Pub Date : 2023-08-01DOI: 10.36334/modsim.2023.mathur
A. Mathur, S. Moka, Z. Botev
: Recent advances in the technological ability to capture and collect data have meant that high-dimensional datasets are now ubiquitous in the fields of engineering, economics, finance, biology, and health sciences to name a few. In the case where the data collected is not labeled it is often desirable to obtain an accurate low-rank approximation for the data which is relatively low-cost to obtain and memory efficient. Such an approximation is useful to speed up downstream matrix computations that are often required in large-scale learning algorithms. The Column Subset Selection Problem (CSSP) is a tool to generate low-rank approximations based on a subset of data instances or features from the dataset. The chosen subset of instances or features are commonly referred to as “landmark” points. The choice of landmark points determines how accurate the low-rank approximation is. More specifically, the challenge in the CSSP is to select the best k columns of a data matrix X ∈ R m × n that span its column space. That is, for any binary vector s ∈ { 0 , 1 } n , compute
最近在捕获和收集数据的技术能力方面取得的进展意味着高维数据集现在在工程、经济、金融、生物和健康科学等领域无处不在。在收集的数据没有标记的情况下，通常需要获得数据的准确低秩近似值，这种近似值相对低成本且内存效率高。这种近似对于加速大规模学习算法中经常需要的下游矩阵计算是有用的。列子集选择问题(Column子集Selection Problem, CSSP)是一种基于数据集中的数据实例子集或特征生成低秩近似的工具。所选择的实例或特征子集通常称为“地标”点。地标点的选择决定了低秩近似的精度。更具体地说，CSSP中的挑战是选择数据矩阵X∈R m × n的最佳k列，这些列跨越了它的列空间。即，对于任意二进制向量s∈{0,1}n，计算
{"title":"A continuous optimization algorithm for column subset selection","authors":"A. Mathur, S. Moka, Z. Botev","doi":"10.36334/modsim.2023.mathur","DOIUrl":"https://doi.org/10.36334/modsim.2023.mathur","url":null,"abstract":": Recent advances in the technological ability to capture and collect data have meant that high-dimensional datasets are now ubiquitous in the fields of engineering, economics, finance, biology, and health sciences to name a few. In the case where the data collected is not labeled it is often desirable to obtain an accurate low-rank approximation for the data which is relatively low-cost to obtain and memory efficient. Such an approximation is useful to speed up downstream matrix computations that are often required in large-scale learning algorithms. The Column Subset Selection Problem (CSSP) is a tool to generate low-rank approximations based on a subset of data instances or features from the dataset. The chosen subset of instances or features are commonly referred to as “landmark” points. The choice of landmark points determines how accurate the low-rank approximation is. More specifically, the challenge in the CSSP is to select the best k columns of a data matrix X ∈ R m × n that span its column space. That is, for any binary vector s ∈ { 0 , 1 } n , compute","PeriodicalId":390064,"journal":{"name":"MODSIM2023, 25th International Congress on Modelling and Simulation.","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125215423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-01DOI: 10.36334/modsim.2023.salinas
H. Salinas, E. Veneklaas, P. Poot, E. Trevenen, M. Renton
: Competitive ability of species can be understood as their tendency to suppress the growth of neighbours (competitive effect), and resist suppression from others (competitive response). Plant species differ in their competitive ability due to their different traits, for example root architecture. However, our understanding of the effect of root architecture on competitive ability is limited due to challenges in empirically measuring and controlling root systems. Allocating biomass to roots will be beneficial to a plant if the costs are compensated by benefits that eventually lead to greater production of reproductive biomass. The costs and benefits of any particular root biomass allocation and root-morphology strategy are likely to depend on the number and position of competing neighbours, and thus traits that maximise fitness under no competition could reduce fitness under competition. We hypothesised that species adapted to low levels of competition will generally have a higher ability to suppress the growth of neighbours than species adapted to higher levels of competition, but a lower ability to resist suppression from neighbours. We used a functional-structural root model to simulate the development of roots with different architectural traits. This model runs on a daily time step and represents roots as a set of connected nodes, with growth being simulated with the addition of new nodes. Roots take up water (assumed to be the limiting resource) from the surrounding substrate. Increase in plant biomass is assumed to be proportional to the amount of water acquired by the root system. A fraction of the obtained biomass is allocated to root and the remainder to above-ground biomass. The model