As a key link in engineering construction, reasonable evaluation of engineering cost can effectively control the budget and save costs. Therefore, the reliability of the engineering cost estimation will directly affect the economic status of the whole project. However, traditional prediction models are based on a single machine learning method, which is not generalized enough and has low accuracy. In view of this, a mathematical model for engineering cost prediction is constructed by combining a random forest algorithm, ridge regression algorithm, and extreme gradient boosting (XG Boost) algorithm to obtain a prediction model with higher generalization and accuracy, and to evaluate the cost of engineering projects reasonably and scientifically. The average relative error between predicted and actual values was only 0.872%. The root mean square error and average percentage error of the fusion model were relatively small. The superiority of the proposed mathematical model of prediction cost is verified, and the model possesses a certain application value in construction engineering, providing practical reference and guidance for engineering cost prediction.
{"title":"Construction of mathematical model for integration of engineering cost prediction and multiple algorithms","authors":"Rufang Zhang","doi":"10.1680/jsmic.23.00061","DOIUrl":"https://doi.org/10.1680/jsmic.23.00061","url":null,"abstract":"As a key link in engineering construction, reasonable evaluation of engineering cost can effectively control the budget and save costs. Therefore, the reliability of the engineering cost estimation will directly affect the economic status of the whole project. However, traditional prediction models are based on a single machine learning method, which is not generalized enough and has low accuracy. In view of this, a mathematical model for engineering cost prediction is constructed by combining a random forest algorithm, ridge regression algorithm, and extreme gradient boosting (XG Boost) algorithm to obtain a prediction model with higher generalization and accuracy, and to evaluate the cost of engineering projects reasonably and scientifically. The average relative error between predicted and actual values was only 0.872%. The root mean square error and average percentage error of the fusion model were relatively small. The superiority of the proposed mathematical model of prediction cost is verified, and the model possesses a certain application value in construction engineering, providing practical reference and guidance for engineering cost prediction.","PeriodicalId":510830,"journal":{"name":"Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction","volume":"120 34","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140378683","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}
With the continuous improvement of living standards, people go outdoors and spend more and more time in scenic spots. The landscape architecture design that serves people in urban scenic spots attracts more and more public attention, which puts forward higher requirements for landscape architecture design that serves people in scenic spots. How to better integrate the design of all kinds of landscape architecture into nature, so as to better serve the public, is an urgent problem to be solved at this stage. This paper selects the evaluation indexes of urban architectural landscape, uses analytic hierarchy process to determine the weights of each index, and quantifies 6 evaluation indexes to build the evaluation model of architectural landscape design. In terms of the improvement of You Only Look Once version 4 (YOLOv4) model, MobileNetV3 was selected as the backbone feature extraction network, and the convolution in the feature enhancement extraction network was replaced by the depth separable volume, and an architectural landscape recognition system based on the improved YOLOv4 model was constructed. In terms of algorithm performance verification, the improved algorithm was compared with Single Shot Detector (SSD), MobileNetV3, ShuffleNetV2, YOLOv3, YOLOv4 and YOLOv5s algorithms under multiple evaluation indexes. The experimental results show that the size of the model is 51.4 MB, which does not cause a large burden. The Mean Average Precision (mAP) value of the improved YOLOv4 algorithm is 93.5%, and the Frames Per Second (FPS) is 30 frame/s, which has higher recognition accuracy and detection speed, and has obvious advantages.
