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Typical undisturbed borehole soil samples of layer › 3 in YEIZ (a) and YECPZ (f), dredger fills in YEIZ (b) and YECPZ (c), miscellaneous fill in YECPZ (d) and plain fill in YECPZ (e).
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Shanghai has the largest newly reclaimed area in China’s coastal regions, and dredger fills are universally distributed in reclaimed lands there. Multi-scale geotechnical properties of dredger fills in region-scale (>1 km²), site-scale (1 m²–1 km²), mesoscale (1 cm²–1 m²), and microscale (1 µm²–1 cm²) were studied mainly through cone penetration in...
Citations
... Therefore, the seal water pressure must be higher than the pressure on the shaft side to ensure sufficient flow to flush sediment to a safe area [8,9]. However, the operational environment for dredgers is inherently intricate and influenced by many factors [10]. During each construction period, factors such as soil quality variations, vibrations from wind and waves, and high salinity and corrosion in the sea can cause the sensor of the underwater pump to lose or even fail, resulting in the inability to accurately measure the pressure value of the shaft seal water pressure [11,12]. ...
The mud pump water sealing system (MPWSS) is important in the efficient operation and prolonged service life of the cutter suction dredger’s (CSD) mud pump. Considering that the underwater pump operates underwater and the shaft seal water pressure sensor is prone to failure, a hybrid deep learning model MCNN transformer is proposed to predict the underwater pump shaft seal water pressure in the event of sensor failure. This paper uses big data from the dredging project to deeply excavate the relationship between the shaft end sealing water pressure and other construction data by combining experience and artificial intelligence, and then uses multi-scale convolutional neural network (MCNN) to reconstruct the data, highlighting the time series characteristics of the multi-scale data were then input into the transformer model for prediction, and compared with a single MCNN, transformer model and four other neural networks. Finally, the cutter suction dredger “Hua An Long” was selected as an application research case; experimental comparisons were conducted on seven different models to verify the accuracy and applicability of the MCNN-transformer model.
... It learns and fits ground subsidence patterns by constructing feature processes, model training, and regression prediction. Existing approaches for subsidence prediction include support vector regression (SVR) [39], artificial neural networks (ANN) [40], and Bayesian networks [41]. Although many studies have been developed in the field of ground settlement prediction research, the majority of them are still combined with traditional measurement techniques and primarily address the problem of reflecting ground settlement in small areas, with relatively few used in the prediction of time series of ground subsidence over large areas with high observation density. ...
Ground subsidence is a significant safety concern in mining regions, making large-scale subsidence forecasting vital for mine site environmental management. This study proposes a deep learning-based prediction approach to address the challenges posed by the existing prediction methods, such as complicated model parameters or large data requirements. Small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology was utilized to collect spatiotemporal ground subsidence data at the Pingshuo mining area from 2019 to 2022, which was then analyzed using the long-short term memory (LSTM) neural network algorithm. Additionally, an attention mechanism was introduced to incorporate temporal dependencies and improve prediction accuracy, leading to the development of the AT-LSTM model. The results demonstrate that the Pingshuo mine area had subsidence rates ranging from −205.89 to −59.70 mm/yr from 2019 to 2022, with subsidence areas mainly located around Jinggong-1 (JG-1) and the three open-pit mines, strongly linked to mining activities, and the subsidence range continuously expanding. The spatial distribution of the AT-LSTM prediction results is basically consistent with the real situation, and the correlation coefficient is more than 0.97. Compared with the LSTM, the AT-LSTM method better captured the fluctuation changes of the time series for fitting, while the model was more sensitive to the mining method of the mine, and had different expressiveness in open-pit and shaft mines. Furthermore, in comparison to existing time-series forecasting methods, the AT-LSTM is effective and practical.
... More recently, the environmental protection requirements for dredging engineering have increased tremendously. The industry has gradually changed from the original rough style to the current precise style, which means that the operator must have strict control on the dredger (Wu et al., 2019). During the construction, it is necessary to ensure the completion of the task, and no less-dredging or over-dredging. ...
