Settlement Data Research Articles

Analysis of long-term MSW settlement based on field data measured at two closed landfills

Waste settlement characteristics vary depending on waste composition, physical properties, waste management operations, and time. In this study, the post-closure waste settlement behavior was analyzed using field data measured at two closed municipal solid waste landfills in Korea, for 23 and 5 years after landfill closure. The long-term waste settlement data was analyzed to derive a time-varying long-term compression ratio (CLT) that was consistent with modeled biodegradation processes. A statistical assessment of the field data was conducted to examine the relationship between CLT and several factors that may affect the observed settlements. The CLT of Landfill #1 and #2 was found to vary between the two landfills that had differences in the percentage of organic matter (B0), total unit weight (γwaste), and moisture content (w) due to changes in waste management policies.

Coupling effects on class C predictions of soft soil settlements

AbstractIn uncoupled consolidation analysis, settlement and pore water pressure are solved independently, whereas in coupled analysis, they are solved simultaneously to ensure continuity (i.e., the volume change in soil due to compression must equal the water volume change caused by dissipation). This study investigates the coupling effects of soil deformation and pore water pressure dissipation in the back analysis of soft soil settlements. It further evaluates the suitability of both coupled and uncoupled constitutive models with different types of monitoring data, providing practical guidance for selecting consolidation models and achieving reliable long-term predictions. The one-dimensional governing equations for soft soil consolidation, incorporating prefabricated vertical drains and creep deformation, are first reviewed. A case study of a trial embankment in Ballina, New South Wales, Australia, is then used to demonstrate the impact of coupling effects and monitoring data on settlement predictions. The results show that considering coupling effects not only improves long-term settlement predictions but also reduces uncertainties in the updated soil parameters, especially when both settlement and pore water pressure data are used.

Prediction of permeability characteristics of mine slurries and sediments using finite-strain framework by optimisation algorithms

Determining the permeability characteristics of mine tailing slurries through laboratory finite-strain consolidation tests is costly due to the extensive testing required. An alternative approach involves back-predicting hydraulic conductivities from settling column test data. This study employed settlement and excess pore pressure data as inputs in optimising the permeability parameters using the finite-strain consolidation (FSC) framework. The proposed approach used the finite difference method to solve the governing equation for FSC in the forward analysis. Two novel variants of the Artificial Bee Colony (ABC) algorithm, namely the Modified Artificial Bee Colony (MABC) and the Hybrid Artificial Bee Colony (HABC) algorithms, were used to back-predict the hydraulic conductivity function parameters from the FSC test data. These models predicted the permeability characteristics using the temporal variation of the settlements for six slurries and excess pore pressure dissipation data for four slurries. The performance of the proposed algorithms along with Hybrid Particle Swarm Optimisation (HPSO) was compared with the conventional optimisation algorithms, viz., PSO, ABC, and Quantum PSO (QPSO). The HABC and HPSO exhibited remarkable stability, consistently converging to identical solution sets across multiple iterations, thereby outperforming other algorithms in this study. Despite its higher time complexity by HABC relative to the other evaluated methods, this complexity is warranted for enhanced robustness. The current study is helpful in brining practical and reliable methodologies for estimating the hydraulic conductivity function from field-scale conductivity (FSC) data, providing critical insights for the safe and efficient management of slurry wastes.

Improved Population Mapping for China Using the 3D Building, Nighttime Light, Points-of-Interest, and Land Use/Cover Data within a Multiscale Geographically Weighted Regression Model

Large-scale gridded population product datasets have become crucial sources of information for sustainable development initiatives. However, mainstream modeling approaches (e.g., dasymetric mapping based on Multiple Linear Regression or Random Forest Regression) do not consider the heterogeneity and multiscale characteristics of the spatial relationships between influencing factors and populations, which may seriously degrade the accuracy of the prediction results in some areas. This issue may be even more severe in large-scale gridded population products. Furthermore, the lack of detailed 3D human settlement data likewise poses a significant challenge to the accuracy of these data products. The emergence of the unprecedented Global Human Settlement Layer (GHSL) data package offers a possible solution to this long-standing challenge. Therefore, this study proposes a new Gridded Population Mapping (GPM) method that utilizes the Multiscale Geographically Weighted Regression (MGWR) model in conjunction with GHSL-3D Building, POI, nighttime light, and land use/cover datasets to disaggregate population data for third-level administrative units (districts and counties) in mainland China into 100 m grid cells. Compared to the WorldPop product, the new population map reduces the mean absolute error at the fourth-level administrative units (townships and streets) by 35%, 51%, and 13% in three test regions. The proposed mapping approach is poised to become a crucial reference for generating next-generation global demographic maps.