includes a number of parameters that define various architectural and allocation traits; using different values for these model parameters results in different root biomass allocation and root-architecture strategies. The functional-structural root model was coupled with an evolutionary algorithm to find combinations of architectural parameters (which we can call “genotypes”) that maximised above-ground biomass in a range of different competition scenarios. These competition scenarios included a target plant developing alone or surrounded by one, two, three or four neighbours. The performance of the target plant was evaluated by assuming that the final above-ground biomass of the plant was a proxy for reproductive fitness. Finally, we conducted virtual competition experiments using plants of the optimal genotypes in different competition scenarios. Our results support our hypothesis that the negative effect of competition on above-ground biomass is greater on genotypes selected under lower competition. However, plants with these genotypes had higher maximum above-ground biomass, and a higher competitive effect. These results suggest that there is an intrinsic trade-off between maximising biomass under low-competition, and resistance to competition. Our re
{"title":"Plant root architecture: A trade-off between tolerance to competitors and potential growth","authors":"H. Salinas, E. Veneklaas, P. Poot, E. Trevenen, M. Renton","doi":"10.36334/modsim.2023.salinas","DOIUrl":"https://doi.org/10.36334/modsim.2023.salinas","url":null,"abstract":": Competitive ability of species can be understood as their tendency to suppress the growth of neighbours (competitive effect), and resist suppression from others (competitive response). Plant species differ in their competitive ability due to their different traits, for example root architecture. However, our understanding of the effect of root architecture on competitive ability is limited due to challenges in empirically measuring and controlling root systems. Allocating biomass to roots will be beneficial to a plant if the costs are compensated by benefits that eventually lead to greater production of reproductive biomass. The costs and benefits of any particular root biomass allocation and root-morphology strategy are likely to depend on the number and position of competing neighbours, and thus traits that maximise fitness under no competition could reduce fitness under competition. We hypothesised that species adapted to low levels of competition will generally have a higher ability to suppress the growth of neighbours than species adapted to higher levels of competition, but a lower ability to resist suppression from neighbours. We used a functional-structural root model to simulate the development of roots with different architectural traits. This model runs on a daily time step and represents roots as a set of connected nodes, with growth being simulated with the addition of new nodes. Roots take up water (assumed to be the limiting resource) from the surrounding substrate. Increase in plant biomass is assumed to be proportional to the amount of water acquired by the root system. A fraction of the obtained biomass is allocated to root and the remainder to above-ground biomass. The model includes a number of parameters that define various architectural and allocation traits; using different values for these model parameters results in different root biomass allocation and root-architecture strategies. The functional-structural root model was coupled with an evolutionary algorithm to find combinations of architectural parameters (which we can call “genotypes”) that maximised above-ground biomass in a range of different competition scenarios. These competition scenarios included a target plant developing alone or surrounded by one, two, three or four neighbours. The performance of the target plant was evaluated by assuming that the final above-ground biomass of the plant was a proxy for reproductive fitness. Finally, we conducted virtual competition experiments using plants of the optimal genotypes in different competition scenarios. Our results support our hypothesis that the negative effect of competition on above-ground biomass is greater on genotypes selected under lower competition. However, plants with these genotypes had higher maximum above-ground biomass, and a higher competitive effect. These results suggest that there is an intrinsic trade-off between maximising biomass under low-competition, and resistance to competition. Our re","PeriodicalId":390064,"journal":{"name":"MODSIM2023, 25th International Congress on Modelling and Simulation.","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131194552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-01DOI: 10.36334/modsim.2023.rajanayaka