With the increasing environmental protection standard, the Cutter Suction Dredger (CSD) construction is required to guarantee precise dredging in some special areas. Specifically, dredging the side slope of channeling, which requires the operator to accurately control the CSD. However, the underwater cutting environment of CSD construction is complicated and the soil kinds are sometimes spatially different, which makes it extremely difficult for the operator to accurately control the CSD. Therefore, we propose a state-of-the-art time series prediction model based on CNN-LSTM Encode-Decode, which can predict the cutting status of the cutter in advance and also ahead estimate the soil kinds to be cut. First, we establish the relationship between the cutting torque and the cutter motor power (CMP) and rotation speed (CMRS). By ahead forecasting the CMP and CMRS, the cutting torque at the next moment can be obtained. Then, the experienced operator can estimate the kind of underwater cutting soil by torque. In addition, we also compare the ahead one-step forecasting and ahead multi-step forecasting of this method with other models and conduct an experiment to verify the performance. The comparison results demonstrate that our approach significantly outperforms, which can help the operator's ahead perception of the underwater cutter status and ensure precise dredging.
... However, the ground subsidence of suburbs, especially the reclamation area, has not been paid enough attention. At present, Shanghai has the largest newly reclaimed land in China's coastal areas (Wu et al., 2020). Dredger fill in the reclamation area is characterized by high porosity, high compressibility, high water content, low permeability, and a low bearing capacity (Yuan et al., 2018). ...
In recent decades, large-scale reclamation projects have been performed in the intertidal flat area of Shanghai, China. Due to the self-weight consolidation of the foundation and dynamic load caused by human activities, the newly reclamation area will sink within a period of time after the land is formed. Therefore, it is necessary to carry out surface deformation monitoring for taking preventive measures in advance. In this research, the PS-InSAR technology, mostly used for urban subsidence monitoring, was applied to obtain the ground deformation information of Shanghai coastal area based on ENVISAT/ASAR (2007.07-2010.02) and Sentinel-1A (2017.07-2020.02) datasets. The results showed that: 1) Compared with ASAR data, the Sentinel-1A data could distinguish more coherent points and get more comprehensive deformation distribution characteristics. 2) Most high-coherent points were detected in artificial objects, especially airport runways, buildings, roads and seawalls. 3) There was obvious uneven land subsidence in the study area during the two monitoring periods, the PS points with high subsidence rates (<-20 mm/a) mainly distributed around Dishui Lake and artificial seawalls. 4) The ground subsidence velocity of the newly formed land gradually slowed down over times, with the average subsidence rate decreased from -10.45 mm/a to -4.94 mm/a. Our study proved that remote sensing monitoring for ground subsidence in reclaimed land could be realized based on PS-InSAR technology, which could provide the spatial distribution characteristics of subsidence in large-scale and long-term series and help the sustainable development of coastal engineering construction.
... Therefore, the slurry concentration sensor is very important in construction. It not only affects the calculation of the output but also affects the dredger operator's judgment of the construction status [2][3][4]. ...
Aiming at the problem that the cutter suction dredger (CSD) slurry concentration measurement method (γ-ray concentration meter) is single, and the frequent failures affect the construction continuity. A soft measurement method of slurry concentration based on the mechanism and data dual-mode drive was proposed. First, analyze the process of cutter cutting and the force of the mud and sand particles inside of the cutter. Then, the concentration prediction model of the slurry transportation process was presented by digital simulation modeling technology. Second, a data-driven slurry concentration prediction model was established, using big data, machine learning, data mining, and other technologies, which learn the historical data of the dredger. To verify the accuracy of the two models, this paper designed three different parameters and five operating conditions on the experimental platform. Experiments show that the prediction performance of the two models is good. In most working conditions, the mechanism-driven slurry concentration model and the data-driven slurry concentration model maintain an error rate of less than 5%, but the prediction error of the mechanism-driven slurry concentration model is relatively large under the flow velocity at the suction port is 3.5 m/s.
Overall, the slurry concentration prediction model based on the mechanism & data has high prediction accuracy and can improve construction efficiency and reliability.
... Therefore, BNs have been widely used in many fields, e.g. human vulnerability to earthquake-induced landslides [22], land subsidence risk evaluation [23], and marine transportation risk assessment [20]. Given that many factors should be considered in decision making related to disposal of excavated soil into abandoned mines, and that some correlated factors of specific mines probably cannot be obtained by decision makers, BN can be used to evaluate engineering practicability of disposing of excavated soft soil during mine reclamation. ...
