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During a deep excavation project, monitoring the structural health of the adjacent buildings is crucial to ensure safety. Therefore, this study proposes a novel probabilistic deep reinforcement learning (PDRL) framework to optimize the monitoring plan to minimize the cost and excavation‐induced risk. First, a Bayesian‐bi‐directional general regression neural network is built as a probabilistic model to describe the relationship between the ground settlement of the foundation pit and the safety state of the adjacent building, along with the actions in a dynamic manner. Subsequently, a double deep Q‐network method, which can capture the realistic features of the excavation management problem, is trained to form a closed decision loop for continuous learning of monitoring strategies. Finally, the proposed PDRL approach is applied to a real‐world deep excavation case in No. 14 Shanghai Metro. This approach can estimate the time‐variant probability of damage occurrence and maintenance actions and update the state of the adjacent building. According to the strategy proposed via PDRL, monitoring of the adjacent buildings begins in the middle stage rather than on the first day of the excavation project if there is full confidence in the quality of the monitoring data. When the uncertainty level of data rises, the starting day might shift to an earlier date. It is worth noting that the proposed PDRL method is adequately robust to address the uncertainties embedded in the environment and model, thus contributing to optimizing the monitoring plan for achieving cost‐effectiveness and risk mitigation. [ABSTRACT FROM AUTHOR]

With the rapid development of urban rail transit and underground space construction, the advantages of the concealed excavation method are gradually highlighted in the urban center area affected by a complex engineering environment. At present, the pipe curtain method with a flexible section and wide application of the soil layer is being widely used in engineering, but the research on the stability of the excavation face during the construction of the pipe curtain method in soft soil areas still has not formed a complete system. Therefore, this paper analyzes the stability of the excavation face under different reinforcement conditions and the influence of different spacing, numbers, and strength of MJS (metro jet system) zones on the stability of the excavation face of the pipe curtain method based on the actual project of the Guiqiao Road Station of the Shanghai Metro, China, and gives a reference for the reinforcement of the palm face in the excavation process of the pipe curtain method in soft soil areas. The study shows that the safety coefficient of the excavation face decreases with the increase in the slope angle of the excavation face, with the increase in spacing of the MJS zone, and with the increase in height of the MJS zone arrangement, while the safety coefficient of the excavation face increases with the increase in strength of the MJS zones themselves and the increase in the strength of the soil between the MJS zones. Further, the machine learning model is applied to predict the stability of the excavation face under different cases, and the relevant fitting accuracy is above 0.9, which can predict the stability of the excavation better. [ABSTRACT FROM AUTHOR]

Using a soil improvement biotechnology based on microbial-induced carbonate precipitation (MICP) to form a horizontal water barrier in foundation pit dewatering can not only achieve a good dewatering control effect, but also control the cost and obtain good economic and environmental benefits. In this study, taking the Shanghai layer ⑨ sand as the research object, the solution injected into the layer ⑨ sand by MICP grouting technology includes bacterial solution (Sporosarcina pasteurii) and cementation solution (urea and CaCl2 solution). The migration mechanism of the bacterial solution and cementation solution in groundwater was analyzed through batch experiment and column test, and the mathematical model of migration was established. The dynamic adsorption characteristics of the S. pasteurii, Ca2+, and urea were obtained by dynamic adsorption test. Through isothermal adsorption test, the Freundlich isotherm was used to describe the isothermal adsorption of S. pasteurii onto fine sand, and the partition coefficient of the S. pasteurii was found to be 1.5 × 10−7 L/mg. The Langmuir isotherm can be used truly reflect the isothermal adsorption of Ca2+ onto fine sand. The distribution coefficient of the Ca2+ was 7 × 10−8 L/mg. The maximum adsorption capacity of the solute was 2404.8 mg/kg. The adsorption capacity of the urea was very small. According to the sand column test, the vertical dispersion was 11.5 cm. Due to the obvious size effect of dispersion, the sensitivity of dispersion in actual working conditions should be analyzed in a subsequent numerical simulation. The determined solute transport parameters provide support for the further study of the numerical simulation of the solute transport process of the HSRB formed by MICP grouting technology and provide a basis for further field application. [ABSTRACT FROM AUTHOR]

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High delicate particulate matter (PM 2.5 ) concentration can seriously reduce air quality, destroy the environment, and even jeopardize human health. Accordingly, accurate prediction for PM 2.5 plays a vital role in taking precautions against upcoming air ambient pollution incidents. However, due to the disturbance of seasonal and nonlinear characteristics in the raw series, pronounced forecasts are confronted with tremendous handicaps, even though for seasonal grey prediction models in the preceding researches. A novel seasonal nonlinear grey model is initially designed to address such issues by integrating the seasonal adjustment factor, the conventional Weibull Bernoulli grey model, and the cultural algorithm, simultaneously depicting the seasonality and nonlinearity of the original data. Experimental results from PM 2.5 forecasting of four major cities (Shanghai, Nanjing, Hangzhou, and Hefei) in the YRD validate that the proposed model can obtain more accurate predictive results and stronger robustness, in comparison with grey prediction models (SNGBM(1,1) and SGM(1,1)), conventional econometric technology (SARIMA), and machine learning methods (LSSVM and BPNN) by employing accuracy levels. Finally, the future PM 2.5 concentration is forecasted from 2020 to 2022 using the proposed model, which provides early warning information for policy-makers to develop PM 2.5 alleviation strategies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier Ltd. All rights reserved.)