DRG payment for major pancreatic surgery: analysis of resource consumption and suggestions from a tertiary hospital in China.

To investigate the cost homogeneity within the Diagnosis-Related Group (DRG) "major operation of pancreas and liver, with general complications or comorbidities" (HB13), its cost-influencing factors, and to propose suggestions for better grouping efficacy. Medical and insurance settlement data of inpatients covered by the DRG payment system at the author's institution were collected from March 15, 2022 to December 31, 2023. The cost homogeneity of group HB13 was assessed using the coefficient of variation (CV). Clinical factors that may have an impact on hospitalization cost for patients undergoing pancreatic surgery were identified through a semi-structured interview administered to the pancreatic surgeons in author's department, their significance was analyzed using multiple linear regression, along with their impact on the cost of different service categories. A proposal to subdivide HB13 was made and evaluated by CV and t-test. The CV of the HB13 group was 0.4. Robotic-assisted surgery and pancreaticoduodenectomy were two independent factors that significantly affected the total cost. Patients undergoing robotic-assisted surgery have an average increase of 41,873 CNY in total cost, primarily derived from operation fee. Patients undergoing pancreaticoduodenectomy have an average increase of 37,487 CNY in total cost, with significant increases across all service categories. HB13 was subdivided based on whether pancreaticoduodenectomy was performed. The newly formed groups exhibited lower CVs than the original HB13. The cost homogeneity of HB13 was lower than that of other DRG groups in author's department. It is recommended to introduce a supplementary payment for patients requiring robotic-assisted surgery, to guarantee their access to this advanced technology. It is recommended to establish a new group with higher payment standard for patients undergoing pancreaticoduodenectomy. A tiered CV criterion for the evaluation of grouping efficacy is recommended to increase intra-group homogeneity, facilitating a better allocation of health insurance funds, and the prevention of unintended negative outcomes such as service cuts and cherry-picking.

Rock and roll: experiments on substrate movement and coral settlement

Rubble is ubiquitous on coral reefs and can aggregate into fields, forming a significant component of the reef substrate. Rubble fields often remain unconsolidated, with the component rubble pieces subject to movement that is dependent on hydrodynamic forcing, rubble size, shape, and other factors. Settlement of corals to rubble fields has long been assumed, but the dynamic movement of rubble pieces has been presumed to deter settlement and is thought to contribute to high post-settlement mortality. Rubble often forms on coral reefs following severe disturbances, and is predicted to increase under climate change, with the potential to impact settlement and recruitment-dependent recovery processes. Through a series of laboratory and field experiments, we demonstrate that corals from broadcast spawning species on the Great Barrier Reef will settle on unstable substrates, even those in constant motion. We also observed more coral spat on settlement tiles suspended in the water column than those fixed to the reef using a common approach to censusing settlement. Sampling of natural rubble on the reef 50 days after a mass-spawning event confirmed the presence of similar numbers of coral settlers on rubble and on tiles fixed to the reef. These results suggest that rubble fields are places of significant settlement for broadcast spawning corals. Suspended tiles were also surprisingly effective in collecting coral settlers, demonstrating that a change in sampling protocol can produce significant variation in settlement data and strengthening the argument for standardisation of settlement-monitoring protocols, particularly at a time of growing need for reliable metrics. These results also suggest that movement of rubble is not precluding settlement outright, but rather post-settlement processes (i.e. competition, predation, shading or burial by shifting rubble) are limiting recruitment to rubble patches. Consequently, rubble stabilisation may increase the survival of spat that have settled in these environments.

