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Construction Cost Optimization of Prefabricated Buildings Based on BIM Technology 基于 BIM 技术的预制建筑施工成本优化
IF 1.1
Information Resources Management Journal Pub Date : 2024-07-18 DOI: 10.4018/irmj.348335
Liwei Fang, Masahiro Arakawa
{"title":"Construction Cost Optimization of Prefabricated Buildings Based on BIM Technology","authors":"Liwei Fang, Masahiro Arakawa","doi":"10.4018/irmj.348335","DOIUrl":"https://doi.org/10.4018/irmj.348335","url":null,"abstract":"Prefabricated assemblies are popular in the construction industry due to their minimal carbon footprint, enhanced safety, and reliability. A combination of software, including Revit, Navisworks, and Practical Structural Design and Construction (PKPM) software, is used to reduce costs by refining the specifications and dimensions of components, streamlining the variety of molds, enhancing design performance through rigorous component collision inspections and structural optimization, and ensuring cost-effectiveness. The integration of building integration modeling (BIM) visualization significantly diminishes errors attributable to information asymmetry and minimized material wastage stemming from production inaccuracies. The implementation of a Radio Frequency Identification (RFID) information exchange platform enables real-time component tracking, provides insights into the transportation dynamics of these components, and facilitates cost optimization during the transportation phase. Moreover, simulations conducted using Fuzor software preemptively identify potential construction site issues. There is a substantial cost savings of 710,000 yuan.","PeriodicalId":44735,"journal":{"name":"Information Resources Management Journal","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141826405","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 Comment Aspect-Level User Preference Transfer Model for Cross-Domain Recommendations 用于跨域推荐的评论式方面级用户偏好转移模型
IF 1.1
Information Resources Management Journal Pub Date : 2024-07-11 DOI: 10.4018/irmj.345360
Wumei Zhang, Jianping Zhang, Yongzhen Zhang
{"title":"A Comment Aspect-Level User Preference Transfer Model for Cross-Domain Recommendations","authors":"Wumei Zhang, Jianping Zhang, Yongzhen Zhang","doi":"10.4018/irmj.345360","DOIUrl":"https://doi.org/10.4018/irmj.345360","url":null,"abstract":"Traditional cross-domain recommendation models make it difficult to deeply mine users' aspect-level preferences from comment information due to existing problems such as polysemy of comment text, sparse comment data, and user cold start. A Cross-Domain Recommender (CDR) model that integrates comment knowledge enhancement and aspect-level user preference transfer (C-KE-AUT) was proposed to address the above issues. Firstly, an aspect-level user preference extraction model was constructed by combining the RoBERTa word embedding model, high-level feature representation based on Transformer, and aspect-level attention-learning methods. Then, a user aspect-level preference cross-domain transfer model was constructed based on a two-stage generative adversarial network that can transfer the aspect-level interest preferences of users in the source domain to the target domain with sparse data. The experimental results on the Amazon 2018 comment dataset indicated that the recommendation performance of the proposed C-KE-AUT model was significantly superior to other advanced comparative models.","PeriodicalId":44735,"journal":{"name":"Information Resources Management Journal","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141655797","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
Risk Prediction of the Development of the Digital Economy Industry Based on a Machine Learning Model in the Context of Rural Revitalization 乡村振兴背景下基于机器学习模型的数字经济产业发展风险预测
IF 1.4
Information Resources Management Journal Pub Date : 2024-05-06 DOI: 10.4018/irmj.343095
Rui Luan, Ping Xu
{"title":"Risk Prediction of the Development of the Digital Economy Industry Based on a Machine Learning Model in the Context of Rural Revitalization","authors":"Rui Luan, Ping Xu","doi":"10.4018/irmj.343095","DOIUrl":"https://doi.org/10.4018/irmj.343095","url":null,"abstract":"In today's society, rural areas face challenges such as complex terrain and uneven population distribution, and infrastructure construction is exceptionally difficult. At the same time, poor information transmission and low communication efficiency have also become a major obstacle to the promotion of the digital economy in rural areas. This study aims to use gradient advancement models to identify potential risks in the growth of the digital economy sector related to rural revitalization. In this study, we used an enhanced hierarchical gradient boosting algorithm. The research results indicate that the introduction of this technology can provide us with a more comprehensive and reliable risk prediction model, thereby more scientifically assisting the development and decision-making of the digital economy in rural areas. This article provides a new perspective and solutions for development issues in rural areas, promoting sustainable development and economic growth in rural areas.","PeriodicalId":44735,"journal":{"name":"Information Resources Management Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141011361","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
