831 research outputs found
Enhanced Welding Operator Quality Performance Measurement: Work Experience-Integrated Bayesian Prior Determination
Measurement of operator quality performance has been challenging in the
construction fabrication industry. Among various causes, the learning effect is
a significant factor, which needs to be incorporated in achieving a reliable
operator quality performance analysis. This research aims to enhance a
previously developed operator quality performance measurement approach by
incorporating the learning effect (i.e., work experience). To achieve this
goal, the Plateau learning model is selected to quantitatively represent the
relationship between quality performance and work experience through a
beta-binomial regression approach. Based on this relationship, an informative
prior determination approach, which incorporates operator work experience
information, is developed to enhance the previous Bayesian-based operator
quality performance measurement. Academically, this research provides a
systematic approach to derive Bayesian informative priors through integrating
multi-source information. Practically, the proposed approach reliably measures
operator quality performance in fabrication quality control processes.Comment: 8 pages, 5 figures, 2 tables, i3CE 201
Enhanced Input Modeling for Construction Simulation using Bayesian Deep Neural Networks
This paper aims to propose a novel deep learning-integrated framework for
deriving reliable simulation input models through incorporating multi-source
information. The framework sources and extracts multisource data generated from
construction operations, which provides rich information for input modeling.
The framework implements Bayesian deep neural networks to facilitate the
purpose of incorporating richer information in input modeling. A case study on
road paving operation is performed to test the feasibility and applicability of
the proposed framework. Overall, this research enhances input modeling by
deriving detailed input models, thereby, augmenting the decision-making
processes in construction operations. This research also sheds lights on
prompting data-driven simulation through incorporating machine learning
techniques
Automated Integration of Infrastructure Component Status for Real-Time Restoration Progress Control: Case Study of Highway System in Hurricane Harvey
Following extreme events, efficient restoration of infrastructure systems is
critical to sustaining community lifelines. During the process, effective
monitoring and control of the infrastructure restoration progress is critical.
This research proposes a systematic approach that automatically integrates
component-level restoration status to achieve real-time forecasting of overall
infrastructure restoration progress. In this research, the approach is mainly
designed for transportation infrastructure restoration following Hurricane
Harvey. In detail, the component-level restoration status is linked to the
restoration progress forecasting through network modeling and earned value
method. Once the new component restoration status is collected, the information
is automatically integrated to update the overall restoration progress
forecasting. Academically, an approach is proposed to automatically transform
the component-level restoration information to overall restoration progress. In
practice, the approach expects to ease the communication and coordination
efforts between emergency managers, thereby facilitating timely identification
and resolution of issues for rapid infrastructure restoration
Projection of Cement Demand and Analysis of the Impacts of Carbon Tax on Cement Industry in China
AbstractCement industry plays a vital role in the process of urbanization and industrialization in China. This paper disaggregates cement consumptions into five large subsectors: building, railway, highway, rural infrastructure and others. We suggest that cement demand will reach the peak of 2.5 billion tons in 2017, followed by a slowly reduction in the next 10 years and a gradually decrease from 2.3 billion tons in 2030 to 1.5 billion tons in 2050. Based on the scenarios analysis of China TIMES model, this paper shows that carbon tax doesn’t work significantly on the technology choice and CO2 emission reduction in the short term. However, in a long run, high carbon tax may increase the application of production with CCS or wasted heat recovery and cut down the small- and medium-sized plants. Moreover, tax on all industries acts more effectively than that only on the cement industry
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Imaging the Centromedian Thalamic Nucleus Using Quantitative Susceptibility Mapping.
The centromedian (CM) nucleus is an intralaminar thalamic nucleus that is considered as a potentially effective target of deep brain stimulation (DBS) and ablative surgeries for the treatment of multiple neurological and psychiatric disorders. However, the structure of CM is invisible on the standard T1- and T2-weighted (T1w and T2w) magnetic resonance images, which hamper it as a direct DBS target for clinical applications. The purpose of the current study is to demonstrate the use of quantitative susceptibility mapping (QSM) technique to image the CM within the thalamic region. Twelve patients with Parkinson's disease, dystonia, or schizophrenia were included in this study. A 3D multi-echo gradient recalled echo (GRE) sequence was acquired together with T1w and T2w images on a 3-T MR scanner. The QSM image was reconstructed from the GRE phase data. Direct visual inspection of the CM was made on T1w, T2w, and QSM images. Furthermore, the contrast-to-noise ratios (CNRs) of the CM to the adjacent posterior part of thalamus on T1w, T2w, and QSM images were compared using the one-way analysis of variance (ANOVA) test. QSM dramatically improved the visualization of the CM nucleus. Clear delineation of CM compared to the surroundings was observed on QSM but not on T1w and T2w images. Statistical analysis showed that the CNR on QSM was significantly higher than those on T1w and T2w images. Taken together, our results indicate that QSM is a promising technique for improving the visualization of CM as a direct targeting for DBS surgery
Enhanced Welding Operator Quality Performance Measurement: Work Experience-Integrated Bayesian Prior Determination
Measurement of operator quality performance has been challenging in the
construction fabrication industry. Among various causes, the learning effect is
a significant factor, which needs to be incorporated in achieving a reliable
operator quality performance analysis. This research aims to enhance a
previously developed operator quality performance measurement approach by
incorporating the learning effect (i.e., work experience). To achieve this
goal, the Plateau learning model is selected to quantitatively represent the
relationship between quality performance and work experience through a
beta-binomial regression approach. Based on this relationship, an informative
prior determination approach, which incorporates operator work experience
information, is developed to enhance the previous Bayesian-based operator
quality performance measurement. Academically, this research provides a
systematic approach to derive Bayesian informative priors through integrating
multi-source information. Practically, the proposed approach reliably measures
operator quality performance in fabrication quality control processes.Comment: 8 pages, 5 figures, 2 tables, i3CE 201
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