Pub Date : 2021-11-01DOI: 10.1017/S0890060421000184
M. Rahimian, J. Duarte, L. Iulo
Abstract This paper discusses the development of an experimental software prototype that uses surrogate models for predicting the monthly energy consumption of urban-scale community design scenarios in real time. The surrogate models were prepared by training artificial neural networks on datasets of urban form and monthly energy consumption values of all zip codes in San Diego county. The surrogate models were then used as the simulation engine of a generative urban design tool, which generates hypothetical communities in San Diego following the county's existing urban typologies and then estimates the monthly energy consumption value of each generated design option. This paper and developed software prototype is part of a larger research project that evaluates the energy performance of community microgrids via their urban spatial configurations. This prototype takes the first step in introducing a new set of tools for architects and urban designers with the goal of engaging them in the development process of community microgrids.
{"title":"A real-time predictive software prototype for simulating urban-scale energy consumption based on surrogate models","authors":"M. Rahimian, J. Duarte, L. Iulo","doi":"10.1017/S0890060421000184","DOIUrl":"https://doi.org/10.1017/S0890060421000184","url":null,"abstract":"Abstract This paper discusses the development of an experimental software prototype that uses surrogate models for predicting the monthly energy consumption of urban-scale community design scenarios in real time. The surrogate models were prepared by training artificial neural networks on datasets of urban form and monthly energy consumption values of all zip codes in San Diego county. The surrogate models were then used as the simulation engine of a generative urban design tool, which generates hypothetical communities in San Diego following the county's existing urban typologies and then estimates the monthly energy consumption value of each generated design option. This paper and developed software prototype is part of a larger research project that evaluates the energy performance of community microgrids via their urban spatial configurations. This prototype takes the first step in introducing a new set of tools for architects and urban designers with the goal of engaging them in the development process of community microgrids.","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":"35 1","pages":"353 - 368"},"PeriodicalIF":2.1,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42222224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract This paper aims at automatically generating dimensioned floorplans while considering constraints given by the users in the form of adjacency and connectivity graph. The obtained floorplans also satisfy boundary constraints where users will be asked to choose their preferred location based on cardinal and inter-cardinal directions. Further, spanning circulations are inserted within the generated floorplans. The larger aim of this research is to provide alternative architecturally feasible layouts to users which can be further refined by architects.
{"title":"Graph-based approach for enumerating floorplans based on users specifications","authors":"Krishnendra Shekhawat, Rahil N. Jain, Sumit Bisht, Aishwarya Kondaveeti, Dipam Goswami","doi":"10.1017/S0890060421000275","DOIUrl":"https://doi.org/10.1017/S0890060421000275","url":null,"abstract":"Abstract This paper aims at automatically generating dimensioned floorplans while considering constraints given by the users in the form of adjacency and connectivity graph. The obtained floorplans also satisfy boundary constraints where users will be asked to choose their preferred location based on cardinal and inter-cardinal directions. Further, spanning circulations are inserted within the generated floorplans. The larger aim of this research is to provide alternative architecturally feasible layouts to users which can be further refined by architects.","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":"35 1","pages":"438 - 459"},"PeriodicalIF":2.1,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47478512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-01DOI: 10.1017/S0890060421000214
A. Bhatt, A. Majumder, A. Chakrabarti
Abstract Literature suggests that people typically understand knowledge by induction and produce knowledge by synthesis. This paper revisits the various modes of reasoning – explanatory abduction, innovative abduction, deduction, and induction – that have been proposed by earlier researchers as crucial modes of reasoning underlying the design process. First, our paper expands earlier work on abductive reasoning – an essential mode of reasoning involved in the process of synthesis – by understanding its role with the help of the “SAPPhIRE” model of causality. The explanations of abductive reasoning in design using the SAPPhIRE model have been compared with those using existing models. Second, the paper captures and analyzes various modes of reasoning during design synthesis with the help of the “Extended Integrated Model of Designing”. The analysis of participants' verbal speech and outcomes shows the model's ability to explain the various modes of reasoning that occur in design. The results indicate the above models to provide a more extensive account of reasoning in design synthesis. Earlier empirical validation of both the models lends further support to the claim of their explanatory capacity.
