Dissertations/Theses - Department of Civil Engineering

Predicting cargo throughput at the Chittagong port using time series data

2026-01-06T04:04:03Z

Predicting cargo throughput at the Chittagong port using time series data Shamsul Hoque, Dr. Md.; Nadia Binte Mohammad; 0422042401; 626.82450954923/NAD/2025 Accurate forecasting of port cargo throughput is critical for efficient logistics and strategic planning in global trade networks. This thesis presents an integrative study that employs multiple state-of-the-art forecasting methods both in univariate and multivariate settings to forecast total cargo volume at the Chittagong Port of Bangladesh. The methods include Long Short-Term Memory (LSTM) networks, Vector Autoregression (VAR), Seasonal Autoregressive Integrated Moving Average (SARIMA), and Transformer-based models to predict total cargo volume at Chittagong Port, Bangladesh. In addition, the research incorporates key economic indicator which is Gross Domestic Product (GDP) of Bangladesh, Export Cargo Volume and Import Cargo Volume as variables in a multivariate forecasting framework. An innovative component of this study is the incorporation of chaos theory to analyze the intrinsic nonlinear dynamics and sensitivity to initial conditions of the cargo throughput time series. To uncover the underlying data dynamics, chaos theory is applied. By calculating Lyapunov exponents, Hurst Exponent and Entropy analysis, this thesis characterizes the degree of chaos, randomness, and complexity within the cargo throughput series. These non-linear analytical methods also provide an insight into the time frame of our data in terms of forecasting ability. This analysis provides critical insights into the stability and predictability of the system, further guiding model selection and parameter tuning. Moreover, the presence of chaotic behavior justifies the use of deep learning models like LSTM and Transformer, which are more tailored to capturing nonlinearity within the time series compared to traditional ARIMA methods. This research commenced with a thorough exploratory dive into the monthly cargo statistics from Chittagong Port. Initial assessments, drawing upon foundational concepts of chaos theory, indicated that the cargo volume time series is not merely linear but instead displays moderately intricate dynamics. These dynamics are characterized by seasonal fluctuations, shifts and trends, and behaviors strongly suggestive of chaotic processes. Given these inherent complexities, the data clearly call for more sophisticated modeling approaches, as conventional linear methods would likely prove inadequate for capturing such nuanced patterns. To address these challenges, we proposed a hybrid forecasting method incorporating a sophisticated signal processing technique named Discrete Wavelet Transformer (DWT) and two deep learning models- Long Short Term Memory (LSTM) networks and another one is Transformer Network. Multiple settings were used in this research for forecasting. A univariate forecasting framework using individual models (ARIMA, LSTM) to predict cargo volume solely based on its historical values. Concurrently, a multivariate approach is implemented using the VAR model, LSTM model with external economic inputs (GDP, export, and import data). Then finally the hybrid model is introduced to the data for univariate settings. The focus of this thesis is to identify the adaptability of a hybrid forecasting method which DWT-LSTM and DWT-Transformer method, commonly known as Wavenet, which is the most recent analytical method invented. Also, this dual strategy allows for an assessment of model performance in isolated versus integrated settings with various other forecasting models. Moreover, all the models also help identify the additional predictive power of economic indicators. In evaluating the performance of the various models, several metrics are employed, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE). These criteria not only quantify forecast accuracy but also facilitate the comparative analysis between univariate and multivariate configurations. The results show that the forecasting accuracy is the highest for DWT-LSTM (a MAPE value of 5.41%) followed by multivariate LSTM (a MAPE value of 6.32%), DWT- Transformer (a MAPE value of 7.01%), ARIMA (8.88%), univariate LSTM (11.19%) and finally VAR (11.97%). The results consistently through other metrices show that, while traditional ARIMA models provide a baseline level of accuracy, advanced models, particularly the hybrid LSTM approach, offer superior performance over all other models in capturing both long-term trends and short-term fluctuations. The findings from this research offer some meaningful takeaways for port authorities and logistics planners. When forecasting is more accurate, it becomes easier to manage resources, schedule operations, and respond to demand shifts without unnecessary delays or bottlenecks. One particularly interesting insight is the presence of chaotic patterns in the cargo data. This suggests that the system is not just noisy or random rather it may actually follow a complex but deterministic path, which means sudden shifts could happen even when things seem stable. That kind of behavior makes a strong case for using adaptive strategies in port management, especially in environments as unpredictable as global trade. From an academic perspective, this study shows how blending machine learning techniques with tools from chaos theory can open up new ways of modeling complex logistics systems. Comparing univariate and multivariate models revealed that hybrid setups especially when paired with wavelet decomposition can capture more nuance and deliver better forecasts. Adding macroeconomic indicators like GDP, import, and export values added another layer of depth, helping to explain some of the broader trends behind cargo movements. And the chaos analysis, though more abstract, offered a deeper look into the structural behavior of the system over time. In short, this thesis tries to bridge the gap between technical modeling and real-world application. It not only contributes to ongoing research in forecasting methods but also provides actionable insights that could genuinely help improve how ports operate in uncertain and dynamic conditions.

