12 research outputs found
Experimental investigation of pier scour depth and its scour hole pattern for different shapes
No full textLocal scour, a complex phenomenon in river flows around piers with movable beds,
can damage bridge piers during high floods. Predicting scour depth accurately is vital for safety
and economic reasons, especially for large bridges. This study using hydraulic flume laboratory
experiments compared diamond, square, and elliptical pier models of different sizes under
steady clear-water conditions considering different flow rates and discharge levels to identify
the most efficient shape with less local scour. Local scour, a complex phenomenon in three
dimensional flow around piers in rivers with movable beds, can lead to detrimental effects on
bridge piers due to high flood velocities. Accurate prediction of scour depth is crucial for
economic and safety reasons, especially for large bridges with complex piers. Hydraulic
engineers are keen on forecasting the equilibrium scour depth. To achieve this, laboratory
testing compared diamond, square, and elliptical pier models under steady clear-water
conditions to identify the most efficient pier shape with less local scour. This research provides
valuable insights for optimizing pier design to enhance bridge stability and resilience against
scour-induced risks. A variety of configurations, including different sizes and shapes of piers
were experimented with in the flume using diamond, square, and elliptical shapes. The test
results showed that the local scour depth around elliptical piers was around 29.16% less, and
around diamond piers, it was approximately 16.05% less compared to the scour depth observed
around square piers with the same dimensions. The researchers also observed distinct patterns
of scouring around different pier shapes. Specifically, the square-shaped piers displayed the
highest level of scouring depth, that is, 48 mm, followed by the diamond-shaped pier which
experienced a scouring depth of 48 mm while the elliptical-shaped piers experienced the least
amount of scouring depth, that is, 34 mm. The test results also demonstrated that pier size
significantly influences scouring, with an increase in pier size from 3 × 3 cm2 to 5 × 5 cm2
leading to a rise in scour depth by 26.04%. Moreover, this study findings also elucidated that
an increase in flow results in an increase of in scouring depth i.e., elevating the discharge from
0.0026 cumecs to 0.0029 cumecs led to a 28.13% increase in scouring depth for the identical
pier size. These findings provide valuable insights into the hydraulic behavior of various pier
shapes and can aid in the optimization of bridge design and hydraulic engineering practices.
The investigations further revealed that local scouring is sensitive not only to pier dimensions
but also to other critical parameters, including flow rate, time of exposure, and the size of a
pier
Comparative performance evaluation of machine learning models for predicting the ultimate bearing capacity of shallow foundations on granular soils
No full textAbstract Accurate estimation of the ultimate bearing capacity (UBC) of shallow foundations is critical for safe and economical geotechnical design. Traditional approaches depend heavily on extensive and costly field and laboratory investigations, while numerical simulations, though effective, are computationally intensive and time-consuming. To address these limitations, this study investigates the application of machine learning (ML) models for efficient and reliable prediction of the ultimate bearing capacity of shallow foundations. Although numerous studies have explored individual ML techniques for this purpose, a comprehensive and consistent comparison of widely used models under identical conditions remains limited. This research evaluates six ML algorithms; k-Nearest Neighbors (kNN), Artificial Neural Network (NN), Random Forest (RF), Extreme Gradient Boosting (xGBoost), Adaptive Boosting (AdaBoost), and Stochastic Gradient Descent (SGD), using a dataset of 169 experimental results collected from literature. The input features include foundation width (B), depth (D), length-to-width ratio (L/B), soil unit weight (γ), and angle of internal friction (φ). Model performance was assessed using multiple evaluation metrics: coefficient of determination (R²), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and objective function (OBJ). To enhance model interpretability, SHapley Additive Explanations (SHAP) and Partial Dependence Plots (PDPs) were employed to analyze feature importance and input-output relationships, highlighting the influence of both soil properties and foundation geometry on predicted bearing capacity. Among the evaluated models, AdaBoost demonstrated the best overall performance, achieving R² values of 0.939 and 0.881 on the training and testing sets, respectively. Based on the cumulative ranking of the models across all evaluation metrics, the models were ranked in the following order of performance: AdaBoost > kNN > RF > xGBoost > NN > SGD. While the results are promising, a key limitation is the use of single-layer soil data, which restricts applicability to more complex, multilayered soil profiles. Future studies should incorporate multilayer datasets and account for spatial variability to enhance the generalizability and robustness of predictive models
