Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction最新文献_第3页

Research on the health evaluation index system of the street space in the old city 老城区街道空间健康评价指标体系研究

Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction Pub Date : 2023-03-22 DOI: 10.1680/jsmic.22.00023

Yu Chen, Kecheng Huo, Hui Tang, Jiangfan Tang

{"title":"Research on the health evaluation index system of the street space in the old city","authors":"Yu Chen, Kecheng Huo, Hui Tang, Jiangfan Tang","doi":"10.1680/jsmic.22.00023","DOIUrl":"https://doi.org/10.1680/jsmic.22.00023","url":null,"abstract":"Purpose. Healthy street space is an important goal in achieving public health, and street space in old areas is a design object to promote urban renewal. In 2021, China’s urbanization reached 65.22% according to Development and Reform Commission of PRC (2023). The rapid development of urbanization leads to urban problems and public health events that threaten residents’ life safety and physical health in old urban areas. Health issues are a major problem faced in urban renewal. Methodology. In this paper, an index system for evaluating the health of urban streets is established through empirical research. Through the preliminary study on the scope of data acquisition and establishing an index system. The authors identified and weighted eight main and 15 secondary indicators. By asking experts, residents, and tourists and using the information entropy-based weight allocation method, we determined the weight of each indicator. An empirical study of the Dongmenkou area in Yiyang City then followed this. Results. The study results show that the street space in the Dongmenkou district of Yiyang City has problems regarding inappropriate street width, imperfect street greening, poor street safety at night, and uneven distribution of facilities. Suggestions are made to improve the street scale, street environment, and facilities.","PeriodicalId":371248,"journal":{"name":"Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129735574","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}

引用次数: 0

Application of computer vision and BIM technology in optimal design of assembled buildings 计算机视觉与BIM技术在装配式建筑优化设计中的应用

Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction Pub Date : 2023-03-22 DOI: 10.1680/jsmic.22.00032

Ying Ding, Kezhi Song, Xingzong Liu, Rui Jiang, Hongxia Zhao, Hongxian Yuan, Xiubin Gong, Keyu Zhang

{"title":"Application of computer vision and BIM technology in optimal design of assembled buildings","authors":"Ying Ding, Kezhi Song, Xingzong Liu, Rui Jiang, Hongxia Zhao, Hongxian Yuan, Xiubin Gong, Keyu Zhang","doi":"10.1680/jsmic.22.00032","DOIUrl":"https://doi.org/10.1680/jsmic.22.00032","url":null,"abstract":"The shape data of prefabricated building components are closely related to their safety and reliability. To solve the problem of shape energy saving optimization, a radial basis function neural network (RBF) model based on particle swarm optimization (PSO) considering temperature compensation is studied and designed, and BIM (Building Information Modeling Technology) is introduced as an auxiliary technology for effective management of visual information, which finally realizes the energy saving calculation of building shape dimensions. The results show that the maximum expansion deformation measured by the proposed model appears in the 28th minute, the maximum expansion deformation is 0.11 mm, the error between the model and the actual value is only 0.0 2mm, and the difference between the monitoring time point is only 3 min. The total energy consumption of this model is 36.92 kWh/m2, 42.15 kWh/m2, and 33.58 kWh/m2 less than that of the PSO model in three types of buildings. In terms of the total contribution rate of energy conservation, the former is 0.76%, 0.88%, and 2.94% higher than the latter respectively. Therefore, this research has effectively improved monocular machine vision technology. At the same time, the energy-saving model of shape with temperature compensation for innovative design has also been effectively verified.","PeriodicalId":371248,"journal":{"name":"Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction","volume":"412 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124395212","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}

引用次数: 0

Dafni: a computational platform to support infrastructure systems research Dafni:一个支持基础设施系统研究的计算平台

Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction Pub Date : 2023-03-20 DOI: 10.1680/jsmic.22.00007

B. Matthews, J. Hall, M. Batty, S. Blainey, Nigel Cassidy, R. Choudhary, Daniel Coca, Stephen Hallett, J. Harou, Phil James, N. Lomax, Peter Oliver, A. Sivakumar, Theodoros Tryfonas, Liz Varga