{"title":"Developing an integrated socioeconomic-hydrological model to support catchment-scale water allocation decisions","authors":"","doi":"10.36334/modsim.2023.rajanayaka","DOIUrl":"https://doi.org/10.36334/modsim.2023.rajanayaka","url":null,"abstract":"","PeriodicalId":390064,"journal":{"name":"MODSIM2023, 25th International Congress on Modelling and Simulation.","volume":"302 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132742113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-01DOI: 10.36334/modsim.2023.foster
J. Foster, P. Graham, J. Hayward
: A growing national and global focus on low greenhouse gas emission fuels, and cost reductions in renewable electricity generation and energy storage, has given rise to renewed attention to water electrolysis to produce hydrogen. While the current dominant form of hydrogen production is steam methane reforming of natural gas, water electrolysis is the principal method of generating hydrogen from renewable energy. When coupled with large-scale renewable energy technologies, renewable hydrogen production may serve as a zero-carbon feedstock for industrial processes. In this paper, we investigate the optimisation of both capacity investment in electricity generation, electricity storage and hydrogen electrolysers as well as the hourly least-cost operation of the same electrolysers simultaneously with generation and storage operation. We use a custom model capable of considering these within a time horizon of one year using annualised discounting. We consider how to design a least-cost system out to 2050 that meets hydrogen off-take requirements through the development of prospective variable renewable resources. We study the relevant trade-offs that are shown between: capacity investments costs in electrolysers and renewable electricity generation and storage; electrolyser sizing and utilisation, and; operational profiles in established networks compared to greenfield development. In each region an annual minimum hydrogen production requirement was imposed. That requirement must be met by new investment in PEM and alkaline electrolyser technology fed by grid-connected electrical energy. The model was free to determine the aggregate regional electrolyser capacity and the utilisation of that capacity for each hour of the year subject to a minimum utilisation or “minimum run” constraint. The STABLE model (Spatial Temporal Analysis of Balancing Levelised-Cost of Energy, adapted from the DIETER open-source model to the Australian context) is a large-scale linear optimization model minimising system cost, deciding hourly operational variables simultaneously with capacity investment in transmission, generation, storage and electrolysers. Results have been presented and compared for the following cases in the Australian context:
{"title":"Hydrogen electrolyser capacity investment in the Australian context","authors":"J. Foster, P. Graham, J. Hayward","doi":"10.36334/modsim.2023.foster","DOIUrl":"https://doi.org/10.36334/modsim.2023.foster","url":null,"abstract":": A growing national and global focus on low greenhouse gas emission fuels, and cost reductions in renewable electricity generation and energy storage, has given rise to renewed attention to water electrolysis to produce hydrogen. While the current dominant form of hydrogen production is steam methane reforming of natural gas, water electrolysis is the principal method of generating hydrogen from renewable energy. When coupled with large-scale renewable energy technologies, renewable hydrogen production may serve as a zero-carbon feedstock for industrial processes. In this paper, we investigate the optimisation of both capacity investment in electricity generation, electricity storage and hydrogen electrolysers as well as the hourly least-cost operation of the same electrolysers simultaneously with generation and storage operation. We use a custom model capable of considering these within a time horizon of one year using annualised discounting. We consider how to design a least-cost system out to 2050 that meets hydrogen off-take requirements through the development of prospective variable renewable resources. We study the relevant trade-offs that are shown between: capacity investments costs in electrolysers and renewable electricity generation and storage; electrolyser sizing and utilisation, and; operational profiles in established networks compared to greenfield development. In each region an annual minimum hydrogen production requirement was imposed. That requirement must be met by new investment in PEM and alkaline electrolyser technology fed by grid-connected electrical energy. The model was free to determine the aggregate regional electrolyser capacity and the utilisation of that capacity for each hour of the year subject to a minimum utilisation or “minimum run” constraint. The STABLE model (Spatial Temporal Analysis of Balancing Levelised-Cost of Energy, adapted from the DIETER open-source model to the Australian context) is a large-scale linear optimization model minimising system cost, deciding hourly operational variables simultaneously with capacity investment in transmission, generation, storage and electrolysers. Results have been presented and compared for the following cases in the Australian context:","PeriodicalId":390064,"journal":{"name":"MODSIM2023, 25th International Congress on Modelling and Simulation.