Settlement estimation during foundation excavation using pattern analysis and explainable AI modeling

With the rapid expansion of underground engineering, accurate settlement estimation during foundation excavation using monitoring data has gained prominence. Previous studies have typically overlooked data patterns, relying solely on time-series models, which yielded limited accuracy and short-term predictability. To address these issues, this paper performs a thorough pattern analysis covering both the temporal and spatial properties, and proposes a spatiotemporal modeling method grounded in explainable artificial intelligence technique. A real-world engineering case study is conducted to validate the effectiveness of the proposed method. Counterintuitively, the results reveal a weak temporal effect within the settlement data, with only the last step playing a dominant role, whereas the spatial correlation among measuring points is notably more significant. Compared to conventional models, the proposed method consistently outperforms, achieving significantly higher R2 scores and excelling in long-term estimation with reductions of at least 78% in RMSE, 80% in MAE, and 72% in MAPE.

Intelligent Prediction and Application Research on Soft Rock Tunnel Deformation Based on the ICPO-LSTM Model

In tunnel construction, the prediction of the surrounding rock deformation is related to the construction safety and stability of the tunnel structure. In order to achieve an accurate prediction of the surrounding rock deformation in soft rock tunnel construction, a Long Short-Term Memory (LSTM) neural network is used to construct a prediction model of the vault settlement and the horizontal convergence of the upper conductor in soft rock tunnels. The crested porcupine optimisation (CPO) algorithm is used to realise the hyper-parameter optimisation of the LSTM model and to construct the framework of the calculation process of the CPO-LSTM model. Taking the soft rock section of the Baoshishan Tunnel as an example, the large deformation of the surrounding rock is measured and analysed in situ, and the monitoring data of arch settlement and superconducting level convergence are obtained, which are substituted into the CPO-LSTM model for calculation, and compared and analysed with traditional machine learning and optimisation algorithms. The results show that the CPO-LSTM model has an R2 of 0.9982, a MAPE of 0.8595% and an RMSE of 0.1922, which are the best among all the models. In order to further improve the optimisation capability of the CPO, some improvements were made to the CPO and an Improved Crested Porcupine Optimiser (ICPO) was proposed. The ICPO-LSTM prediction model was established, and the ZK6 + 834 section was selected as a research object for comparison and analysis with the CPO-LSTM model. The results of the error analysis show that the prediction accuracy of the improved ICPO-LSTM model has been further improved, and the prediction accuracy of the model meets the requirements of guiding construction.

A novel method for settlement imputation and monitoring of earth-rockfill dams subjected to large-scale missing data

Various missing values inevitably exist in the settlement monitoring data of prolonged operation of earth-rockfill dams, which delays or even interrupts the dam structure analytical procedure. This study aims to develop a reliable method for addressing the missing data problem and monitoring earth-rockfill dam settlement behavior. First, an imputation model, support vector regression based on the finite element method and time input (FEMTSVR), is proposed and offers superior performance when capturing complex nonlinear mappings from environmental variables to settlement on small sample data. Herein, an improved particle swarm optimization algorithm (IPSO) is developed, realizing the nonlinear adjustment of weights and parameter reduction during hyperparameter optimization. Then, this study establishes a sequential prediction model based on gate recurrent unit (GRU) networks to monitor dam behavior on the imputed and complete dataset. Eventually, the proposed method is evaluated using real-world earth-rockfill dam monitoring data with the help of statistical indicators, demonstrating its efficiency in monitoring settlement data subjected to large-scale missingness. This study provides a robust database for dam structural health monitoring, while also providing a promising framework for the safety assessment of other civil or hydraulic engineering based on raw monitoring data.

Subway track foundation settlement deformation prediction based on the BiLSTM-AdaBoost model

The rapid economic expansion has spurred extensive construction near subway networks, impacting the stability of their track foundations. Consequently, it’s crucial to monitor and predict settlement in subway track foundations. However, the dynamic deformation patterns often exhibit nonlinearity and non-stationarity, posing challenges for traditional linear regression models. To tackle this, our study integrates the BiLSTM (bi-directional long short-term memory) network with the AdaBoost ensemble learning algorithm. Using settlement data from Shanghai metro monitoring points, the model is trained and evaluated employing R2 (coefficient of determination), MAE (mean absolute error), and RMSE (root mean square error). Results show that our proposed model displays superior predictive accuracy compared to the LSTM and the BiLSTM, with an average training set R2 of 0.99, test set R2 of 0.78, average MAE of 0.32 mm, and average RMSE of 0.4 mm. Consequently, for forecasting subway track foundation deformations, employing our network model ensures highly accurate predictive capabilities.

