Water-related hazards, such as river floods, flash floods and droughts, are becoming more frequent in the Upper Chao Phraya River Basin, Thailand, due to climate change and urbanization, causing significant societal, ...Water-related hazards, such as river floods, flash floods and droughts, are becoming more frequent in the Upper Chao Phraya River Basin, Thailand, due to climate change and urbanization, causing significant societal, economic, and environmental damage. This study supports decision-making for nature-based solutions (NBS) to address mitigate these hazards. Using multi-criteria decision analysis, simulation modeling, and spatial analysis, the study identified precipitation and river discharges as key hazard drivers. Mapping hazard severity at various scales, the findings suggest that expanding green areas and water storage can enhance water management and reduce hazard impacts. This research offers critical insights for NBS adoption in water-related risk reduction.展开更多
Road transport plays a crucial role in facilitating mobility and the movement of goods,particularly in the Extended Bangkok Metropolitan Region(EBMR),Thailand.This area is undergoing rapid industrialization and urbani...Road transport plays a crucial role in facilitating mobility and the movement of goods,particularly in the Extended Bangkok Metropolitan Region(EBMR),Thailand.This area is undergoing rapid industrialization and urbanization,resulting in significant energy consumption and greenhouse gas(GHG)emissions.This study examined the relationships among individual socioeconomic factors,travel characteristics,and energy consumption characteristics and their impacts on GHG emissions from road transport.The path analysis technique was applied to identify the key driving factors and their causal relationships.The data were collected through 1600 questionnaire surveys with road drivers in representative areas of the EBMR from December 2022 to May 2023.The results revealed that individual socioeconomic factors significantly influenced GHG emissions from road transport.Among the drivers,factors such as income,age,education,and driving experience indirectly influenced travel characteristics and energy consumption characteristics,impacting GHG emissions.Similarly,individual socioeconomic factors affected the travel characteristics of tourists and personal travelers.Driving experience was a crucial factor for public road transport and freight vehicle drivers,influencing travel characteristics and contributing to GHG emissions.These findings highlight the importance of key policy recommendations,such as promoting the adoption of electric vehicles,optimizing public transport,incentivizing low-emission tourism,and modernizing freight transport with clean technologies,to enhance efficiency,reduce emissions,and support regional sustainability.This study provides policy-makers with insights into the key factors influencing GHG emissions across different driving factors,revealing how individual socioeconomic factors impact travel characteristics and energy consumption characteristics.The findings will inform the development of targeted emission reduction strategies and sustainable transport policies.展开更多
Bangladesh aims to become a high-income country by 2041,requiring investment in critical infrastructure sectors.Disruptions in one sector can affect others,so prioritizing actions for key sectors is essential when res...Bangladesh aims to become a high-income country by 2041,requiring investment in critical infrastructure sectors.Disruptions in one sector can affect others,so prioritizing actions for key sectors is essential when resources are limited.Since no country has endless resources,the current strategy is to focus on developing infrastructure in order of importance.This means that the most critical infrastructure is given priority when allocating resources.The aim of this study was to identify the critical infrastructure sectors and their interdependencies in Bangladesh.While the science of critical infrastructure protection and resilience is well-developed in high-income and developed economies,this research sheds light on identifying critical infrastructure in developing nations like Bangladesh.To identify the critical infrastructure sectors,a comprehensive literature survey was conducted,which was verified and validated by country experts.Policymakers,practitioners,and researchers were consulted through key informant interviews(KII).Interpretive structural modeling(ISM)was applied to determine the interdependencies among identified sectors.Furthermore,cross-impact matrix multiplication applied to classification(MICMAC)analysis was applied to categorize the identified sectors based on driving power and dependence of sectors.The study found that 14 sectors-energy,information and communication technology(ICT),media and culture,law enforcement,transportation,among others-need extra protection measures.It also identified infrastructures with driving power and dependencies in the country’s context.Additionally,this article offers recommendations for improving policy and institutional actions to enhance the resilience of critical infrastructure in the country.展开更多