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128853953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-01DOI: 10.36334/modsim.2023.poddar
{"title":"Estimation of long-term solar power fluctuations across Australia using high-resolution regional climate projections","authors":"","doi":"10.36334/modsim.2023.poddar","DOIUrl":"https://doi.org/10.36334/modsim.2023.poddar","url":null,"abstract":"","PeriodicalId":390064,"journal":{"name":"MODSIM2023, 25th International Congress on Modelling and Simulation.","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115927125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-01DOI: 10.36334/modsim.2023.kularathna
{"title":"Planned development of a modelling information system for Melbourne's water supply system","authors":"","doi":"10.36334/modsim.2023.kularathna","DOIUrl":"https://doi.org/10.36334/modsim.2023.kularathna","url":null,"abstract":"","PeriodicalId":390064,"journal":{"name":"MODSIM2023, 25th International Congress on Modelling and Simulation.","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124566814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-01DOI: 10.36334/modsim.2023.yao674
Shijin Yao, Bin Wang, Deli Liu, Xiuliang Jin, Haoming Xia, Qiang Yu
: Grain weight (GW) holds significant importance as a crop phenotype parameter, playing a direct role in determining grain yield. As a more stable crop yield parameter (Hamblin et al., 1978), it stands as a pivotal characteristic for crop breeders, agronomists, and farmers alike, serving as a crucial factor for assessing and choosing high-yielding varieties, refining crop management techniques, and projecting crop quality and nutritional composition. Through diligent observation of this parameter, researchers and farmers are empowered to make well-informed choices concerning strategies for enhancing crops, nutrient allocation, irrigation methods, and other elements that impact overall crop productivity. Here we collected red-green-blue (RGB) and multispectral imagery from UAV throughout the entire wheat growth stages in November 2021 – June 2022, covering 300 wheat plots. Diverse crop features were derived from UAV-based imagery, namely vegetable indices (VIs) including NDVI to NDYI, texture indices (GLCM) including contrast to dissimilarity, canopy cover calculated by the ratio of canopy pixels over the total number of pixels within a plot, and canopy height extracted from the digital surface model (DSM) generated from the 3D point cloud model. The crop yield composition parameter of GW was estimated using artificial neural network (ANN) with different types of crop features derived from UAV-imagery. Our machine learning model could estimate GW accurately with R 2 and nRMSE being 0.51 and 14.3%, respectively. We utilized the GW estimation model to predict the GW of various wheat types, including winter wheat, spring wheat, high-gluten wheat, and disease-resistant wheat, across more than 230 test plots. We also examined the stability of GW among repeated plots. Subsequently, we identified and selected wheat varieties with high GW. Furthermore, we analyzed the correlation between the GW of different wheat types and their corresponding yields, highlighting the significance of considering GW as a crucial parameter in the selection of high-yielding varieties. Our study showcased the promising capabilities of utilizing multispectral sensor imagery acquired from UAV-captured data across different spectral bands to forecast the crucial crop phenotype parameter, GW. By harnessing the GW model and integrating GW data with other variables like grain number per unit area and crop-specific characteristics, we envision our model to be a valuable tool in constructing yield prediction models that provide reliable estimates of the final harvest (Bai et al., 2022). Furthermore, the application of our GW model exhibits potential in supporting researchers to identify genetic traits and management practices that influence GW, as well as evaluating the impact of weather conditions on crop productivity. This, in turn, can facilitate the advancement of enhanced crop varieties and cultivation techniques, ultimately benefiting the agricultural industry as a whole.