An Exploration of the Computer Big Data Mining Service Model Under Resource Sharing 资源共享下的计算机大数据挖掘服务模式探索
IF 1.4
Information Resources Management Journal Pub Date : 2024-03-12 DOI: 10.4018/irmj.340032
WeiWei Hu, Lina Sun, Lijie Li
{"title":"An Exploration of the Computer Big Data Mining Service Model Under Resource Sharing","authors":"WeiWei Hu, Lina Sun, Lijie Li","doi":"10.4018/irmj.340032","DOIUrl":"https://doi.org/10.4018/irmj.340032","url":null,"abstract":"In order to meet the diverse needs of users for data mining services and improve resource utilization and enterprise competitiveness, this article aims to construct a Big Data Analytics (BDA) data mining service model based on resource sharing mechanisms. This article designs a customized data mining service model for BDA based on its characteristics. In this model, the authors apply the improved Apriori algorithm to determine the optimization plan and improve the ant colony optimization algorithm to improve the efficiency and accuracy of data mining. By analyzing the experimental results, the scientificity and rationality of the proposed data mining service model for BDA were demonstrated, and the implementation strategy of the data mining model was improved. These research findings provide important references for BDA's data mining service model based on response surface modeling and also provide guidance for enterprises on how to better utilize resources and improve competitiveness when facing big data.","PeriodicalId":44735,"journal":{"name":"Information Resources Management Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140250327","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
Improving Supply Chain Human-Machine Systems by the Analysis of Departmental-Level User Characteristics 通过分析部门级用户特征改进供应链人机系统
IF 1.4
Information Resources Management Journal Pub Date : 2024-02-07 DOI: 10.4018/irmj.337387
Kun Yang
{"title":"Improving Supply Chain Human-Machine Systems by the Analysis of Departmental-Level User Characteristics","authors":"Kun Yang","doi":"10.4018/irmj.337387","DOIUrl":"https://doi.org/10.4018/irmj.337387","url":null,"abstract":"In this study, the authors aimed to enhance supply chain efficiency at Company A, a key player in China's manufacturing sector, by focusing on user-centric system design. The approach involved analyzing departmental-level user characteristics to inform the development of an optimized human-machine interface. To achieve this, they conducted focus groups and card sorting exercises, identifying specific user needs across departments like planning, purchasing, receiving, warehousing, production, and quality control. The application of these insights into system design led to significant operational improvements. Notably, time savings were observed in departments such as receiving (74.44%) and quality control (41.86%). This tailored approach underscored the importance of understanding and integrating user characteristics in supply chain systems, leading to enhanced productivity and a competitive edge. The study demonstrates the impact of user-focused system design in improving both efficiency and user satisfaction in supply chain management.","PeriodicalId":44735,"journal":{"name":"Information Resources Management Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139797828","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
Improving Supply Chain Human-Machine Systems by the Analysis of Departmental-Level User Characteristics 通过分析部门级用户特征改进供应链人机系统
IF 1.4
Information Resources Management Journal Pub Date : 2024-02-07 DOI: 10.4018/irmj.337387
Kun Yang
{"title":"Improving Supply Chain Human-Machine Systems by the Analysis of Departmental-Level User Characteristics","authors":"Kun Yang","doi":"10.4018/irmj.337387","DOIUrl":"https://doi.org/10.4018/irmj.337387","url":null,"abstract":"In this study, the authors aimed to enhance supply chain efficiency at Company A, a key player in China's manufacturing sector, by focusing on user-centric system design. The approach involved analyzing departmental-level user characteristics to inform the development of an optimized human-machine interface. To achieve this, they conducted focus groups and card sorting exercises, identifying specific user needs across departments like planning, purchasing, receiving, warehousing, production, and quality control. The application of these insights into system design led to significant operational improvements. Notably, time savings were observed in departments such as receiving (74.44%) and quality control (41.86%). This tailored approach underscored the importance of understanding and integrating user characteristics in supply chain systems, leading to enhanced productivity and a competitive edge. The study demonstrates the impact of user-focused system design in improving both efficiency and user satisfaction in supply chain management.","PeriodicalId":44735,"journal":{"name":"Information Resources Management Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139857876","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
The Control of Civil Engineering Projects Based on Deep Learning and Building Information Modeling 基于深度学习和建筑信息建模的土木工程项目控制
IF 1.4
Information Resources Management Journal Pub Date : 2023-08-29 DOI: 10.4018/irmj.329250
Fang Wang, Liangqiong Chen