In this paper,we propose a novel probabilistic method for predicting the undrained bearing capacity of spatially variable soils.Our approach combines a Gaussian process regression(GPR)-based surrogate model with rando...In this paper,we propose a novel probabilistic method for predicting the undrained bearing capacity of spatially variable soils.Our approach combines a Gaussian process regression(GPR)-based surrogate model with random cell-based smoothed finite analysis.The Gaussian process emulator(GPE)serves as a statistical tool for making predictions from a data set.First,we validate the accuracy and efficiency of kinematic limit analysis using the cell-based smoothed finite element method(CS-FEM)against the standard finite element method(FEM)and edge-based smoothed FEM(ES-FEM).The numerical results demonstrate that the CS-FEM framework surpasses traditional numerical approaches,establishing its reliability in computing collapse loads.Subsequently,we conduct several hundred simulations to develop a surrogate model for predicting the undrained bearing capacity of shallow foundations.By utilizing various kernel functions,we enhance the accuracy of the GPE in these predictions.This method offers a practical and efficient solution,effectively addressing multiple uncertainties.Numerical results indicate that the GPE significantly boosts computational efficiency,achieving satisfactory outcomes within minutes compared to the days required for conventional simulations.Notably,the mean absolute percentage error(MAPE)decreases from 2.38%to 1.82%for rough foundations when employing Matérn and rational quadratic kernel functions,respectively.Additionally,combining different kernel functions further enhances the accuracy of collapse load predictions.展开更多
Leakage events occurring at multiple locations simultaneously generate overlapping and topologydependent pressure signatures,making reliable detection and subsequent restoration planning a persistent challenge in wate...Leakage events occurring at multiple locations simultaneously generate overlapping and topologydependent pressure signatures,making reliable detection and subsequent restoration planning a persistent challenge in water distribution systems(WDSs).While recent data-driven techniques have improved the ability to identify anomalous hydraulic behavior,most approaches remain limited to the detection stage and offer little guidance on how utilities should prioritize repairs once multiple failures are identified.To bridge this gap,this study proposes an integrated framework that links topology-aware leakage detection with quantitative restoration prioritization.First,a multi-task learning framework based on Graph Attention Networks(GAT)is employed to jointly detect both the location and magnitude of multiple leakages by explicitly incorporating hydraulic responses and network topology into the learning process.The model’s detection robustness is evaluated across networks with contrasting looped,branched,and hybrid topologies to examine how structural characteristics influence detection accuracy under multievent conditions.Second,the study develops a restoration-planning module that constructs a two-objective decision space combining restoration cost and segment vulnerability,where the latter accounts for disruption potential arising from hydraulic importance and local service connectivity.Non-dominated sorting is used to derive Pareto-optimal restoration sequences,enabling explicit quantification of the trade-offs between operational cost and service disruption.This provides decision-makers with a ranked set of restoration orders that reflect both hydraulic impact and functional risk,rather than relying on heuristics or cost-only criteria.Notably,the proposed framework separates offline training from online inference,requiring only a single forward pass for real-time decision-making without the need for iterative hydraulic simulations.Results demonstrate that topology strongly governs both detection performance and the structure of optimal repair sequences,underscoring the importance of integrating network-aware learning with multi-criteria restoration evaluation.展开更多
Properties of aggregates are majorly influenced by parameters of source rocks viz.,formation process,chemical composition,impurities,volume of pores,and grain size.The study presents a review of aggregate treatment me...Properties of aggregates are majorly influenced by parameters of source rocks viz.,formation process,chemical composition,impurities,volume of pores,and grain size.The study presents a review of aggregate treatment methods and its efficacy to enhance the quality of aggregate.Various aspects of aggregate treatment methods like processing temperature,the dosage of additives,adaptability in the field is studied for three treatment methods viz.,polymer coating,cementitious coating,and chemical treatments.The paper also presents an insight to understand the effect of different treatment methods on mix properties and performance parameters of asphalt mixes.The review revealed that the shape properties of aggregates can be enhanced by the incorporating suitable crushing process(two-stage or three-stage).Whereas,physical and durability properties of aggregates can be improved by various treatment methods like polymer coating,Zycosoil treatment.It was further inferred from the review that treatment methods can have moderate effects on the mechanical properties of aggregates,since,it is mostly dependent on properties of source rocks.展开更多
文摘Water-related hazards, such as river floods, flash floods and droughts, are becoming more frequent in the Upper Chao Phraya River Basin, Thailand, due to climate change and urbanization, causing significant societal, economic, and environmental damage. This study supports decision-making for nature-based solutions (NBS) to address mitigate these hazards. Using multi-criteria decision analysis, simulation modeling, and spatial analysis, the study identified precipitation and river discharges as key hazard drivers. Mapping hazard severity at various scales, the findings suggest that expanding green areas and water storage can enhance water management and reduce hazard impacts. This research offers critical insights for NBS adoption in water-related risk reduction.
