Early detection of adverse conditions in deep excavations using statistical process control

摘要

Deep excavation is a typical practice in the construction of modern high-rise buildings, especially in urban centers andmodern congested cities. Due to the uncertainties inherited from geotechnical works, monitoring programs are mandated atexcavation sites to avoid undesired consequences of failures or excessive deformations. In these monitoring programs, thecommon practice is using fixed threshold limits to identify possible adverse conditions that may lead to undesirable consequencesand delays in the corrective actions. This study proposes a statistical process control (SPC) approach integratingindividual X and exponential weighted moving average (EWMA) charts to enhance the early detection of extreme readingsand consequently, support the decision making process in the monitoring programs of deep excavations. The proposed SPCapproach is demonstrated by a deep excavation case study in the heart of the congested business district in Dubai, UAE.The results reveal that the proposed SPC approach is considerably superior to the current practices for the early detectionof adverse conditions and unanticipated shifts. The outcomes of this study are expected to improve the efficacy of the deepexcavation monitoring systems and support decision makers in taking the required corrective actions to avoid serious economicand human losses.