Disposal of the excess excavated soft soils produced in construction is a contemporary engineering challenge. A win-win solution could be found through utilising much excavated soil during reclamation of abandoned mines, and an evaluation model is necessary to realise this solution. This study aimed to propose a decision-making framework for large-scale disposal of excavated soft soil during mine reclamation using a Bayesian network (BN). Utilising multi-facet information, a decision-making path considering engineering and economic factors, and engineering risks was established. Thereafter, the practicability of backfilling much excavated soft soil into 215 abandoned opencast mines located in Wenzhou, China was evaluated. Based on the decision-making path and information of the 215 mines, a BN including 22 nodes was established with the practicability of large-scale soil disposal in mines as the decision node. Consequently, 40 valid mines were evaluated to be able to absorb 5 × 10⁷ m³ soil, around 50% of the total excavated soil from the municipal districts of Wenzhou during 2020–2024. The BN evaluation showed high reliability with accuracy of all the nodes >70% and that of the decision node >90%. Five input nodes were identified to be high-impact for evaluation of the practicability and total fill volume.
... Shanghai is unique for its continuous artificial seawalls, low-elevation topography, and large population. Additionally, subsidence has been widely confirmed in the reclamation area of Shanghai [45,46,108] from multi-platform InSAR measurements. In this work, we also show DInSAR-driven ground displacement maps that are useful for insights into the future evolution of coastline subsidence. ...
Chinese coastal topography has changed significantly over the last two decades due to human actions such as the development of extensive land reclamation projects. Newly-reclaimed lands typically have low elevations (<10 m) and often experience severe ground subsidence. These conditions, combined with the more frequent occurrence of extreme sea-level events amplified by global climate change, lead to an increased risk of flooding of coastal regions. This work focuses on twelve Chinese coastal areas that underwent significant changes from 2000 to 2015 in their environments, correlated to relevant land reclamation projects. First, the ground changes between 2000 and 2015 were roughly computed by comparing the TanDEM-X and the Shuttle Radar Topography Mission (SRTM) digital elevation models of the investigated areas. These results indicate that six of the analyzed coastal zones have reclaimed more than 200 km2 of new lands from 2000 to 2015, with five of them in northern China. Second, we focused specifically on the city of Shanghai, and characterized the risk of flood in this area. To this purpose, two independent sets of synthetic aperture radar (SAR) data collected at the X- and C-band through the COSMO-SkyMed (CSK) and the European Copernicus Sentinel-1 (S-1) sensors were exploited. We assumed that the still extreme seawater depth is chi-square distributed, and estimated the probability of waves overtopping the coast. We also evaluated the impact on the territory of potential extreme flood events by counting the number of very-coherent objects (at most anthropic, such as buildings and public infrastructures) that could be seriously affected by a flood. To forecast possible inundation patterns, we used the LISFLOOD-FP hydrodynamic model. Assuming that an extreme event destroyed a given sector of the coastline, we finally computed the extent of the flooded areas and quantified its impact in terms of coherent structures potentially damaged by the inundation. Experimental results showed that two coastline segments located in the southern districts of Shanghai, where the seawalls height is lower, had the highest probability of wave overtopping and the most significant density of coherent objects potentially subjected to severe flood impacts.
... Therefore, dredging channel and hydraulic reclamation (Figure 1e) are the key measures to maintain the effective depth of water channels, to settle the dredged waste soil, to reap abundant reserve land resources, and to protect the marine and biological environments. Within this framework, Shanghai has the largest newly reclaimed land in China's coastal areas [9]. However, the widespread dredger load covers the original estuarine and littoral depositions in the whole reclamation area (Figure 1f), which can not only produce large deformation in dredger fill but can also cause varying degrees of consolidation deformations in underlying soft soil layers [10]. ...
... However, the widespread dredger load covers the original estuarine and littoral depositions in the whole reclamation area (Figure 1f), which can not only produce large deformation in dredger fill but can also cause varying degrees of consolidation deformations in underlying soft soil layers [10]. The deformations of multiple soil layers Within this framework, Shanghai has the largest newly reclaimed land in China's coastal areas [9]. However, the widespread dredger load covers the original estuarine and littoral depositions in the whole reclamation area (Figure 1f), which can not only produce large deformation in dredger fill but can also cause varying degrees of consolidation deformations in underlying soft soil layers [10]. ...