籽粒重(GW)作为一种重要的作物表型参数，对籽粒产量起着直接的决定作用。作为一个更稳定的作物产量参数(Hamblin et al.， 1978)，它是作物育种家、农学家和农民的关键特征，是评估和选择高产品种、改进作物管理技术、预测作物质量和营养成分的关键因素。通过对这一参数的仔细观察，研究人员和农民有权在提高作物产量、养分分配、灌溉方法和其他影响作物整体生产力的因素方面做出明智的选择。在这里，我们收集了2021年11月至2022年6月期间整个小麦生长阶段的无人机红绿蓝(RGB)和多光谱图像，覆盖了300个小麦地块。从基于无人机的图像中获得多种作物特征，即蔬菜指数(VIs)(包括NDVI和NDYI)、纹理指数(GLCM)(包括对比与不相似度)、由地块内冠层像素与总像素之比计算的冠层覆盖度，以及从三维点云模型生成的数字表面模型(DSM)中提取的冠层高度。利用基于无人机影像的不同作物类型特征的人工神经网络估计了GW的作物产量组成参数。我们的机器学习模型可以准确地估计GW, r2和nRMSE分别为0.51和14.3%。在230多个试验小区中，利用该估算模型对冬小麦、春小麦、高筋小麦和抗病小麦等不同小麦品种的小麦产量进行了预测。我们还检查了重复地块中GW的稳定性。随后，我们鉴定和选择了高GW的小麦品种。此外，我们还分析了不同小麦品种的吉瓦数与其产量之间的相关性，强调将吉瓦数作为高产品种选择的关键参数的重要性。我们的研究展示了利用无人机在不同光谱波段捕获数据获得的多光谱传感器图像来预测关键作物表型参数GW的前景。通过利用GW模型并将GW数据与其他变量(如单位面积谷物数和作物特性)相结合，我们设想我们的模型将成为构建产量预测模型的宝贵工具，为最终收获提供可靠的估计(Bai et al.， 2022)。此外，我们的全球变暖模型在支持研究人员识别影响全球变暖的遗传性状和管理实践，以及评估天气条件对作物生产力的影响方面显示出潜力。这反过来又可以促进改良作物品种和栽培技术的进步，最终使整个农业产业受益。
{"title":"Estimating wheat grain weight using UAV-multispectral imagery and machine learning","authors":"Shijin Yao, Bin Wang, Deli Liu, Xiuliang Jin, Haoming Xia, Qiang Yu","doi":"10.36334/modsim.2023.yao674","DOIUrl":"https://doi.org/10.36334/modsim.2023.yao674","url":null,"abstract":": Grain weight (GW) holds significant importance as a crop phenotype parameter, playing a direct role in determining grain yield. As a more stable crop yield parameter (Hamblin et al., 1978), it stands as a pivotal characteristic for crop breeders, agronomists, and farmers alike, serving as a crucial factor for assessing and choosing high-yielding varieties, refining crop management techniques, and projecting crop quality and nutritional composition. Through diligent observation of this parameter, researchers and farmers are empowered to make well-informed choices concerning strategies for enhancing crops, nutrient allocation, irrigation methods, and other elements that impact overall crop productivity. Here we collected red-green-blue (RGB) and multispectral imagery from UAV throughout the entire wheat growth stages in November 2021 – June 2022, covering 300 wheat plots. Diverse crop features were derived from UAV-based imagery, namely vegetable indices (VIs) including NDVI to NDYI, texture indices (GLCM) including contrast to dissimilarity, canopy cover calculated by the ratio of canopy pixels over the total number of pixels within a plot, and canopy height extracted from the digital surface model (DSM) generated from the 3D point cloud model. The crop yield composition parameter of GW was estimated using artificial neural network (ANN) with different types of crop features derived from UAV-imagery. Our machine learning model could estimate GW accurately with R 2 and nRMSE being 0.51 and 14.3%, respectively. We utilized the GW estimation model to predict the GW of various wheat types, including winter wheat, spring wheat, high-gluten wheat, and disease-resistant wheat, across more than 230 test plots. We also examined the stability of GW among repeated plots. Subsequently, we identified and selected wheat varieties with high GW. Furthermore, we analyzed the correlation between the GW of different wheat types and their corresponding yields, highlighting the significance of considering GW as a crucial parameter in the selection of high-yielding varieties. Our study showcased the promising capabilities of utilizing multispectral sensor imagery acquired from UAV-captured data across different spectral bands to forecast the crucial crop phenotype parameter, GW. By harnessing the GW model and integrating GW data with other variables like grain number per unit area and crop-specific characteristics, we envision our model to be a valuable tool in constructing yield prediction models that provide reliable estimates of the final harvest (Bai et al., 2022). Furthermore, the application of our GW model exhibits potential in supporting researchers to identify genetic traits and management practices that influence GW, as well as evaluating the impact of weather conditions on crop productivity. This, in turn, can facilitate the advancement of enhanced crop varieties and cultivation techniques, ultimately benefiting the agricultural industry as a whole.","PeriodicalId":390064,"journal":{"name":"MODSIM2023, 25th International Congress on Modelling and Simulation.","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114546826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-01DOI: 10.36334/modsim.2023.jeong