Enhancing intelligence in multi-agent systems with edge-assisted causal knowledge aggregation
No full textDynamic and uncertain environments pose major challenges for multi-agent autonomous systems, particularly in achieving robust simultaneous localization and mapping (SLAM) and efficient knowledge sharing across robots. Conventional data-driven methods often overlook underlying causal structures, resulting in spurious correlations and limited generalization. To address this, we present CASK—an edge-assisted causal knowledge aggregation framework that fuses structured causal inference with data-driven learning to improve adaptive decision-making. A key feature is a time-based normalization mechanism that ensures mapping consistency across varying operational speeds, enabling speed-independent transfer of spatial knowledge between heterogeneous agents. Wevalidate CASK through simulations and real-world experiments using autonomous ground vehicles,aclassofmobilerobots.Resultsshowsubstantialgainsoverstate-of-the-artmethods:upto 20%higher success at low speeds, 40% at high speeds, 50% lower trajectory deviation, and 45% fewer re-planning steps. These findings demonstrate how causal inference combined with mobile edge computing enables scalable, reliable, and generalizable autonomy in multi-agent systems
Site Response Analysis Considering Site-Affects Leading to Seismic Microzonation Map of Lahore
No full textSeismic microzonation is performed to assess the seismic risk in an area. In this paper, seismic microzonation for Lahore, Pakistan has been carried out. Firstly, the Geotechnical and geological properties of soils in the region were classified based on 119 boreholes. Two downhole tests were performed to measure the dynamic in-situ properties of soil. The design spectra for Lahore city from BCP 2007 and 2021 were used as target spectra to develop two synthetic acceleration time histories respectively. Afterward, one-dimensional non-linear site response analysis was performed at 33 sites having depth of 30 m for evaluation of parameters such as peak ground acceleration and spectral acceleration at the ground surface. Major seismic hazards considered for the seismic risk assessment are (1) peak ground acceleration at the ground surface, (2) surface spectral acceleration and (3) spectral amplification in the top 30 m of soil. All the major hazards estimated above were also used to prepare a seismic risk map of Lahore. Additionally, two site-specific design spectra were proposed in accordance with the soil classes D and E. The results of this study demonstrate the importance of micro-scale seismic studies to quantify the seismic risks associated with earthquakes
Evaluation of site amplification factors for shallow rock sites of Islamabad, Pakistan.
No full textCurrent seismic provisions in Building Code of Pakistan (BCP, 2007), seismic site classifications and corresponding site amplification factors (AF) were determined similar to UBC-1997, which were based on the local site conditions of Western United States (WUS) with low impedance contrast, deep sites and high seismicity conditions. Use of these deep sites based AF factors to the shallow sites may not be appropriate to capture the response of shallow sites. In this study, we performed suite of nonlinear site specific response analysis to compute the AF for the six (06) representative shallow bed rock sites of Islamabad, Pakistan. The computed AF are compared with BCP, 2007 code-based design estimates, it is found that BCP, 2007 code based guideline underestimate AF factor at short period whereas overestimate at long period. The findings of the study, highlights the potential implications of current code based AF for the shallow sites of Islamabad, and suggest the need of improving the current seismic guidelines
COVID-19 impact on hematological and biochemical parameters on outcomes of admitted patients
