000, 1200 and 1600, respectively; and 75 KN, 80 KN, 81 KN, 94 KN and 101 KN when the steel content ratios were 0.5, 1.0, 1.5, 2.0 and 2.5, respectively. The higher the steel content, the greater the bearing capacity of the wall, it is expected that the parameters obtained from this study and the change rule of wall load carrying capacity can provide a certain reference basis for the construction project and accelerate the realisation of green building materials.
{"title":"Research on the bearing capacity of foam concrete wall materials in green buildings","authors":"Weiwei Li","doi":"10.1680/jsmic.23.00017","DOIUrl":"https://doi.org/10.1680/jsmic.23.00017","url":null,"abstract":"The study fabricated a combined wall with the help of lightweight steel structural wall skeleton and foam concrete and designed four sets of strain experiments for walls with different foam concrete densities (FC) and steel content, and analysed the relationship between the wall parameters and shear strength with finite element software. In the displacement results, the higher the density of FC, the higher the load-bearing capacity. When the density of foam concrete is 1000 kg/m 3 and 1600 kg/m 3 , the wall will lose its load carrying capacity after a maximum load of 80 KN and 90 KN respectively. The greater the axial compression ratio of the sample, the greater the shear capacity of the combined wall. When the displacement distance is 30 mm, the maximum load is 162 KN, 110 KN, 94 KN and 85 KN when the shear span ratio is 1.0, 2.0, 3.0 and 4.0 respectively, and the load carrying capacity decreases with the increase of the shear span ratio, and compared with the shear span ratio of 1.0, the load carrying capacity decreases by 23%, 41% and 51% successively. The maximum loads of the combined walls were 88 KN, 79 KN, 81 KN and 62 KN when the densities were 800, 1000, 1200 and 1600, respectively; and 75 KN, 80 KN, 81 KN, 94 KN and 101 KN when the steel content ratios were 0.5, 1.0, 1.5, 2.0 and 2.5, respectively. The higher the steel content, the greater the bearing capacity of the wall, it is expected that the parameters obtained from this study and the change rule of wall load carrying capacity can provide a certain reference basis for the construction project and accelerate the realisation of green building materials.","PeriodicalId":49670,"journal":{"name":"Proceedings of the Institution of Civil Engineers-Transport","volume":"14 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134900830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xuan Chen, Ying Liu, Heliang Xiao, Jun Hou, Shuigen Zhang
With urban development and industrial restructuring, many old industrial buildings are left unused, making the renewal of such buildings a crucial aspect of urban construction. To meet the growing need for intelligent and efficient urban construction, this study proposes a greedy algorithm that considers the update of action spaces (AP-GA) to optimise the basic work of old building renovation – the layout of rows of tiles. The algorithm is optimised using the idea of action space update and backtracking. Real testing shows that the optimisation method provides the highest optimisation rate (18.20%) for AP-GA and reduces the number of cut bricks. Although the running time is slightly longer than that of the original algorithm, the brick integrity of the layout is significantly improved. When compared with other algorithms, the optimised AP-GA has the shortest average running time of 580.1 μs, demonstrating its effectiveness in the layout of rows of bricks. This new algorithm provides a more efficient and excellent method for the renewal and renovation of old industrial buildings, broadening the research perspective in the field.
{"title":"Research on the renovation of old industrial buildings in the context of smart city construction: Based on improved greedy algorithm","authors":"Xuan Chen, Ying Liu, Heliang Xiao, Jun Hou, Shuigen Zhang","doi":"10.1680/jsmic.23.00012","DOIUrl":"https://doi.org/10.1680/jsmic.23.00012","url":null,"abstract":"With urban development and industrial restructuring, many old industrial buildings are left unused, making the renewal of such buildings a crucial aspect of urban construction. To meet the growing need for intelligent and efficient urban construction, this study proposes a greedy algorithm that considers the update of action spaces (AP-GA) to optimise the basic work of old building renovation – the layout of rows of tiles. The algorithm is optimised using the idea of action space update and backtracking. Real testing shows that the optimisation method provides the highest optimisation rate (18.20%) for AP-GA and reduces the number of cut bricks. Although the running time is slightly longer than that of the original algorithm, the brick integrity of the layout is significantly improved. When compared with other algorithms, the optimised AP-GA has the shortest average running time of 580.1 μs, demonstrating its effectiveness in the layout of rows of bricks. This new algorithm provides a more efficient and excellent method for the renewal and renovation of old industrial buildings, broadening the research perspective in the field.","PeriodicalId":49670,"journal":{"name":"Proceedings of the Institution of Civil Engineers-Transport","volume":"28 42","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135043107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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