H. Jeong, J. Won, Seung-Hyeon Shin
: As the proportion of aging buildings has been rapidly increasing nationwide in South Korea, issues related to the structural safety of buildings and social citizen safety have arisen. The Building Act stipulates that if aging buildings have structural issues, they should undergo reconstruction. Similarly, the Special Act on Promotion of and Support for Urban Regeneration regulates the reconstruction of buildings to improve the urban landscape and residential environment. Therefore, demolition work through urban redevelopment is on the rise. Demolition collapse accidents are one of the risks associated with construction sites. Thus, the analysis of actual demolition collapse accidents demonstrated that technical issues, relevant regulations, government policies, and the relationships among stakeholders such as involved parties both external and internal to the site can have an impact on the safety of demolition work from the permit stage. In this study, the construction processes of two major accidents (i.e., Jamwon-dong collapse accident and Gwangju Hak-dong collapse accident) were investigated to propose a safety control structure model that can estimate the safety interaction between stakeholders. In this study, the STAMP CAST analysis, which are systematic accident analysis methods, was used to investigate the risk factors and safety control measures that may occur during demolition work. Then, a safety control structure model of the demolition work system was developed through STAMP STPA analysis. However, it was difficult to reflect on all of the stakeholders’ opinions, and the site characteristics as the developed safety control structure model was based on accident cases. Therefore, a legal safety control structure model was derived by referring to major laws related to demolition work, such as but not limited to the Building Management Act, Occupational Safety and Health Act, and Construction Waste Recycling Promotion Act in South Korea. The exploratory analysis showed that the occurrence of demolition collapse accidents was not only due to technical issues but
{"title":"Systematic analysis and modeling for safety control in building demolition: A case study in South Korea","authors":"H. Jeong, J. Won, Seung-Hyeon Shin","doi":"10.36334/modsim.2023.jeong","DOIUrl":"https://doi.org/10.36334/modsim.2023.jeong","url":null,"abstract":": As the proportion of aging buildings has been rapidly increasing nationwide in South Korea, issues related to the structural safety of buildings and social citizen safety have arisen. The Building Act stipulates that if aging buildings have structural issues, they should undergo reconstruction. Similarly, the Special Act on Promotion of and Support for Urban Regeneration regulates the reconstruction of buildings to improve the urban landscape and residential environment. Therefore, demolition work through urban redevelopment is on the rise. Demolition collapse accidents are one of the risks associated with construction sites. Thus, the analysis of actual demolition collapse accidents demonstrated that technical issues, relevant regulations, government policies, and the relationships among stakeholders such as involved parties both external and internal to the site can have an impact on the safety of demolition work from the permit stage. In this study, the construction processes of two major accidents (i.e., Jamwon-dong collapse accident and Gwangju