Modernizing cocoa production in Ghana: successes, constraints, and future possibilities of innovation diffusion in the cocoa sector

ABSTRACT This paper employs the political settlement framework, diffusion theory, and data generated through a mixed methods design to examine the adoption of innovations and technology in Ghana’s cocoa sector, with particular reference to the Juaboso district, a major cocoa producing area. The findings indicate that while most farmers have adopted improved cocoa planting methods in rows, the use of hybrid varieties and application of chemicals because of perceived benefits of these innovations and state support in adopting them, the use of fertilizers on cocoa farms is very low due to political economy issues, economic challenges, and neopatrimonialism. Historically, corrupt state politicians have used the fertilizer subsidy programme to strengthen patronage networks and reward crony capitalists. We conclude that the socio-political dimension of innovation-diffusion overrides the technical-economic reasons often cited in the literature for the failure of cocoa farmers to adopt these technologies. Addressing neopatrimonialism and recent politicization of input distribution is needed to enhance adoption of innovation and improved technology in Ghana’s cocoa sector.

A reflection on neolithization in the plains of Behshahr and Neka in Eastern Mazandaran, Iran

About 70 years ago, C. S. Coon reported the sudden presence of domesticated animal species following a gap between the Mesolithic/Epi-Paleolithic and the Pre-Pottery Neolithic (PPN), based on data from excavations of the Hotu and Kamarband (Belt) Caves in the southeastern littoral of the Caspian Sea. Then, the first scientific step towards Neolithization studies in the region was taken by proposing a hypothesis that emphasized the import of Neolithic culture and domestication packages from the eastern wing of the Fertile Crescent, within the framework of diffusion theory. More recently, another hypothesis has been proposed that Neolithization in eastern Mazandaran is due to endogenous factors. These hypotheses were proposed despite serious weaknesses in both field studies and analyses. Lack of reliable evidence of domesticated species, inconsistency of paleo-climatology chronologies and data with archaeological periods, lack of subsistence and settlement data, problems of Mesolithic/Epi-Paleolithic to Neolithic chronology, and weaknesses in processing and analyses of archaeological data, are some of the problems in Neolithization studies in the region. The present paper intends to provide a proper analysis of the Neolithization studies in this region, and proposes the theory of low-level food production as a key component.

Study on Grouting Repair and Reinforcement Technology of soft Foundation of large diameter Highway Tunnel

Taking the uneven settlement of the foundation of a two-way eight-lane large diameter highway tunnel as the engineering background, the joint grouting reinforcement technology of miniature steel pipe pile and steel sleeve valve pipe is put forward. based on the field measured settlement data and foundation core samples, the effectiveness of repair and reinforcement is analyzed. The research results show that the combined grouting of miniature steel pipe pile and steel sleeve valve pipe can effectively control the settlement of tunnel foundation, and significantly improve the RQD (Rock Quality Designation) value of tunnel foundation, which provides technical reference and basis for the construction of similar projects.

Estimation of Spatiotemporal Changes of Land Cover in Llapi River Catchment (Northeastern Part of Kosova)