{"title":"Dafni: a computational platform to support infrastructure systems research","authors":"B. Matthews, J. Hall, M. Batty, S. Blainey, Nigel Cassidy, R. Choudhary, Daniel Coca, Stephen Hallett, J. Harou, Phil James, N. Lomax, Peter Oliver, A. Sivakumar, Theodoros Tryfonas, Liz Varga","doi":"10.1680/jsmic.22.00007","DOIUrl":"https://doi.org/10.1680/jsmic.22.00007","url":null,"abstract":"Research into the engineering of infrastructure systems is increasingly data-intensive. Researchers build computational models to explore scenarios such as investigating the merits of infrastructure plans, analysing historical data to inform system operations, or assessing the impacts of infrastructure on the environment. Models are more complex, at higher resolution and with larger coverage. Researchers also require a ‘multi-systems’ approach to explore interactions between systems, such as energy and water with urban development, and across scales, from buildings and streets to regions or nations. Consequently, researchers need enhanced computational resources to support cross-institutional collaboration and sharing at scale. The Data and Analytics Facility for National Infrastructure (Dafni) is an emerging computational platform for infrastructure systems research. It provides high-throughput compute resources so larger data sets can be used, with a data repository to upload data and share it with collaborators. Users’ models can also be uploaded and executed using modern containerisation techniques, giving platform independence, scaling and sharing. Further, models can be combined into workflows, supporting multi-systems modelling, and generating visualisations to present results. Dafni forms a central resource accessible to all infrastructure systems researchers in the UK, supporting collaboration and providing a legacy, keeping data and models available beyond a project’s lifetime.","PeriodicalId":371248,"journal":{"name":"Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131653170","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}

引用次数: 1

Application of data mining algorithm in energy-saving renovation prediction of urban landscape buildings 数据挖掘算法在城市景观建筑节能改造预测中的应用

Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction Pub Date : 2023-03-20 DOI: 10.1680/jsmic.22.00030

J. Hu, X. Han

{"title":"Application of data mining algorithm in energy-saving renovation prediction of urban landscape buildings","authors":"J. Hu, X. Han","doi":"10.1680/jsmic.22.00030","DOIUrl":"https://doi.org/10.1680/jsmic.22.00030","url":null,"abstract":"In order to solve the problem of the heavy burden of electricity and energy consumption of urban landscape buildings, a prediction model for energy conservation and reconstruction of urban landscape buildings was established by combining data mining algorithms. Firstly, the energy demand and consumption of urban landscape buildings are analyzed, and the energy consumption of buildings is predicted by relevant mathematical calculation methods. Then, combined with data mining technology, effective information is extracted from the basic building information of urban landscape, daily energy consumption, operation data and other aspects. Finally, the prediction model of building energy conservation transformation based on data mining algorithm is constructed, and the Bayesian energy model is used for parameter correction. Test the performance of the model and find that under the single influence factor of different energy consumption, the change trend of total energy consumption is different. Among them, lighting power density factor has the greatest impact on energy consumption, and its annual energy consumption change rate can reach about 0.35. Applying the prediction model to the energy consumption prediction of 15 urban single buildings, it was found that the total energy consumption of the buildings before the transformation was much higher than the total energy consumption after the transformation, and the energy saving rate of the whole observation sample building group was as high as 18.5%, while the highest energy saving rate of the single buildings reached 30.1%. To sum up, the model has good prediction ability. Applying it to the energy conservation prediction of urban landscape buildings can better complete the energy prediction task and achieve the energy conservation goal.","PeriodicalId":371248,"journal":{"name":"Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115848099","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}

引用次数: 0

Using the characteristic search algorithm in a library fingerprint identification system 特征搜索算法在图书馆指纹识别系统中的应用

Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction Pub Date : 2023-03-17 DOI: 10.1680/jsmic.22.00024

Tuofu Peng

{"title":"Using the characteristic search algorithm in a library fingerprint identification system","authors":"Tuofu Peng","doi":"10.1680/jsmic.22.00024","DOIUrl":"https://doi.org/10.1680/jsmic.22.00024","url":null,"abstract":"As an important identification method, fingerprint recognition has a wide range of applications. To make the fingerprint recognition system of a library more efficient and secure, a recognition technology based on the characteristic search algorithm is proposed, and the performance of the algorithm is analysed. When a reasonable threshold is set, the matching error rate of the algorithm can be controlled at a lower level, and the algorithm can also ensure a higher fingerprint and determine the overall accuracy. At the same time, three other identification algorithms of the same type are introduced: radio frequency fingerprinting, convolutional neural network and local binary pattern. In a comparative experiment, it was found that the characteristic search algorithm model had the highest accuracy, with a value of 94.8%. When dealing with the same amount of fingerprint data, the recognition time of the algorithm model was the shortest. In addition, the area under the curve value corresponding to the receiver operating characteristic curve of the algorithm was the largest, and its value was 0.94. It is well known that the performance of the characteristic search algorithm is optimal and can effectively improve the operation efficiency of a library fingerprint identification system.","PeriodicalId":371248,"journal":{"name":"Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction","volume":"728 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115129216","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}

