Safety risk analysis based on a geotechnical instrumentation data warehouse in metro tunnel project

Lack of complete information is the major factor causing accidents on metro tunnel projects. Safety risk control requires a comprehensive analysis of instrumentation data integrated with other safety related data. Based on a multidimensional data model, a safety-oriented geotechnical instrumentation data warehouse is established to integrate all the data from heterogeneous data sources, not only for quick retrieval of information on site, but also for further analysis. As one of the most important safety indicators, ground settlement is analyzed to take advantage of the data warehouse. A double Gaussian model is proposed to represent the settlement trough over twin tunnels. Moreover, a revised trough width parameter is proposed to represent the scope of safety risk areas. The ground loss is found to grow in a pattern that can be represented by a logistic curve. The first derivative of ground loss is applied to represent the evolution of safety risk.

[1]  B. M. New,et al.  SETTLEMENTS ABOVE TUNNELS IN THE UNITED KINGDOM - THEIR MAGNITUDE AND PREDICTION , 1982 .

[2]  K. M. Neaupane,et al.  Prediction of tunneling-induced ground movement with the multi-layer perceptron , 2006 .

[3]  S. W. Hong,et al.  Neural network based prediction of ground surface settlements due to tunnelling , 2001 .

[4]  O. J. Santos,et al.  Artificial neural networks analysis of São Paulo subway tunnel settlement data , 2008 .

[5]  S. Chi,et al.  OPTIMIZED BACK-ANALYSIS FOR TUNNELING-INDUCED GROUND MOVEMENT USING EQUIVALENT GROUND LOSS MODEL , 2001 .

[6]  Herbert H. Einstein,et al.  Describing settlement troughs over twin tunnels using a superposition technique , 2007 .

[7]  Irtishad Ahmad,et al.  Development of a decision support system using data warehousing to assist builders/developers in site selection , 2004 .

[8]  S Suwansawat.  Shield tunneling database management for ground movement evaluation , 2004 .

[9]  Chien-Cheng Chou,et al.  A spatiotemporal database approach to the management of utility work schedules in transportation projects , 2011 .

[10]  Yılmaz Mahmutoğlu.  Surface subsidence induced by twin subway tunnelling in soft ground conditions in Istanbul , 2011 .

[11]  Jonathan Jingsheng Shi,et al.  A project-oriented data warehouse for construction , 2006 .

[12]  J. S. Kim,et al.  Effectiveness of OLAP-based cost data management in construction cost estimate , 2007 .

[13]  Kwok-wing Chau,et al.  Application of data warehouse and Decision Support System in construction management , 2003 .

[14]  Jingsheng Shi,et al.  MODULAR NEURAL NETWORKS FOR PREDICTING SETTLEMENTS DURING TUNNELING , 1998 .

[15]  Karen Corral,et al.  The impact of alternative diagrams on the accuracy of recall: A comparison of star-schema diagrams and entity-relationship diagrams , 2006, Decis. Support Syst..

[16]  S. Suwansawat,et al.  Artificial neural networks for predicting the maximum surface settlement caused by EPB shield tunneling , 2006 .

[17]  R. Peck.  Deep excavations and tunnelling in soft ground , 1969 .

[18]  Yang Jun,et al.  Utilizing exchanged documents in construction projects for decision support based on data warehousing technique , 2005 .

[19]  Stephen J. Wright,et al.  Numerical Optimization , 2018, Fundamental Statistical Inference.

[20]  W. H. Inmon,et al.  Building the data warehouse , 1992 .