{"title":"Constructing and applying neural network-based architectural landscape evaluation model","authors":"Weiwei Yang, Chunyan Yan, Yifan Wei","doi":"10.1680/jsmic.23.00085","DOIUrl":"https://doi.org/10.1680/jsmic.23.00085","url":null,"abstract":"With the continuous improvement of living standards, people go outdoors and spend more and more time in scenic spots. The landscape architecture design that serves people in urban scenic spots attracts more and more public attention, which puts forward higher requirements for landscape architecture design that serves people in scenic spots. How to better integrate the design of all kinds of landscape architecture into nature, so as to better serve the public, is an urgent problem to be solved at this stage. This paper selects the evaluation indexes of urban architectural landscape, uses analytic hierarchy process to determine the weights of each index, and quantifies 6 evaluation indexes to build the evaluation model of architectural landscape design. In terms of the improvement of You Only Look Once version 4 (YOLOv4) model, MobileNetV3 was selected as the backbone feature extraction network, and the convolution in the feature enhancement extraction network was replaced by the depth separable volume, and an architectural landscape recognition system based on the improved YOLOv4 model was constructed. In terms of algorithm performance verification, the improved algorithm was compared with Single Shot Detector (SSD), MobileNetV3, ShuffleNetV2, YOLOv3, YOLOv4 and YOLOv5s algorithms under multiple evaluation indexes. The experimental results show that the size of the model is 51.4 MB, which does not cause a large burden. The Mean Average Precision (mAP) value of the improved YOLOv4 algorithm is 93.5%, and the Frames Per Second (FPS) is 30 frame/s, which has higher recognition accuracy and detection speed, and has obvious advantages.","PeriodicalId":510830,"journal":{"name":"Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction","volume":"90 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140261225","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 : 2024-03-01DOI: 10.1680/jsmic.2024.177.1.57
{"title":"Smart Infrastructure and Construction: Referees 2023","authors":"","doi":"10.1680/jsmic.2024.177.1.57","DOIUrl":"https://doi.org/10.1680/jsmic.2024.177.1.57","url":null,"abstract":"","PeriodicalId":510830,"journal":{"name":"Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction","volume":"669 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140281115","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 : 2024-03-01DOI: 10.1680/jsmic.2024.177.1.1
José A F O Correia
{"title":"Editorial: Advanced technologies for smart buildings and infrastructure (Part 1)","authors":"José A F O Correia","doi":"10.1680/jsmic.2024.177.1.1","DOIUrl":"https://doi.org/10.1680/jsmic.2024.177.1.1","url":null,"abstract":"","PeriodicalId":510830,"journal":{"name":"Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction","volume":"88 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140274990","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}
The thermal comfort of rural residents is a major issue related to people’s livelihood. Due to differences in climatic factors (e.g. local climate) and non-climatic factors (such as building structures, economic and social-cultural levels, living habits, and availability of environmental control) between rural and urban areas, participants have different thermal requirements for a given thermal environment. This difference means that the thermal comfort standards that only consider urban conditions may not be applicable for rural situations. Therefore, a thermal comfort field study was conducted on the thermal comfort of traditional residential houses with natural ventilation located in rural areas of northern, Guizhou, China. This study aims to understand the indoor thermal conditions and perceptions of occupants in rural areas, with a total of 513 subjects participate into questionnaire survey. Most survey respondents accept the thermal environment in which they reside, even if the indoor temperature is not within the recommended thermal comfort range specified by international standards such as ASHRAE 55. Adaptive predict mean vote (aPMV) is established using the least square method. The adaptive coefficient λ representing the adaptive capacity is twice the recommended coefficient of the Chinese standard, GB/T50785-2012. This confirms that rural residents have a stronger adaptability to cold conditions in winter. The findings are benefit for improving thermal comfort and carbon emissions reduction for traditional residential houses in rural areas of northern Guizhou, China.
{"title":"Assessment of occupants’ adaptive capacity: a case study in naturally ventilated traditional residential houses","authors":"Jing Liu, Essah Emmanuel, Ting Cai","doi":"10.1680/jsmic.23.00075","DOIUrl":"https://doi.org/10.1680/jsmic.23.00075","url":null,"abstract":"The thermal comfort of rural residents is a major issue related to people’s livelihood. Due to differences in climatic factors (e.g. local climate) and non-climatic factors (such as building structures, economic and social-cultural levels, living habits, and availability of environmental control) between rural and urban areas, participants have different thermal requirements for a given thermal environment. This difference means that the thermal comfort standards that only consider urban conditions may not be applicable for rural situations. Therefore, a thermal comfort field study was conducted on the thermal comfort of traditional residential houses with natural ventilation located in rural areas of northern, Guizhou, China. This study aims to understand the indoor thermal conditions and perceptions of occupants in rural areas, with a total of 513 subjects participate into questionnaire survey. Most survey respondents accept the thermal environment in which they reside, even if the indoor temperature is not within the recommended thermal comfort range specified by international standards such as ASHRAE 55. Adaptive predict mean vote (aPMV) is established using the least square method. The adaptive coefficient λ representing the adaptive capacity is twice the recommended coefficient of the Chinese standard, GB/T50785-2012. This confirms that rural residents have a stronger adaptability to cold conditions in winter. The findings are benefit for improving thermal comfort and carbon emissions reduction for traditional residential houses in rural areas of northern Guizhou, China.","PeriodicalId":510830,"journal":{"name":"Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction","volume":"18 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140440873","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}
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