基金the Royal Thai Government(RTG)provided financing for this study,as well as a scholarship to assist PhD studies at the Asian Institute of Technology(AIT)The National Science and Technology Development Agency(NSTDA)of Thailand via the Development of High-Quality Research Graduates in Science and Technology Project,a collaboration between NSTDA and AIT, also offers a top-up scholarship for this study
文摘Road transport plays a crucial role in facilitating mobility and the movement of goods,particularly in the Extended Bangkok Metropolitan Region(EBMR),Thailand.This area is undergoing rapid industrialization and urbanization,resulting in significant energy consumption and greenhouse gas(GHG)emissions.This study examined the relationships among individual socioeconomic factors,travel characteristics,and energy consumption characteristics and their impacts on GHG emissions from road transport.The path analysis technique was applied to identify the key driving factors and their causal relationships.The data were collected through 1600 questionnaire surveys with road drivers in representative areas of the EBMR from December 2022 to May 2023.The results revealed that individual socioeconomic factors significantly influenced GHG emissions from road transport.Among the drivers,factors such as income,age,education,and driving experience indirectly influenced travel characteristics and energy consumption characteristics,impacting GHG emissions.Similarly,individual socioeconomic factors affected the travel characteristics of tourists and personal travelers.Driving experience was a crucial factor for public road transport and freight vehicle drivers,influencing travel characteristics and contributing to GHG emissions.These findings highlight the importance of key policy recommendations,such as promoting the adoption of electric vehicles,optimizing public transport,incentivizing low-emission tourism,and modernizing freight transport with clean technologies,to enhance efficiency,reduce emissions,and support regional sustainability.This study provides policy-makers with insights into the key factors influencing GHG emissions across different driving factors,revealing how individual socioeconomic factors impact travel characteristics and energy consumption characteristics.The findings will inform the development of targeted emission reduction strategies and sustainable transport policies.
基金partial scholarship support under the EDITS-AIT projectThe EDITS-AIT project at the Asian Institute of Technology, Thailand, received funding from the Energy Demand changes Induced by Technological and Social innovations (EDITS) project, which is part of the initiative coordinated by the Research Institute of Innovative Technology for the Earth (RITE) and the International Institute for Applied Systems Analysis (IIASA) (and funded by the Ministry of Economy, Trade, and Industry (METI), Japan)
文摘Bangladesh aims to become a high-income country by 2041,requiring investment in critical infrastructure sectors.Disruptions in one sector can affect others,so prioritizing actions for key sectors is essential when resources are limited.Since no country has endless resources,the current strategy is to focus on developing infrastructure in order of importance.This means that the most critical infrastructure is given priority when allocating resources.The aim of this study was to identify the critical infrastructure sectors and their interdependencies in Bangladesh.While the science of critical infrastructure protection and resilience is well-developed in high-income and developed economies,this research sheds light on identifying critical infrastructure in developing nations like Bangladesh.To identify the critical infrastructure sectors,a comprehensive literature survey was conducted,which was verified and validated by country experts.Policymakers,practitioners,and researchers were consulted through key informant interviews(KII).Interpretive structural modeling(ISM)was applied to determine the interdependencies among identified sectors.Furthermore,cross-impact matrix multiplication applied to classification(MICMAC)analysis was applied to categorize the identified sectors based on driving power and dependence of sectors.The study found that 14 sectors-energy,information and communication technology(ICT),media and culture,law enforcement,transportation,among others-need extra protection measures.It also identified infrastructures with driving power and dependencies in the country’s context.Additionally,this article offers recommendations for improving policy and institutional actions to enhance the resilience of critical infrastructure in the country.