{"title":"Analyzing the modes of reasoning in design using the SAPPhIRE model of causality and the Extended Integrated Model of Designing","authors":"A. Bhatt, A. Majumder, A. Chakrabarti","doi":"10.1017/S0890060421000214","DOIUrl":"https://doi.org/10.1017/S0890060421000214","url":null,"abstract":"Abstract Literature suggests that people typically understand knowledge by induction and produce knowledge by synthesis. This paper revisits the various modes of reasoning – explanatory abduction, innovative abduction, deduction, and induction – that have been proposed by earlier researchers as crucial modes of reasoning underlying the design process. First, our paper expands earlier work on abductive reasoning – an essential mode of reasoning involved in the process of synthesis – by understanding its role with the help of the “SAPPhIRE” model of causality. The explanations of abductive reasoning in design using the SAPPhIRE model have been compared with those using existing models. Second, the paper captures and analyzes various modes of reasoning during design synthesis with the help of the “Extended Integrated Model of Designing”. The analysis of participants' verbal speech and outcomes shows the model's ability to explain the various modes of reasoning that occur in design. The results indicate the above models to provide a more extensive account of reasoning in design synthesis. Earlier empirical validation of both the models lends further support to the claim of their explanatory capacity.","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":"35 1","pages":"384 - 403"},"PeriodicalIF":2.1,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48882581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-01DOI: 10.1017/S0890060421000251
Kinda Al Sayed, P. Cheng, A. Penn
Abstract This paper presents a preliminary study into the spatial features that can be used to distinguish creativity andefficiency in design layouts, and the distinct pattern of cognitive and metacognitive activity that is associated with creative design. In a design experiment, a group of 12 architects were handed a design brief. Their drawing activity was recorded and they were required to externalize their thoughts during the design process. Both design solutions and verbal comments were analysed and modelled. A separate group of experienced architects used their expert knowledge to assign creativity and efficiency scores to the 12 design solutions. The design solutions were evaluated spatially. Protocol analysis studies including linkography and macroscopic analysis were used to discern distinctive patterns in the cognitive and metacognition activity of designs marked with the highest and least creativity scores. Entropy models of the linkographs and knowledge graphs were further introduced Finally, we assessed how creativity and efficiency correlates to experiment variables, cognitive activity, metacognitive activity, spatial and functional distribution of spaces in the design solutions, and the number and type of design constraints applied through the course of design. Through this investigation, we suggest that expert knowledge can be used to assess creativity and efficiency in designs. Our findings indicate that efficient layouts have distinct spatial features, and that cognitive and metacognitive activity in design that yields a highly creative outcome corresponds to higher frequencies of design moves and higher linkages between design moves.
{"title":"An investigation into the cognitive, metacognitive, and spatial markers of creativity and efficiency in architectural design","authors":"Kinda Al Sayed, P. Cheng, A. Penn","doi":"10.1017/S0890060421000251","DOIUrl":"https://doi.org/10.1017/S0890060421000251","url":null,"abstract":"Abstract This paper presents a preliminary study into the spatial features that can be used to distinguish creativity andefficiency in design layouts, and the distinct pattern of cognitive and metacognitive activity that is associated with creative design. In a design experiment, a group of 12 architects were handed a design brief. Their drawing activity was recorded and they were required to externalize their thoughts during the design process. Both design solutions and verbal comments were analysed and modelled. A separate group of experienced architects used their expert knowledge to assign creativity and efficiency scores to the 12 design solutions. The design solutions were evaluated spatially. Protocol analysis studies including linkography and macroscopic analysis were used to discern distinctive patterns in the cognitive and metacognition activity of designs marked with the highest and least creativity scores. Entropy models of the linkographs and knowledge graphs were further introduced Finally, we assessed how creativity and efficiency correlates to experiment variables, cognitive activity, metacognitive activity, spatial and functional distribution of spaces in the design solutions, and the number and type of design constraints applied through the course of design. Through this investigation, we suggest that expert knowledge can be used to assess creativity and efficiency in designs. Our findings indicate that efficient layouts have distinct spatial features, and that cognitive and metacognitive activity in design that yields a highly creative outcome corresponds to higher frequencies of design moves and higher linkages between design moves.","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":"35 1","pages":"423 - 437"},"PeriodicalIF":2.1,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43352606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-01DOI: 10.1017/S089006042100024X