Aim of the studyIn recent decades, Kosova in general and the Llapi River catchment have experienced landscape transformation which resulted in Land Use and Land Cover (LULC) changes, mainly in flat terrains where most of country’s population is concentrated. Other landscapes have experienced changes too, but on a small scale. The aim of the study was to identify landscape transformation and driving forces behind it.Material and methodsSpatiotemporal changes of Land Use and Land Cover (LULC) were made using GIS techniques. Land Cover data from Copernicus Land Monitoring Service (CLMS) with spatial resolution 100 m for three different years: 2000, 2006 and 2018 were analyzed. DEM with 10 m spatial resolution was used to estimate slope steepness and catchments’ altitude. Population data for settlements inside the Llapi River catchment represented in features were downloaded from the Statistical Agency of Kosova, and those data include population numbers for official censuses between 1948 until 2011, while ArcMap 10.8 environment was used for analysis.Results and conclusionsWith GIS technique in analyzing Land Use and Land Cover data (LULC) and other open source data, we found that agricultural land have decreased in area by 9.53%, while the increase of artificial surface is 160.87%, or expressing by absolute values, total agricultural areas in 2000 were 37,827 ha, while in 2018 were 34,334 ha, meaning 0.3ha per capita, less than European level. In order to protect agricultural land, zoning and land use plans should be implemented.

Emerging Spatio-temporal Hot Spot Analysis of Beijing Subsidence Trend Detection Based on PS-InSAR

Abstract. Scholars have done a lot of research on urban settlement, but it is difficult to give consideration to the temporal and spatial attributes of settlement at the same time in its display and analysis. Most of them focused on the analysis of regional settlement, single point settlement curve and settlement rate map at a certain time, but few combined time and space for collaborative analysis. Therefore, in this paper, 32 scenes Sentinel-1B SAR data are used to obtain settlement data of Beijing via PS-InSAR method. Secondly, combined with the temporal and spatial attributes of settlement results, the subsidence law revealed by using spatio-temporal cube slicing and attribute filtering. Finally, subsidence development trend and the detection of abnormal subsidence are explored by emerging hot spots (ESH) analysis. The experimental results show that the settlement funnel center in Beijing is mainly concentrated near the junction of Chaoyang district and Tongzhou district. The settlement range tends to expand. There are several local continuous subsidence areas in the settlement oscillating area. Spatio-temporal analysis makes the development trend of urban settlement more intuitive. Emerging hotspot analysis combined with Getis-Ord Gi* statistics and Mann-Kendall trend test could more effectively analyze the settlement trend of the study area and detect new potential settlement centers, so that to provide auxiliary decision-making for urban safety early warning and city development.

A grid-based methodology for the assessment of time-dependent building damage at large scale

Leakage into underground constructions can result in time-dependent settlements in soft clays. In urban areas with spatial variability in geologic stratification, groundwater conditions and soil compressibility, differential settlements may occur, causing damage to buildings. Current methods for damage assessment that rely on 1D formulations for settlement prediction are not representative for drawdown-induced settlements in heterogeneous environments. Thus, in this paper, we propose a stand-alone approach to integrating spatially distributed, non-Gaussian settlement data into early-stage building damage assessments at a district scale. Deformations computed using a 2D coupled hydro-mechanical finite element model with an advanced constitutive model were then employed to get the time-dependent settlements computed as a 3D grid (along x- and y-directions) over a large area. Building damage was then calculated from these green-field simulations with typically used damage parameters for each building-specific settlement profile and comparing these with damage criteria. The approach was applied to 215 buildings in central Gothenburg, Sweden by simulating scenarios of 10 kPa and 40 kPa pore pressure drawdown in the lower (confined) aquifer. Several scenarios were studied, and the correlation between damage parameters and damage criteria was assessed. Finally, a sensitivity study on grid resolution was performed, as well as a validation against observed damage data. The proposed methodology offers an effective way for early-stage damage assessments at a large area for non-Gaussian settlements so that further investigations and mitigation measures can be targeted to the buildings and locations at the highest risk for damage.

Analysis of sensor parameters for ultra close range lateral penetration of subway tunnel full section sand layer soil pressure shield tunneling

In order to solve the problem of large disturbance to the stratum caused by shield tunneling in the full face sand layer geology by using the earth pressure balance full bin tunneling technology in the subway tunnel shield construction, this paper presents a parameter analysis of the ultra close side penetration of the full face sand layer earth pressure shield in the subway tunnel. By analyzing the monitoring data of surface settlement caused by subway shield construction, it is concluded that the law of surface horizontal settlement basically conforms to peak settlement theory, and the law of surface vertical settlement basically shows a steady downward trend. Through the mathematical modeling of the cumulative settlement data affected by the shield passing of the right line tunnel at the surface monitoring point, the influence of the shield passing of the left line tunnel on the surface settlement is predicted, and the prediction result is accurate. The experimental results show that the maximum deviation is 1.47 mm and the minimum deviation is 0.03 mm. The prediction accuracy is high and meets the accuracy requirements of practical engineering. Under the same geological conditions and construction parameters, the disturbance law of tunnel shield construction on the ground surface has important guiding significance in practical engineering.