文摘In this paper,we propose a novel probabilistic method for predicting the undrained bearing capacity of spatially variable soils.Our approach combines a Gaussian process regression(GPR)-based surrogate model with random cell-based smoothed finite analysis.The Gaussian process emulator(GPE)serves as a statistical tool for making predictions from a data set.First,we validate the accuracy and efficiency of kinematic limit analysis using the cell-based smoothed finite element method(CS-FEM)against the standard finite element method(FEM)and edge-based smoothed FEM(ES-FEM).The numerical results demonstrate that the CS-FEM framework surpasses traditional numerical approaches,establishing its reliability in computing collapse loads.Subsequently,we conduct several hundred simulations to develop a surrogate model for predicting the undrained bearing capacity of shallow foundations.By utilizing various kernel functions,we enhance the accuracy of the GPE in these predictions.This method offers a practical and efficient solution,effectively addressing multiple uncertainties.Numerical results indicate that the GPE significantly boosts computational efficiency,achieving satisfactory outcomes within minutes compared to the days required for conventional simulations.Notably,the mean absolute percentage error(MAPE)decreases from 2.38%to 1.82%for rough foundations when employing Matérn and rational quadratic kernel functions,respectively.Additionally,combining different kernel functions further enhances the accuracy of collapse load predictions.
基金supported by the Korea Environmental Industry&Technology Institute(KEITI)through Water Management Program for Drought,funded by Korea Ministry of Environment(MOE)(RS-2023-00231944)supported by the research grant of the Gyeongsang National University in 2023。
文摘Leakage events occurring at multiple locations simultaneously generate overlapping and topologydependent pressure signatures,making reliable detection and subsequent restoration planning a persistent challenge in water distribution systems(WDSs).While recent data-driven techniques have improved the ability to identify anomalous hydraulic behavior,most approaches remain limited to the detection stage and offer little guidance on how utilities should prioritize repairs once multiple failures are identified.To bridge this gap,this study proposes an integrated framework that links topology-aware leakage detection with quantitative restoration prioritization.First,a multi-task learning framework based on Graph Attention Networks(GAT)is employed to jointly detect both the location and magnitude of multiple leakages by explicitly incorporating hydraulic responses and network topology into the learning process.The model’s detection robustness is evaluated across networks with contrasting looped,branched,and hybrid topologies to examine how structural characteristics influence detection accuracy under multievent conditions.Second,the study develops a restoration-planning module that constructs a two-objective decision space combining restoration cost and segment vulnerability,where the latter accounts for disruption potential arising from hydraulic importance and local service connectivity.Non-dominated sorting is used to derive Pareto-optimal restoration sequences,enabling explicit quantification of the trade-offs between operational cost and service disruption.This provides decision-makers with a ranked set of restoration orders that reflect both hydraulic impact and functional risk,rather than relying on heuristics or cost-only criteria.Notably,the proposed framework separates offline training from online inference,requiring only a single forward pass for real-time decision-making without the need for iterative hydraulic simulations.Results demonstrate that topology strongly governs both detection performance and the structure of optimal repair sequences,underscoring the importance of integrating network-aware learning with multi-criteria restoration evaluation.
基金National Highways Authority of India(NHAI)for providing financial support to carry out this research。
文摘Properties of aggregates are majorly influenced by parameters of source rocks viz.,formation process,chemical composition,impurities,volume of pores,and grain size.The study presents a review of aggregate treatment methods and its efficacy to enhance the quality of aggregate.Various aspects of aggregate treatment methods like processing temperature,the dosage of additives,adaptability in the field is studied for three treatment methods viz.,polymer coating,cementitious coating,and chemical treatments.The paper also presents an insight to understand the effect of different treatment methods on mix properties and performance parameters of asphalt mixes.The review revealed that the shape properties of aggregates can be enhanced by the incorporating suitable crushing process(two-stage or three-stage).Whereas,physical and durability properties of aggregates can be improved by various treatment methods like polymer coating,Zycosoil treatment.It was further inferred from the review that treatment methods can have moderate effects on the mechanical properties of aggregates,since,it is mostly dependent on properties of source rocks.