Hao Ji, Yan Jin
Abstract Self-organizing systems (SOS) are developed to perform complex tasks in unforeseen situations with adaptability. Predefining rules for self-organizing agents can be challenging, especially in tasks with high complexity and changing environments. Our previous work has introduced a multiagent reinforcement learning (RL) model as a design approach to solving the rule generation problem of SOS. A deep multiagent RL algorithm was devised to train agents to acquire the task and self-organizing knowledge. However, the simulation was based on one specific task environment. Sensitivity of SOS to reward functions and systematic evaluation of SOS designed with multiagent RL remain an issue. In this paper, we introduced a rotation reward function to regulate agent behaviors during training and tested different weights of such reward on SOS performance in two case studies: box-pushing and T-shape assembly. Additionally, we proposed three metrics to evaluate the SOS: learning stability, quality of learned knowledge, and scalability. Results show that depending on the type of tasks; designers may choose appropriate weights of rotation reward to obtain the full potential of agents’ learning capability. Good learning stability and quality of knowledge can be achieved with an optimal range of team sizes. Scaling up to larger team sizes has better performance than scaling downwards.
{"title":"Evaluating the learning and performance characteristics of self-organizing systems with different task features","authors":"Hao Ji, Yan Jin","doi":"10.1017/S089006042100024X","DOIUrl":"https://doi.org/10.1017/S089006042100024X","url":null,"abstract":"Abstract Self-organizing systems (SOS) are developed to perform complex tasks in unforeseen situations with adaptability. Predefining rules for self-organizing agents can be challenging, especially in tasks with high complexity and changing environments. Our previous work has introduced a multiagent reinforcement learning (RL) model as a design approach to solving the rule generation problem of SOS. A deep multiagent RL algorithm was devised to train agents to acquire the task and self-organizing knowledge. However, the simulation was based on one specific task environment. Sensitivity of SOS to reward functions and systematic evaluation of SOS designed with multiagent RL remain an issue. In this paper, we introduced a rotation reward function to regulate agent behaviors during training and tested different weights of such reward on SOS performance in two case studies: box-pushing and T-shape assembly. Additionally, we proposed three metrics to evaluate the SOS: learning stability, quality of learned knowledge, and scalability. Results show that depending on the type of tasks; designers may choose appropriate weights of rotation reward to obtain the full potential of agents’ learning capability. Good learning stability and quality of knowledge can be achieved with an optimal range of team sizes. Scaling up to larger team sizes has better performance than scaling downwards.","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":"35 1","pages":"404 - 422"},"PeriodicalIF":2.1,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43571068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-01DOI: 10.1017/S0890060421000196
Mohammad Alsager Alzayed, Scarlett R. Miller, Christopher McComb
Abstract Over the past decade, engineering design research has seen a significant surge of the discussion of empathy. As such, design researchers have been devoted in devising and assessing empathic design activities. While prior research has examined the utility of empathic design experiences on driving creative concept generation, little is known about the role of a designer's empathic tendencies in driving creative concept generation and selection in an engineering design project. Without this knowledge, we cannot be sure if, when, or how empathy influences the design process. Thus, the main goal of this paper was to identify the role of trait empathy in creative concept generation and selection in an engineering design student project. In order to achieve this objective, a study was conducted with 103 first-year engineering students during two design stages of an 8-week design project (concept generation and concept selection). The main findings from this paper highlighted that empathic concern tendencies positively impacted the generation of more ideas while personal distress tendencies negatively impacted the generation of more ideas. During concept selection, perspective-taking tendencies positively impacted participants’ propensity for selecting elegant ideas. This research took the first step in encouraging empirical investigations aimed at understanding the role of trait empathy across different stages of the design process.
