標題: Titlebook: Construction Analytics; Forecasting and Inve Mohsen Shahandashti,Bahram Abediniangerabi,Sooin K Textbook 2023 The Editor(s) (if applicable) [打印本頁] 作者: fasten 時間: 2025-3-21 16:42
書目名稱Construction Analytics影響因子(影響力)
書目名稱Construction Analytics影響因子(影響力)學科排名
書目名稱Construction Analytics網(wǎng)絡公開度
書目名稱Construction Analytics網(wǎng)絡公開度學科排名
書目名稱Construction Analytics被引頻次
書目名稱Construction Analytics被引頻次學科排名
書目名稱Construction Analytics年度引用
書目名稱Construction Analytics年度引用學科排名
書目名稱Construction Analytics讀者反饋
書目名稱Construction Analytics讀者反饋學科排名
作者: 個阿姨勾引你 時間: 2025-3-21 22:59 作者: Corporeal 時間: 2025-3-22 01:41
Mohsen Shahandashti,Bahram Abediniangerabi,Sooin KIllustrates theoretical explanations of construction analytics, hands-on practices, and R codes for analytics techniques.Enables readers to investigate the problems in the construction industry such a作者: 清楚說話 時間: 2025-3-22 07:11
http://image.papertrans.cn/c/image/236040.jpg作者: iodides 時間: 2025-3-22 11:30
https://doi.org/10.1007/978-3-322-88550-0nhance construction productivity, and reduce construction cost overruns. Although data analytics have tremendous potential to improve strategic decision-making in the construction industry as an ever-increasing volume of data becomes available, it has not been fully exploited on a larger scale in th作者: Hyaluronic-Acid 時間: 2025-3-22 15:51 作者: Hyaluronic-Acid 時間: 2025-3-22 18:30
https://doi.org/10.1007/978-3-322-88557-9struction time series data have not shown a constant variance. The volatility of a construction time series variable over time is challenging for accurate forecasting and risk management. This chapter discusses two time series volatility models (i.e., ARCH and GARCH) to forecast the variance of a co作者: Extricate 時間: 2025-3-22 22:22
https://doi.org/10.1007/978-3-322-88557-9ls. This chapter explains the process of identifying the leading indicators of a construction time series and developing proper multivariate models, such as vector error correction and vector autoregressive models for forecasting them. Several practical examples are provided along with R codes to sh作者: MIR 時間: 2025-3-23 02:17 作者: resuscitation 時間: 2025-3-23 06:11 作者: enmesh 時間: 2025-3-23 10:51 作者: Minuet 時間: 2025-3-23 17:05 作者: Polydipsia 時間: 2025-3-23 19:09 作者: abracadabra 時間: 2025-3-23 23:10
Charakteristika des Baumarktes,and compared with those of seasonal ARIMA and VEC models. The comparison results show that recurrent neural networks (i.e., long short-term memory and gated recurrent unit networks) can provide higher accuracies in forecasting the long-term variations of HCS than statistical linear time series models based on typical error measures.作者: paltry 時間: 2025-3-24 02:49
Construction Time Series Forecasting Using Multivariate Time Series Models,s compared with the results of the univariate seasonal ARIMA model. The comparison results show that the VEC model outperforms the seasonal autoregressive integrated moving average (SARIMA) model based on typical error measures.作者: exclusice 時間: 2025-3-24 09:44 作者: reaching 時間: 2025-3-24 13:09
Textbook 2023tion. The book maximizes students’ understanding of the necessary theoretical background of data analytics, and explains the implementation of data analytics techniques to solve the actual problems in the construction industry.?.?..作者: 古文字學 時間: 2025-3-24 16:06 作者: incubus 時間: 2025-3-24 21:47 作者: FEMUR 時間: 2025-3-24 23:17 作者: cancer 時間: 2025-3-25 06:49
Construction Forecasting Using Time Series Volatility Models,rform the ARIMA model assuming a constant variance in terms of accuracy. R code examples are provided to develop time series volatility models and forecast a time series considering its time-varying variance. Exercise problems are presented at the end of the chapter for readers to review and practic作者: 盡管 時間: 2025-3-25 10:04
Investment Valuation of Construction Projects Under Uncertainty,e-cycle cost analysis method, and determines the probability distribution of the life-cycle cost. Real options analysis evaluates real (nonfinancial) investments under uncertainty with elements for strategic management flexibility and delayed investment. Various examples of construction investment v作者: Fantasy 時間: 2025-3-25 13:10
https://doi.org/10.1007/978-3-322-88550-0engineers in identifying the appropriate time to invest, quantifying the investment risks in projects, and determining the optimum value of an investment for maximizing the returns on investments. This book provides theoretical explanations, hands-on practice problems with R code scripts, and exerci作者: Interferons 時間: 2025-3-25 16:59 作者: SENT 時間: 2025-3-25 23:49
Charakteristika des Baumarktes,e-cycle cost analysis method, and determines the probability distribution of the life-cycle cost. Real options analysis evaluates real (nonfinancial) investments under uncertainty with elements for strategic management flexibility and delayed investment. Various examples of construction investment v作者: 口訣 時間: 2025-3-26 03:03
vide the necessary functions for performing investment valuation. The book maximizes students’ understanding of the necessary theoretical background of data analytics, and explains the implementation of data analytics techniques to solve the actual problems in the construction industry.?.?..978-3-031-27294-3978-3-031-27292-9作者: 公社 時間: 2025-3-26 06:29
Introduction to Construction Analytics,nhance construction productivity, and reduce construction cost overruns. Although data analytics have tremendous potential to improve strategic decision-making in the construction industry as an ever-increasing volume of data becomes available, it has not been fully exploited on a larger scale in th作者: jettison 時間: 2025-3-26 11:26
Construction Time Series Forecasting Using Univariate Time Series Models,ojects. This chapter aims to introduce several construction time series variables, such as Highway Construction Spending and National Highway Construction Cost Index, and demonstrate procedures for investigating the characteristics of such time series. This chapter introduces several univariate time作者: 佛刊 時間: 2025-3-26 14:41 作者: Hemiparesis 時間: 2025-3-26 19:54
Construction Time Series Forecasting Using Multivariate Time Series Models,ls. This chapter explains the process of identifying the leading indicators of a construction time series and developing proper multivariate models, such as vector error correction and vector autoregressive models for forecasting them. Several practical examples are provided along with R codes to sh作者: Restenosis 時間: 2025-3-26 22:31
Construction Forecasting Using Recurrent Neural Networks,racterizing nonlinear relationships. Machine learning models, such as neural networks, have established themselves as a serious alternative to classical statistical models for exploring nonlinear relationships. This chapter introduces recurrent neural networks (e.g., long short-term memory and gated作者: 怪物 時間: 2025-3-27 03:50 作者: 旋轉(zhuǎn)一周 時間: 2025-3-27 06:47 作者: onlooker 時間: 2025-3-27 11:31 作者: 他去就結(jié)束 時間: 2025-3-27 17:29
Race in the Russian Academia,n the previous chapter, one of the modes of racial formation that unfolds through the formalized production of knowledge in the case analyzed here—that is, the social sciences and humanities in Russia today.作者: Repetitions 時間: 2025-3-27 20:48
Book 2020uroendocrine activity, the reader will learn about the molecular specification of hypothalamic cells, developmental modulators and epigenetic factors influencing hypothalamic development, and the development of neuroendocrine circuits. Each chapter provides a concise review of the current and future