No full textCOVID-19 an ongoing pandemic has high transmission and mortality rate gets the attention of researchers to focus on the disease. The burden of disease on the health care system focuses on the COVID-19 impacts on the hematological and biochemical parameters. This study aimed to focus on the laboratory indicators that fluctuate in COVID-19. The single-center cross-sectional study in the pathology department of POF hospital Wah Cantt from August 2021 to December 2021. Three hundred positive COVID-19 patients were included in the study. About 138 (46%) were males and 162 (54%) females and the mean age was 58 ± 15.06 (range 5 – 86). The biochemical indicator raised in COVID-19 included CPK (191.25 ± 507.39), CRP (68.81 ± 70.95), LDH (429.48 ± 246.96), and ALT (46.50 ± 43.23). In hematological parameters, only neutrophils elevated (70.00 ± 13.52) lymphocytes decreased. Laboratory parameters measured were similar values in recovered and death cases. The findings suggest the raised level of CRP, LDH, CPK, ALT, ferritin, D-Dimer, and neutrophils predict early diagnosis and prompt treatment
Evaluation of design parameters for geosynthetic reinforced-soil integrated bridge system based on finite element analysis
No full textThis study evaluates the performance of a geosynthetic reinforced soil integrated bridge system (GRS-IBS) in terms of total displacement by varying different design parameters simultaneously and also suggests optimum values of them. These parameters include, i. backfill internal friction angle (∅b) and reinforcement spacing (Sv), ii. Backfill internal friction angle (∅b) and geogrid axial stiffness (EA) at varying reinforcement spacing (Sv), iii. Backfill internal friction angle (∅b) and number of bearing bed layers, and the effect of retained backfill slope (mb). Simulations were conducted using PLAXIS 2D software. Analysis showed that the cumulative effect of these parameters had a significant effect on total displacement but after a certain point increase or decrease in their values showed no effect on the results while some parameters showed negligible effect on the deformation of the wall. Furthermore, due to the notable effect of ∅b, Sv and EA on the total displacement of the wall, the impact of these parameters was also investigated on the development of tensile force in the topmost layer of geogrid in GRS IBS. It was noted that the shape of the tensile force distribution graph was the same for all the cases and the order of the parameters in terms of their effect on tensile force was Sv > ∅b > EA. Also, a detailed analysis of tensile force development in all the layers of geogrids showed that if Sv ≤ 0.2 m, the spacing between reinforcement in the lower portion of GRS IBS can be increased as these layers showed approximately zero tensile load
A Hybrid Spatio-Temporal Graph Attention (ST D-GAT Framework) for Imputing Missing SBAS-InSAR Deformation Values to Strengthen Landslide Monitoring
No full textReservoir-induced landslides threaten infrastructures and downstream communities, making continuous deformation monitoring vital. Time-series InSAR, notably the SBAS algorithm, provides high-precision surface-displacement mapping but suffers from voids due to layover/shadow effects and temporal decorrelation. Existing deep-learning approaches often operate on fixed-size patches or ignore irregular spatio-temporal dependencies, limiting their ability to recover missing pixels. With this objective, a hybrid spatio-temporal Graph Attention (ST-GAT) framework was developed and trained on SBAS-InSAR values using 24 influential features. A unified spatio-temporal graph is constructed, where each node represents a pixel at a specific acquisition time. The nodes are connected via inverse distance spatial edges to their K-nearest neighbors, and they have bidirectional temporal edges to themselves in adjacent acquisitions. The two spatial GAT layers capture terrain-driven influences, while the two temporal GAT layers model annual deformation trends. A compact MLP with per-map bias converts the fused node embeddings into normalized LOS estimates. The SBAS-InSAR results reveal LOS deformation, with 48% of missing pixels and 20% located near the Dasu dam. ST D-GAT reconstructed fully continuous spatio-temporal displacement fields, filling voids at critical sites. The model was validated and achieved an overall R2 (0.907), ρ (0.947), per-map R2 ≥ 0.807 with RMSE ≤ 9.99, and a ROC-AUC of 0.91. It also outperformed the six compared baseline models (IDW, KNN, RF, XGBoost, MLP, simple-NN) in both RMSE and R2. By combining observed LOS values with 24 covariates in the proposed model, it delivers physically consistent gap-filling and enables continuous, high-resolution landslide monitoring in radar-challenged mountainous terrain