Hak-dong collapse accident) were investigated to propose a safety control structure model that can estimate the safety interaction between stakeholders. In this study, the STAMP CAST analysis, which are systematic accident analysis methods, was used to investigate the risk factors and safety control measures that may occur during demolition work. Then, a safety control structure model of the demolition work system was developed through STAMP STPA analysis. However, it was difficult to reflect on all of the stakeholders’ opinions, and the site characteristics as the developed safety control structure model was based on accident cases. Therefore, a legal safety control structure model was derived by referring to major laws related to demolition work, such as but not limited to the Building Management Act, Occupational Safety and Health Act, and Construction Waste Recycling Promotion Act in South Korea. The exploratory analysis showed that the occurrence of demolition collapse accidents was not only due to technical issues but","PeriodicalId":390064,"journal":{"name":"MODSIM2023, 25th International Congress on Modelling and Simulation.","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116999550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-01DOI: 10.36334/modsim.2023.perraud379
J. Perraud, R. Bridgart, Catherine Carney, F. Ximenes
: Australia is in the bottom quartile of OECD countries in using bioenergy as a proportion of total energy supply. A lack of reliable information about underutilised biomass feedstocks was identified as a significant roadblock to the development of bioenergy projects across Australia. Existing biomass resources derived from various sources were mapped in New South Wales, Australia as part of the Australian Biomass for Bioenergy Assessment Project. A parallel project is trialling the establishment of Australian native species as short-rotation biomass crops. Focussing for now on the available data for New South Wales (https://www.dpi.nsw.gov.au/forestry/science/forest-carbon/biomass-for-bioenergy) we have implemented a software product and web browser tool to facilitate the exploitation of this data. This is a tool for a spectrum of users who want to rapidly obtain aggregate information and explore scenarios for the use of biomass, notably but not limited to energy supply. Two categories of sources of biomass are conceptualised: agricultural, forestry and other residues, and the plantation of energy crops in marginal or degraded land areas. Users can define geographic or point areas as sources of feedstocks, transport and pelletisation hubs, power plants such as a biomass power stations or hybrid solar-biomass plants, and linkages between these nodes (Figure 1). From the defined geographic extents of interest, the tool queries the spatial layers from an ArcGIS server to obtain the potential available biomass for each crop type and biomass productivity from woody biomass crops. Users can optionally adjust values and attributes such as moisture content and energy density. Additional custom feedstock types can be defined. Scenarios can be saved as files to the desktop. The types of outputs reported for a scenario include the carbon footprint and cost of transport, or which size of power plant is feasible given the available biomass of a scenario and the characteristics of the solar irradiation for a hybrid solar-biomass plant. It is also easy to produce the amount of avoided equivalent carbon footprint from a fossil fuel such as coal. The tool is built mostly with Python. Two packages contain respectively the domain logic and user interface components. The domain package features a data model that consistently handles biomass feedstocks throughout the system, to avoid possible confusions such as wet and dry masses in calculations. The web front-end is built using Jupyter notebooks and the packages ipywidgets and jupyter-flex to work via a web browser. The web application is currently available as a testing site with an access restricted to testers and key stakeholders. The broader vision for this activity is an evolvable software infrastructure for holistic, large-scale assessment of projects using biomass including their engineering, economic and environmental aspects. We are considering opportunities to use this software as an integration platform