{"title":"The Control of Civil Engineering Projects Based on Deep Learning and Building Information Modeling","authors":"Fang Wang, Liangqiong Chen","doi":"10.4018/irmj.329250","DOIUrl":"https://doi.org/10.4018/irmj.329250","url":null,"abstract":"The aim of this study is to enhance the quality of civil engineering project management and optimize project control in order to ensure adequate construction resources and facilitate seamless project progression. By integrating building information modeling (BIM) technology with deep learning techniques, optimal control was examined at various stages of civil engineering project management. A simulation test was performed on a selected gymnasium engineering project, focusing on cost and resource control aspects. The findings revealed that, as the project advanced, the planned cost exceeded the actual cost by nearly 100,000 yuan in the final stage. The combination of BIM technology and deep learning model prediction substantially reduced the cost and material budgets of the engineering project. Data analysis showed that the average positioning error of the convolutional neural network algorithm for the project model was below 2%.","PeriodicalId":44735,"journal":{"name":"Information Resources Management Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45161035","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
Rational or Emotional User 理性或感性用户
IF 1.4
Information Resources Management Journal Pub Date : 2023-07-10 DOI: 10.4018/irmj.325241
Edgardo Bravo, Jhony Ostos
{"title":"Rational or Emotional User","authors":"Edgardo Bravo, Jhony Ostos","doi":"10.4018/irmj.325241","DOIUrl":"https://doi.org/10.4018/irmj.325241","url":null,"abstract":"Understanding why users continue or discontinue using specific technology is vital for its providers. Existing literature has explored the reasons for continuance and discontinuance by taking into account both rational and emotional factors. However, one question remains unanswered: Why do some users depend more on rational factors for decision-making, while others rely more on emotional factors? This study addresses this question by integrating cognitive decision-making styles and the fear of making incorrect decisions into traditional continued usage models. Data were gathered from 285 TV users and analyzed using structural equation modeling. The results demonstrate that the introduced constructs moderated the direct effects of rational and emotional factors. The contribution of the study lies in incorporating the underlying cognitive processes of decision-making. It presents two actionable variables that managers can utilize to categorize their customers and enhance the effectiveness of their use continuance strategies.","PeriodicalId":44735,"journal":{"name":"Information Resources Management Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43540190","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
Risk and Revenue Management in the Chinese Auto Loan Industry 中国汽车贷款行业的风险与收益管理
IF 1.4
Information Resources Management Journal Pub Date : 2023-05-18 DOI: 10.4018/irmj.323438
Jianping Peng, Wanli Liu, Zhenheng Huang, Dongmei Xu, Qinglei Cai, J. Quan
{"title":"Risk and Revenue Management in the Chinese Auto Loan Industry","authors":"Jianping Peng, Wanli Liu, Zhenheng Huang, Dongmei Xu, Qinglei Cai, J. Quan","doi":"10.4018/irmj.323438","DOIUrl":"https://doi.org/10.4018/irmj.323438","url":null,"abstract":"The automobile consumption credit business promotes the development of the automobile industry. However, the current credit system in China requires further refinement. Thus, the credit loan business is associated with certain risks, and company profits are often negatively impacted by clients who default on loans. Based on the data, this article leverages the economic and financial theories of consumer credit risk control to construct a logistic model to predict customers' default probability. Then, a quadratic regression model is established to determine the optimal commission structure to balance profitability with incentives from retail stores. Results show that the macro-level variables are negatively associated with the probability of good behavior. The personal level variables exhibit a positive association. In addition, a negative coefficient in the quadratic profit equation indicates the presence of an inverted “U” relationship between profit and commission. Corresponding suggestions are put forward.","PeriodicalId":44735,"journal":{"name":"Information Resources Management Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43800275","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
Deep Learning Techniques for Demand Forecasting: Review and Future Research Opportunities 需求预测的深度学习技术:回顾和未来的研究机会
IF 1.4
Information Resources Management Journal Pub Date : 2022-01-01 DOI: 10.4018/IRMJ.291692
N. ArunkumarO., D. Divya
{"title":"Deep Learning Techniques for Demand Forecasting: Review and Future Research Opportunities","authors":"N. ArunkumarO., D. Divya","doi":"10.4018/IRMJ.291692","DOIUrl":"https://doi.org/10.4018/IRMJ.291692","url":null,"abstract":"","PeriodicalId":44735,"journal":{"name":"Information Resources Management Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70478278","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
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