引用次数: 0

A novel improved model for green building energy consumption prediction based on time-series analysis 基于时间序列分析的绿色建筑能耗预测改进模型

Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction Pub Date : 2023-03-03 DOI: 10.1680/jsmic.22.00028

Shirui Xiao

{"title":"A novel improved model for green building energy consumption prediction based on time-series analysis","authors":"Shirui Xiao","doi":"10.1680/jsmic.22.00028","DOIUrl":"https://doi.org/10.1680/jsmic.22.00028","url":null,"abstract":"The development and popularization of renewable energy is necessary. The application of renewable energy technology in buildings is an important research direction. And the prediction of renewable energy consumption in this direction is an essential research content. In view of this, a buildings energy consumption prediction model of renewable energy based on time-series analysis and Support Vector Machine (SVM) is proposed. The performance test of this model shows that its loss value is as low as 1.5% in training set, and the loss value is 4.1% in test set. In addition, it shows the highest accuracy rate of 95.5% in the neural network accuracy test, which is significantly higher than the comparison of traditional algorithms. About the overall energy consumption prediction ability of the model, the experimental results showed that the lowest error of the energy consumption prediction model was 2.3%, the average relative error of the traditional SVM model in the same data set was 6.8%, and the chaotic time-series model was 4.1%. Compared with the traditional models currently used, the prediction ability of the energy consumption prediction model had been greatly improved, and it had the potential to be put into practical application.","PeriodicalId":371248,"journal":{"name":"Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132781548","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}

引用次数: 0

Construction cost prediction based on adaptive boosting and artificial neural networks 基于自适应boosting和人工神经网络的工程造价预测

Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction Pub Date : 2023-02-01 DOI: 10.1680/jsmic.22.00027

Wenhui Feng, Yafeng Zou

{"title":"Construction cost prediction based on adaptive boosting and artificial neural networks","authors":"Wenhui Feng, Yafeng Zou","doi":"10.1680/jsmic.22.00027","DOIUrl":"https://doi.org/10.1680/jsmic.22.00027","url":null,"abstract":"The artificial bee colony algorithm and multilayer error back propagation neural networks commonly used in construction project cost forecasting suffer from slow training speed and high cost. A combination of the beetle antennae search, support vector machine, adaptive boosting and genetic algorithms was proposed to solve these problems. Support vector machine optimisation was accomplished using the beetle antennae search algorithm. The enhanced genetic algorithm was then used directly to swap out the fit solutions for the unfit ones. One hundred projects completed during the last three years were chosen from a network integration database to serve as the training data set after developing the prediction model. Using actual cost information and trial and error, appropriate parameters were chosen and combinations of algorithms were selected for comparison. The maximum relative error of the improved method was 9.01%, which was 34.68% lower than the baseline method, while the smallest relative error was 0.59%, which was 1.58% lower than the baseline method. The study’s innovation lay in the addition of the beetle antennae search algorithm and enhancement of the genetic algorithm. The former significantly increased the network’s search efficiency, while the latter increased population fitness generally and mitigated the drawback of the genetic algorithm, which was prone to local convergence.","PeriodicalId":371248,"journal":{"name":"Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127229753","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}

引用次数: 0

Fabrication progress detection for concrete T-girder based on improved YOLOv4 基于改进YOLOv4的混凝土t型梁制作进度检测

Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction Pub Date : 2023-01-26 DOI: 10.1680/jsmic.22.00020

