Efficiency Estimation Model of 3D Technology in the Construction Industry

Author(s):  
Alexey V. Bataev
1983 ◽  
Vol 27 (11) ◽  
pp. 906-910 ◽  
Author(s):  
C. Mazie Knerr ◽  
Lawrence B. Nadler ◽  
Susan K. Dowell ◽  
Daniel R. Tufano

The costs of training devices and simulators have induced the military to formulate models for predicting the training effectiveness, including transfer effectiveness, of the devices during their design and development. Analysis of existing models compared them on the following dimensions: objectives, components, units of analysis, metrics, and development. Development included level of completion, validation, and automation. The models analyzed were those for predicting effectiveness or prescribing device characteristics rather than models for empirical evaluation of existing devices. Baseline models included the military Instructional Systems Development (ISD) model and the Navy's Training Effectiveness and Cost Effectiveness Prediction model. Other models were the Training Device Effectiveness model (TRAINVICE); Training Efficiency Estimation Model; training resource estimators; manpower, personnel, and training estimation models; multi-attribute utility estimation; and methods for specifying training device features. Most of the models were prescriptive, rather than predictive, of effectiveness. Few (including TRAINVICE) estimated transfer to the operational setting.


2018 ◽  
Vol 36 (12) ◽  
pp. 1157-1165 ◽  
Author(s):  
Usman A Umar ◽  
Nasir Shafiq ◽  
Mohamed Hasnain Isa

The construction sector is among the fastest growing sectors in Malaysia; it consumes a vast amount of natural resources and produces a massive volume of construction and demolition waste. The waste is collected in a decentralised manner by sub-contracted companies. It is challenging to obtain reliable information on the amount of construction waste generated, because it is hard to determine its exact quantity and composition. Therefore, this study proposes a quantitative construction waste estimation model for residential buildings according to available data collected from the Construction Industry Development Board, Malaysia. In the development of this model, a theoretical investigation of the construction procedure and the construction waste generation process was conducted. The waste generated rate was determined as 25.79 kg m−2 for new residential constructions, which translates into about 553,406 t of anticipated waste annually.


Author(s):  
Qiang Du ◽  
Yi Li ◽  
Libiao Bai

As the largest energy consumer and carbon emitter, China has made substantial efforts to improve energy efficiency for decrease energy consumption, while the energy rebound effect determines its effectiveness. The embodied energy consumption of construction projects accounted for nearly one-sixth of the total economy's energy consumption in China. This paper is based on the logical relationship among capital input, technological progress, economic growth, and energy consumption, adapting an alternative estimation model to estimate the energy rebound effect for the construction industry in China for the first time. Empirical results in our paper reveal that the energy rebound effect for the construction industry in China is about 59.5% for the period of 1990–2014. The results indicate that the energy rebound effect does exist in China’s construction industry and it presented a fluctuating declining trend. This implies that half of the energy savings by technological progress is achieved. In addition, China’s government should implement proper energy pricing reforms and energy taxes to promote the sustainable development of China’s construction industry.


2020 ◽  
Vol 27 (7) ◽  
pp. 1533-1552
Author(s):  
Fanning Yuan ◽  
Miaohan Tang ◽  
Jingke Hong

PurposeThe objective of this study is to evaluate the overall technical efficiency, labor efficiency, capital efficiency and equipment efficiency of 30 Chinese construction sectors to foster sustainable economic growth in the construction industry.Design/methodology/approachThis study employed the super-efficiency data envelopment analysis (SE-DEA) and artificial neural network model (ANN) to evaluate the industrial performance and improvement potential of the Chinese regional construction sectors from 2000 to 2017.FindingsResults showed that the overall technical and capital efficiencies displayed relatively stable patterns. Equipment efficiency presented a relatively huge fluctuation during the sample period. Meanwhile, labor, capital and equipment efficiencies could potentially improve in the next five years. A spatial examination of efficiencies implied that the economic level was still a major factor in determining the efficiency performance of the regional construction industry. Beijing, Shanghai and Zhejiang were consistently the leading regions with the best performance in all efficiencies. Shandong and Hubei were critical regions with respect to their large reduction potential of labor, capital and equipment.Research limitations/implicationsThe study focused on the regional efficiency performance of the construction industry; however, it failed to further deeply discover the mechanism that captured the regional inefficiency. In addition, sample datasets used to predict might induce the accuracy of prediction results. Qualitative policy implications failed to regress the efficiency performance of the industrial policy variables. These limitations will be discussed in our further researches.Practical implicationsEnhancing the overall performance of the Chinese construction industry should focus on regions located in the western areas. In comparison with labor and capital efficiencies, equipment efficiency should be given priority by eliminating outdated equipment and developing high technology in the construction industry. In addition, the setting of the national reduction responsibility system should be stratified to account for regional variations.Originality/valueThe findings of this study can provide a systematic understanding for the current and future industry performance of the Chinese construction industry, which would help decision makers to customize appropriate strategies to improve the overall industrial performance with the consideration of regional differences.


2017 ◽  
Author(s):  
Hannah M. Curtis ◽  
Hendrika Meischke ◽  
Nancy Simcox ◽  
Sarah Laslett ◽  
Noah Seixas

2020 ◽  
Author(s):  
N. Madrahimov ◽  
K. Alhussini ◽  
V. Sales ◽  
D. Radakovic ◽  
K. Penov ◽  
...  
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