Land reclamation has been increasingly employed in many coastal cities to resolve issues associated with land scarcity and natural hazards. Especially, land subsidence is a non-negligible environmental geological problem in reclamation areas, which is essentially caused by soil consolidation. However, spatial-scale evaluation on the average degree of consolidation (ADC) of soil layers and the effects of soil consolidation on land subsidence have rarely been reported. This study aims to carry out the integrated analysis on soil consolidation and subsidence mechanism in Chongming East Shoal (CES) reclamation area, Shanghai, at spatial-, macro-, and micro-scale so that appropriate guides can be provided to resist the potential environmental hazards. The interferometric synthetic aperture radar (InSAR) technique was utilized to retrieve the settlement curves of the selected onshore (Ra) and offshore (Rb) areas. Then, the hyperbolic (HP) model and three-point modified exponential (TME) model were combined applied to predict the ultimate settlement and to determine the range of ADC rather than a single pattern. With two boreholes Ba and Bb set within Ra and Rb, conventional tests, MIP test, and SEM test were conducted on the collected undisturbed soil to clarify the geological features of exposed soil layers and the micro-scale pore and structure characteristics of representative compression layer. The preliminary results showed that the ADC in Rb (93.1–94.1%) was considerably higher than that in Ra (60.8–78.7%); the clay layer was distinguished as the representative compression layer; on micro-scale, the poor permeability conditions contributed to the low consolidation efficiency and slight subsidence in Rb, although there was more compression space. During urbanization, the offshore area may suffer from potential subsidence when it is subjected to an increasing ground load, which requires special attention.
... The rapid development of urbanization has led to a severe paucity of land and proximity of construction activities to the different existing structures. The land shortage has widely obliged the coastal cities to perform a plan of land reclamation (Cao and Wang 2007;Shi et al. 2015;Ni, Mei, and Zhao 2018;Wu et al. 2019). The reclamation of land from surrounding waters can be performed by filling materials such as soil and rock to an area of water. ...
The ground vibrations during pile driving operation have a drastic potential to undermine the surrounding structures both in land and reclaimed land. Particularly, reclaimed land necessitates ample application of pile driving due to the weak land condition. To prevent the structural damage, attenuation of the ground vibrations to an allowable level through active isolation of circular open trench is the scope of this study. In this research, finite element simulations of continuous impact pile driving process from the ground surface was executed with particular attention to the pile-soil interaction, and thereby, the efficiency of open trench application in attenuation of the unsafe distance of different structures was surveyed using the vibration sensitivity degree. Regarding the crucial parameters of an open trench (depth, width, and location), it was concluded that a sufficient high depth can attenuate the unsafe distance up to 68%, the trench width variations are less effective, and an average pile-trench distance is the most efficient option. The excavation volume was also concluded as another crucial parameter in open trench design which takes all three parameters into account. The trench depth equal to the pile’s maximum critical depth of vibration was inferred for an optimum design.
... Determining how to improve the dredging efficiency and increase the dredging output has always been a research focus in the industry, and dredging productivity predictions can be used in advance to plan the construction strategy, shorten the construction period and reduce the construction cost. However, the actual construction environment of dredgers is complex, and there are many influential factors (Wu et al., 2019a) (Rojas-Sola and De la Morena-De la Fuente, 2018). At present, the prediction of productivity in the industry generally involves adopting prediction methods based on semi-empirical formulas; that is, the production process of dredgers is modelled and simulated according to previous construction experience, and many activity parameters are added in the process of model fitting to fit the actual production movements of dredgers. ...
To solve the problem that dredging prediction systems provide inaccurate productivity predictions and rely heavily on mud concentration data. This paper presents a data mining method to accurately predict dredger productivity by using model-stacked generalization in the absence of mud concentration data. First, eliminate abnormal construction data, and ℓ2 norm normalization and log smooth transformation are then performed on the data. Second, Spearman's rank correlation coefficient method is used to extract features. Five machine learning models, namely, Lasso, Elastic net (ENet), Gradient-boosting decision tree (GBDT), extreme gradient boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM), were introduced to predict dredger productivity. Based on these five models, a stacked generalization model was applied. The results show that the goodness of fit R² of the stacked generalization model for productivity prediction is 0.9281, which is higher than the accuracy of the other algorithms investigated, and the optimization effect is obvious.