{"title":"Empathic creativity: can trait empathy predict creative concept generation and selection?","authors":"Mohammad Alsager Alzayed, Scarlett R. Miller, Christopher McComb","doi":"10.1017/S0890060421000196","DOIUrl":"https://doi.org/10.1017/S0890060421000196","url":null,"abstract":"Abstract Over the past decade, engineering design research has seen a significant surge of the discussion of empathy. As such, design researchers have been devoted in devising and assessing empathic design activities. While prior research has examined the utility of empathic design experiences on driving creative concept generation, little is known about the role of a designer's empathic tendencies in driving creative concept generation and selection in an engineering design project. Without this knowledge, we cannot be sure if, when, or how empathy influences the design process. Thus, the main goal of this paper was to identify the role of trait empathy in creative concept generation and selection in an engineering design student project. In order to achieve this objective, a study was conducted with 103 first-year engineering students during two design stages of an 8-week design project (concept generation and concept selection). The main findings from this paper highlighted that empathic concern tendencies positively impacted the generation of more ideas while personal distress tendencies negatively impacted the generation of more ideas. During concept selection, perspective-taking tendencies positively impacted participants’ propensity for selecting elegant ideas. This research took the first step in encouraging empirical investigations aimed at understanding the role of trait empathy across different stages of the design process.","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":"35 1","pages":"369 - 383"},"PeriodicalIF":2.1,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49393698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Prolonged sitting in a fixed or constrained position exposes aircraft passengers to long-term static loading of their bodies, which has deleterious effects on passengers’ comfort throughout the duration of the flight. The previous studies focused primarily on office and driving sitting postures and few studies, however, focused on the sitting postures of passengers in aircraft. Consequently, the aim of the present study is to detect and recognize the sitting postures of aircraft passengers in relation to sitting discomfort. A total of 24 subjects were recruited for the experiment, which lasted for 2 h. Furthermore, a total of 489 sitting postures were extracted and the pressure data between subjects and seat was collected from the experiment. After the detection of sitting postures, eight types of sitting postures were classified based on key parts (trunk, back, and legs) of the human bodies. Thereafter, the eight types of sitting postures were recognized with the aid of pressure data of seat pan and backrest employing several machine learning methods. The best classification rate of 89.26% was obtained from the support vector machine (SVM) with radial basis function (RBF) kernel. The detection and recognition of the eight types of sitting postures of aircraft passengers in this study provided an insight into aircraft passengers’ discomfort and seat design.
{"title":"Sitting posture detection and recognition of aircraft passengers using machine learning","authors":"Wenzhe Cun, Rong Mo, Jianjie Chu, Suihuai Yu, Huizhong Zhang, Hao Fan, Yanhao Chen, Mengcheng Wang, Hui Wang, Chen Chen","doi":"10.1017/S0890060421000135","DOIUrl":"https://doi.org/10.1017/S0890060421000135","url":null,"abstract":"Abstract Prolonged sitting in a fixed or constrained position exposes aircraft passengers to long-term static loading of their bodies, which has deleterious effects on passengers’ comfort throughout the duration of the flight. The previous studies focused primarily on office and driving sitting postures and few studies, however, focused on the sitting postures of passengers in aircraft. Consequently, the aim of the present study is to detect and recognize the sitting postures of aircraft passengers in relation to sitting discomfort. A total of 24 subjects were recruited for the experiment, which lasted for 2 h. Furthermore, a total of 489 sitting postures were extracted and the pressure data between subjects and seat was collected from the experiment. After the detection of sitting postures, eight types of sitting postures were classified based on key parts (trunk, back, and legs) of the human bodies. Thereafter, the eight types of sitting postures were recognized with the aid of pressure data of seat pan and backrest employing several machine learning methods. The best classification rate of 89.26% was obtained from the support vector machine (SVM) with radial basis function (RBF) kernel. The detection and recognition of the eight types of sitting postures of aircraft passengers in this study provided an insight into aircraft passengers’ discomfort and seat design.","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":"35 1","pages":"284 - 294"},"PeriodicalIF":2.1,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44857361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：