M Lu, X Shen, S Mao
Estimating Potential Cost Savings from Implementing an Innovative TBM Guidance Automation System
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Mao S,Shen X,Lu M,Wu X
Real-time tablet-based virtual reality implementation to facilitate tunnel boring machine steering control in tunnel construction
Abstract: On the majority of tunneling projects, steering a tunnel boring machine (TBM) currently relies on a laser station which projects a laser beam onto a laser target board mounted on the TBM. However, laser target boards lack accuracy and reliability, thus potentially contributing to quality defects and increased risks of schedule delay and budget overrun in tunnel construction. This research has developed a cost-effective, real-time solution called “virtual laser target board (VLTB)” to substitute for physical laser target boards in guiding TBM during construction. Through integrating automation control mechanisms, innovative computing algorithms, and wireless network technologies, the VLTB technology transforms a popular survey tool, the robotic total station, into a construction control robot which precisely tracks and positions the TBM. By applying an enhanced point-to-angle computing algorithm, VLTB calculates the exact coordinates of the cutter head center on the working TBM in millimeter-level accuracy. The invisible cutter head center is projected onto a “virtual laser target board” on a tablet interface in relation to the as-designed alignment. Based on field testing, VLTB is found to be able to lend real-time, relevant assistance to TBM operators and tunnel surveyors. Compared to other advanced technologies in the market, VLTB provides a simpler and more flexible solution to ensure tunnel alignment control and enhance quality and productivity performances in tunnel construction.
Keywords: Tunnel Construction,Tunnel Boring Machine,Virtual Reality,Mobile Computing,Machine Control and Guidance,Robotic Total Station
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Mao Zhi, Goh Bee Hua, Wang Shouqing, Ofori G
Forecasting construction industry-level total factor productivity growth using neural network modeling
Abstract: Total Factor Productivity (TFP) is widely recognised as a better indicator than
Labour Productivity and Multi-Factor Productivity to represent industry-level
productivity performance. Productivity is the key determinant of a nation's
standard of living and an industry's competitiveness. As such, the ability to predict
trends in TFP growth in the construction industry is very important. The factors
influencing TFP growth in the construction industry are complicatedly interrelated.
This fact made the conventional regression method highly inadaptable to such
complex multi-attribute nonlinear mappings.
As an AI information-processing tool, the artificial neural network (ANN) system
has been proven to be a powerful approach to solving complex nonlinear mappings
with higher accuracy than regression methods. However, so far, there has been
little application of ANNs in predicting TFP growth in the construction field. This
study will for the first time, apply the concepts of ANNs to develop a model to
forecast the TFP growth in the case of the construction industry of Singapore.
Macro-level information processing models are useful in monitoring and predicting
the performance of the construction industry as a whole. With the need to manage
construction performance information at all three levels, namely, industry, firm and
site, this study looks specifically at developing an 'intelligent' model for forecasting
Meanwhile, using the same set of data, a model developed by the Multiple Linear
Regression method will serve as a benchmark to judge the performance of the ANN
model. The ANN model, compared with the traditional regression model, would be
expected to have better forecasting ability for TFP growth in the construction
industry, in terms of accuracy.
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Cluster: papers of the same cluster (result of machine made clusters)
Class: class.analysis (0.037562)
Sound: read aloud.
Permission to reproduce these documents have been graciously provided by the Aarhus School of Architecture, Denmark. The assistnace of the editor, Prof. Kristian Agger, is gratefully aprecciated.
Quan Mao and Nan Li
Resilience Assessment of Interdependent Critical Infrastructure Systems
Abstract: The resilience of critical infrastructure systems (CISs), in terms of the containment of spreading of system failures and function losses, and the effectiveness of post-disturbance restoration, is largely determined by how the CISs are interconnected and their interactions with each other. Despite the wide recognition of their significance in the literature, the impact of the interdependencies on the resilience of the CISs has rarely been quantitatively assessed, and the mechanism of such impact has largely remained unknown. This study models CISs interdependencies with two typical network-based models, simulates the destruction and restoration stages of the interconnected CISs under various assumptions and settings, and based on the simulation results assesses the level of resilience of the CISs with two typical resilience metrics. A case study was conducted in a middle-sized city in China. The results showed that the resilience of the CISs may be overestimated by more than 20% if interdependencies of the CISs are not factored in. The findings suggested that to conduct comprehensive damage estimation of extreme events and optimize the efficiency of post-event restoration tasks, it is critical to consider the interdependencies of CISs and appropriately account for their impact.
Keywords: Critical Infrastructure, Interdependency, Resilience, Resilience Metric, Cascading Failure, Restoration
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