Bidirectional and nested model for numerical simulation and machine learning: a case study of long-term settlement prediction of tunnel

Numerical simulation and machine learning are commonly adopted research methods in engineering. This paper proposes a bidirectional and nested model for numerical simulation and machine learning (BNNM). This model permits numerical simulation methods and machine learning methods to participate in each other’s calculation process. It helps overcome the obstruction of unclear mechanisms and inaccurate parameters in numerical simulation methods, and avoid overfitting problem caused by too many features in machine learning methods. Moreover, BNNM frees machine learning methods from the dependence on a specific set of labels. The BNNM helps train machine learning models using obtainable labels, and output results that cannot be easily obtained using field, experiment, and numerical simulations. To illustrate its construction method and performance, a representative BNNM model is constructed using BPNN, in addition to a simple numerical simulation model. This model predicts the long-term settlement of shield tunnel. The results show that the representative model effectively reduces the modelling difficulty associated with numerical simulation and improves prediction accuracy of BPNN model. The model also derives long-term constitutive models of various soils with only the tunnel settlement data set. Although a simplified constitutive model was used, the main advantages of the BNNM model have been highlighted.

Prediction of dredged soil settlement based on improved BP neural network

Based on the measured settlement data from the Reclamation Project of Eastern Hengsha Shoal, two improved BP neural networks optimized by the particle swarm optimization (PSO) algorithm and genetic algorithm (GA) respectively are employed to predict the long-term consolidation settlement of the dredged soil in the reclamation area. By comparing the prediction results of the improved neural networks with those obtained from standard BP neural network and various curve fitting methods including hyperbola method, Hoshino method, and Asaoka method, as well as field-measured data, the improved neural network methods are validated to have a better predictive performance. The error analysis results indicate that compared with the standard BP neural network and curve-fitting prediction methods, the improved BP neural networks exhibit smaller prediction errors in terms of MAE, MSE, RMSE, MAPE, etc., and have a closer match between the predicted settlement values and the actual values. The selected GA-BP and PSO-BP neural network prediction methods in this study demonstrate higher prediction accuracy. Meanwhile, GA-BP demonstrates higher prediction accuracy and generalization ability than PSO-BP, but PSO-BP shows faster convergence in terms of prediction speed. The preferred methods presented in this paper exhibit good robustness and high reliability, which can achieve accurate prediction of dredged soil settlement. Additionally, unlike the traditional curve-fitting methods, the selected methods can effectively reduce the influence of subjective factors during the settlement prediction, and provide a new approach for settlement prediction, which can be effectively utilized as a substitute for long-term settlement monitoring and give reference for the design of settlement control of foundation of further construction projects.

Prediction of Airport Taxiway Settlement based on CNNBiLSTM-Attention

The accurate real-time prediction of complex time series in airport runway settlement is crucial. In order to achieve precise and reliable predictions of runway surface settlement, considering the nonlinear and time-dependent characteristics of settlement data under the coupling effects of dynamic loads, water levels, and other factors, a CNN-BiLSTM-Attention approach is proposed. This method first employs Convolutional Neural Networks (CNN) to extensively extract local temporal features from the input sequences. Subsequently, the extracted local temporal features are input to BiLSTM layers to learn the features of the sequences both in the forward and reverse directions. Finally, the output of the BiLSTM layers is fed into an attention mechanism layer. By computing attention weights, crucial information in the input sequences is weighted and added to enhance the model’s focus on key information. The final settlement prediction is then generated through a fully connected layer. To validate the proposed method, field settlement monitoring data from Runway L at Hangzhou Xiaoshan Airport are utilized as an engineering case study. The results indicate that the combined model captures local features of settlement sequences more effectively with a smaller dataset, achieving higher accuracy compared to a standalone BiLSTM model.