MEVO‐Based Dynamic Safety Thresholds for Soft‐Soil Deep Excavations: Engineering Enhancements and Field Validation

This paper presents a multiobjective expectation–value optimization (MEVO)‐guided dynamic safety‐threshold framework for deep excavations in soft soils, integrating soft‐soil‐oriented indicator construction, drift‐aware rolling updates, and full‐process field validation. This study applies and extends the MEVO model to the Hangzhou Fengshouhu Metro Station excavation, a 28 m‐deep cut located in layered marine–lacustrine clays. A comprehensive database, including wall deflection, strut axial force, and pore‐water pressure was established. Region‐specific adaptations were introduced by (i) redefining the search domain through a combined expert judgement/historical‐case procedure and (ii) embedding a displacement–slope dual‐trigger rolling‐update mechanism to mitigate threshold drift. The optimized dynamic thresholds were implemented and validated over an 81‐day observation window. Compared with the original static design limits, the calibrated MEVO thresholds reduced the average alarm frequency by 41% and eliminated missed alarms (missed alarm rate = 0), while ensuring all key deformation indices remained within code‐prescribed envelopes. These results confirm the transferability of the MEVO framework to soft‐soil environments and provide a replicable workflow for deriving engineering‐ready dynamic safety thresholds in Hangzhou and similar coastal regions. The proposed methodology underpins proactive risk management for deep excavations.

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Publication Details

Journal
Advances in Civil Engineering
Published
2026-01-01
DOI
https://doi.org/10.1155/adce/8334017
Primary Topic
Geotechnical Engineering and Analysis
Type
article
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article

MEVO‐Based Dynamic Safety Thresholds for Soft‐Soil Deep Excavations: Engineering Enhancements and Field Validation

Weihuang Zheng, Xiaodan Cao, Weineng Yan, Wenchang Cai et al.
Advances in Civil Engineering
Geotechnical Engineering and Analysis
article

MEVO‐Based Dynamic Safety Thresholds for Soft‐Soil Deep Excavations: Engineering Enhancements and Field Validation

Weihuang Zheng, Xiaodan Cao, Weineng Yan, Wenchang Cai, Biao Li
article en

Abstract

This paper presents a multiobjective expectation–value optimization (MEVO)‐guided dynamic safety‐threshold framework for deep excavations in soft soils, integrating soft‐soil‐oriented indicator construction, drift‐aware rolling updates, and full‐process field validation. This study applies and extends the MEVO model to the Hangzhou Fengshouhu Metro Station excavation, a 28 m‐deep cut located in layered marine–lacustrine clays. A comprehensive database, including wall deflection, strut axial force, and pore‐water pressure was established. Region‐specific adaptations were introduced by (i) redefining the search domain through a combined expert judgement/historical‐case procedure and (ii) embedding a displacement–slope dual‐trigger rolling‐update mechanism to mitigate threshold drift. The optimized dynamic thresholds were implemented and validated over an 81‐day observation window. Compared with the original static design limits, the calibrated MEVO thresholds reduced the average alarm frequency by 41% and eliminated missed alarms (missed alarm rate = 0), while ensuring all key deformation indices remained within code‐prescribed envelopes. These results confirm the transferability of the MEVO framework to soft‐soil environments and provide a replicable workflow for deriving engineering‐ready dynamic safety thresholds in Hangzhou and similar coastal regions. The proposed methodology underpins proactive risk management for deep excavations.

Advances in Civil EngineeringVol. 2026(1)
China University of Geosciences (Beijing) (CN), Zhe Jiang Institute of Geology and Mineral Resource (CN)
Life below water
Openalex Percentile: Top 5%
Geotechnical Engineering and Analysis
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MEVO‐Based Dynamic Safety Thresholds for Soft‐Soil Deep Excavations: Engineering Enhancements and Field Validation — Weihuang Zheng, Xiaodan Cao, et al. · Advances in Civil Engineering (2026) | TGRS Research Map | TGRS