Dong Liang, Liu Yang, Chuankui Ma, Yang Yu

{"title":"Fabrication progress detection for concrete T-girder based on improved YOLOv4","authors":"Dong Liang, Liu Yang, Chuankui Ma, Yang Yu","doi":"10.1680/jsmic.22.00020","DOIUrl":"https://doi.org/10.1680/jsmic.22.00020","url":null,"abstract":"Large precast concrete girder plants have many processes, long cycles, and a large amount of data. This study proposes an improved YOLOv4 object detection algorithm with a spatio-temporal relationship to detect each fabrication process of precast concrete girders. It realises the fabrication information’s digitisation of traditional precast concrete girder plants. Initially, adding upsampling and convolution layers to the YOLOv4 base model enhances the algorithm’s feature extraction ability at different fabrication stages of precast concrete girders. Adopting the spatio-temporal relationship to determine the fabrication progress of precast concrete girders with identical features but at various fabrication stages. Finally, this research conducts an application analysis in an actual precast concrete girder plant. The analysis result indicated that the improved YOLOv4 algorithm significantly raises mAP and Average IOU in recognition. In addition, the spatio-temporal relationship effectively solves error detection problems caused by the similar appearance at different fabrication stages. This method provides practical support for digitising the fabrication data of traditional precast girder plants.","PeriodicalId":371248,"journal":{"name":"Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction","volume":"162 12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129137234","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}

引用次数: 0

3D deep learning enhanced void-growing approach in creating geometric digital twins of buildings 3D深度学习增强了创建几何数字建筑双胞胎的空隙增长方法

Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction Pub Date : 2022-12-14 DOI: 10.1680/jsmic.21.00035

Yuandong Pan, A. Braun, A. Borrmann, I. Brilakis

{"title":"3D deep learning enhanced void-growing approach in creating geometric digital twins of buildings","authors":"Yuandong Pan, A. Braun, A. Borrmann, I. Brilakis","doi":"10.1680/jsmic.21.00035","DOIUrl":"https://doi.org/10.1680/jsmic.21.00035","url":null,"abstract":"The challenge this paper addresses is how to automatically generate geometric digital twins of the indoor environment of buildings. Unlike most previous research that starts with detecting planes in the point cloud and only considers the geometric information, the proposed ”void-growing” approach is a full-automatic approach that starts with detecting void space inside rooms, considering geometric information, as well as semantic information predicted from deep learning. Then based on the detected room spaces, structural elements, as well as doors and windows, are extracted. The method can work in (1) rooms with complex structures like U-shape and L-shape, (2) rooms with different ceiling heights, and (3) rooms under a high occlusion level. Compared with previous studies that mainly only use geometric information, the approach also focuses on how to select useful information predicted by deep learning. This study used existing state-of-the-art deep learning architecture for the segmentation task in the proposed approach. By taking useful semantic information into consideration, the proposed approach performs better in creating geometric digital twins of buildings.","PeriodicalId":371248,"journal":{"name":"Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129141137","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}

引用次数: 2

Digital simulation modelling for a school re-opening during the Covid-19 pandemic 2019冠状病毒病大流行期间学校重新开放的数字模拟建模

Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction Pub Date : 2022-10-19 DOI: 10.1680/jsmic.21.00026

S. Comai, D. Simeone, S. M. Ventura, A. Ciribini

{"title":"Digital simulation modelling for a school re-opening during the Covid-19 pandemic","authors":"S. Comai, D. Simeone, S. M. Ventura, A. Ciribini","doi":"10.1680/jsmic.21.00026","DOIUrl":"https://doi.org/10.1680/jsmic.21.00026","url":null,"abstract":"The Covid-19 pandemic influenced the way buildings are used and experienced. In particular, educational facilities were among the most affected by the pandemic in terms of use processes. This paper presents a methodology developed to re-organise spaces in a school building, a real case study, to allow safe re-opening. Social distancing and availability of learning spaces were taken into account to simulate the use of the educational facility according to the emergency protocols. Based on a digital survey of the existing building, a building information model was generated and used as a basis for the spatial analysis, crowd and agent-based simulations. Additionally, interactive games and training videos were developed as communication tools to inform end-users about the new rules to be respected inside the building. The digital approach adopted for the analysis of use processes as well as for communicating the results to the end-users allowed them to experience the school fruition processes within a virtual environment before the school re-opening. Future works could deal with the application of the same methodology in other schools, as well as in different contexts, going beyond the specificity of the pandemic emergency, and for other types of buildings.","PeriodicalId":371248,"journal":{"name":"Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125751263","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}

引用次数: 0