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

Deep excavation is a typical practice in the construction of modern high-rise buildings, especially in urban centers and modern congested cities. Due to the uncertainties inherited from geotechnical works, monitoring programs are mandated at excavation sites to avoid undesired consequences of failures or excessive deformations. In these monitoring programs, the common practice is using fixed threshold limits to identify possible adverse conditions that may lead to undesirable consequences and delays in the corrective actions. This study proposes a statistical process control (SPC) approach integrating individual X and exponential weighted moving average (EWMA) charts to enhance the early detection of extreme readings and consequently, support the decision making process in the monitoring programs of deep excavations. The proposed SPC approach 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 detection of adverse conditions and unanticipated shifts. The outcomes of this study are expected to improve the efficacy of the deep excavation monitoring systems and support decision makers in taking the required corrective actions to avoid serious economic and human losses.