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Pub Date : 2023-08-01DOI: 10.36334/modsim.2023.kim371
Y. M. Kim, C. S. Yoo, S. Yoon
: The usefulness of the Muskingum model for flow routing has been widely recognized and made the model one of the most commonly used hydrologic channel flood routing models. Despite its wide application in hydrologic flood routing, the model is unsuitable for ungauged channel reaches due to its need for data from hydrologic observations for the estimation of its parameters. This paper uses a different method based on basin characteristics suggested by Yoo et al. (2013) at Chungju Dam basin to compare its results with the current hydrologic observation based parameter estimation method. The Chungju Dam basin was divided into subbasins and each subbasin’s exit was further divided into either upstream or downstream section of the channel reach. Rainfall data from 2010 to 2020 was used and a total of 55 rainfall events were selected from the entire data. 12 channel reaches were formed based on 15 water level stations within the study basin. The amount of storage was calculated using the inflow and outflow hydrographs for every channel reach, and then the Muskingum parameters were estimated by the graphical method. The time of concentration 𝑇𝑇 𝑐𝑐 and storage coefficient 𝐾𝐾 values were calculated by the difference between the times of concentration ( 𝑇𝑇 𝑐𝑐1 and 𝑇𝑇 𝑐𝑐2 ) and the storage coefficients ( 𝐾𝐾 1 and 𝐾𝐾 2 ) formed by two subbasins created according to Yoo and others’ method. The weighting factor 𝑥𝑥 was calculated by using the range of the time interval ∆ t . The 𝑇𝑇 𝑐𝑐 and storage coefficient 𝐾𝐾 values used in the parameter estimation process were calculated by empirical equations such as the ones proposed by Lee et al (2013) and Kraven II and Sabol. In order to obtain the ideal outflow hydrograph to be used in the graphical method, the lateral inflow was calculated using the effective rainfall obtained by the SCS-CN method and Clark unit hydrograph. The Muskingum parameters were estimated using the final outflow hydrograph derived after removing the upstream lateral inflow and base flow from the observed outflow hydrograph data at the downstream in graphical method. The Muskingum parameters estimated by Yoo and others’ method, which is based on Lee’s and Kraven II and Sabol empirical equations were compared with the estimations made by the graphical method. The results showed that the empirical equations proposed by Lee and others give values that are closer to the Muskingum parameters obtained by the graphical method. This suggests that the Lee’s empirical equations derived using the basin characteristics of the Chungju Dam basin yield more appropriate results.
Muskingum模型对水流走向的有用性已得到广泛认可，并使该模型成为最常用的水文河道洪水走向模型之一。尽管该模型在水文洪水路径中得到了广泛的应用，但由于其参数估计需要水文观测数据，因此不适用于未测量的河段。本文采用Yoo et al.(2013)在忠州坝流域提出的基于流域特征的不同方法，将其结果与目前基于水文观测的参数估计方法进行比较。忠州坝盆地被划分为多个子盆地，每个子盆地的出口被进一步划分为河道河段的上游或下游段。使用2010 - 2020年的降雨数据，从整个数据中选取55个降雨事件。根据研究流域内15个水位站，形成了12条河道。利用各河段的入流和出流曲线计算库存量，然后用图解法估计了Muskingum参数。浓缩时间𝑇𝑇𝑐𝑐和储存系数𝐾𝐾是根据Yoo等人的方法建立的两个子盆地的浓缩时间(𝑇𝑇𝑐𝑐1和𝑇𝑇𝑐𝑐2)和储存系数(𝐾𝐾1和𝐾𝐾2)的差值计算出来的。采用时间区间的取值范围∆t计算权重因子。参数估计过程中使用的𝑇𝑇𝑐𝑐和存储系数𝐾𝐾值由Lee et al(2013)和Kraven II和Sabol提出的经验方程计算。为了得到图解法中理想的流出线线，利用SCS-CN法和Clark单位线线得到的有效降雨量计算了侧向流入。Muskingum参数是用图形法从下游观测的流出线数据中去除上游侧向流入和基流后得到的最终流出线来估计的。将Yoo等人基于Lee’s和Kraven II以及Sabol经验方程估计的Muskingum参数与图解法估计的参数进行了比较。结果表明，由Lee等人提出的经验方程给出的值更接近于用图解法得到的Muskingum参数。这表明，利用忠州坝流域特征推导的Lee经验方程可以得到更合适的结果。
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