Assessment of seasonal and spatial variability of hydrochemical characteristics of groundwater in Bangladesh

2025-12-30T08:46:40Z

Assessment of seasonal and spatial variability of hydrochemical characteristics of groundwater in Bangladesh Mokhlesur Rahman, Dr. Sheikh; Hamid, Rana; 0423042113; 627.17095492/HAM/2025 Groundwater, a critical resource for drinking, agriculture, and industry in Bangladesh, faces significant challenges to its quality and sustainability due to spatial, depth-related, and seasonal variability. This study evaluates the seasonal and spatial variability of hydrochemical characteristics of groundwater across 11 physiographic regions of Bangladesh. Groundwater data from over 900 wells, covering shallow (<50m), intermediate depth (50 – 200m), and deep (>200m) groundwater, were collected during both wet (August – October, 2020) and dry (March – May, 2021) seasons. Water quality index (WQI) and Irrigation Water Quality Index (IWQI), coupled with statistical tests and hydrochemical analysis, were used to assess variations in water quality and underlying geochemical processes. For hydrochemical analysis, Piper diagram, Gibbs diagram, Gaillardet diagram, and USSL diagram were prepared for each physiographic region. Results indicate silicate weathering is the dominant geochemical process nationwide, with carbonate dissolution, seawater mixing, and redox processes contributing to region-specific variations. Coastal regions, particularly the Delta (Tidal) tract, exhibit poor water quality due to seawater intrusion, resulting in Na-Cl/Na-SO4 facies with high salinity and sodicity, rendering water largely unsuitable for drinking and irrigation. Central and southern floodplains show depth-stratified quality, with shallow groundwater heavily degraded by salinization, arsenic, Fe2+, and Mn2+ mobilization, and over half classified as “unsuitable” by the WQI. Intermediate depth groundwater shows mixed quality, vulnerable to seasonal salinity spikes, while deep groundwater generally offers better potable water with stable Ca-Mg-HCO3 facies. Seasonally, dry periods exacerbate concentrations of EC, TDS, major ions, and trace metals due to reduced recharge and evapotranspiration, deteriorating drinking water quality. However, IWQI remains relatively stable across seasons and shows slight improvement in the dry season due to ion flushing and cation enrichment. These findings highlight the need for targeted management, including enhanced monitoring in coastal zones and shallow depths.

Experimental investigation of thixotropic hardening of selected clays

2025-12-09T04:53:51Z

Experimental investigation of thixotropic hardening of selected clays Shariful Islam, Dr. Mohammad; Raihan, Muhammad; 1018042210; 624.151/RAI/2025 This study investigates the thixotropic hardening behavior of reconstituted clay soils from Gazipur, Savar, and Mohakhali in Bangladesh, examining the impact of thixotropic aging on their strength recovery, deformation characteristics, and microstructural changes over 48 days. Utilizing advanced analytical techniques, including X-ray Diffraction (XRD), X-ray Fluorescence (XRF), and Scanning Electron Microscopy (SEM), this research comprehensively assesses the role of clay mineralogy, particle arrangement, and water content in enhancing the engineering properties of these soils, with significant implications for geotechnical design and construction. The methodology involved collecting both disturbed and undisturbed soil samples using wash boring and Shelby tube sampling techniques. These samples were then naturally dried, ground, and sieved to prepare reconstituted specimens at their respective liquid limits. A series of laboratory tests, including unconfined compressive strength (UCS), triaxial compression, and one-dimensional consolidation, were conducted at various aging intervals up to 42 days. These tests were complemented by detailed mineralogical and microstructural analyses to determine specific gravity, Atterberg limits, and particle size distribution, revealing variations in fines content ranging from 89.6% to 98.63% and specific gravity values between 2.63 and 2.7. Significant findings from the study highlight a pronounced time-dependent strength recovery, especially notable in Mohakhali soil, which demonstrated the highest increase in unconfined compressive strength, escalating from 60.8 kPa to 87.5 kPa. The research introduced and utilized the Thixotropic Strength Ratio (TSR) and Thixotropic Regain Strength Ratio (Bt) to effectively quantify the recovery, capturing the reformation of particle structures and bond enhancement post-disturbance. Triaxial test results showed a remarkable 144% increase in deviator stress under a consolidation pressure of 120 kPa over 28 days. Microstructural analyses via SEM revealed densification and improved particle alignment, enhancing soil mechanical properties. XRD and XRF identified mineral variations influencing strength recovery, with higher illite in Mohakhali soil enhancing cohesion and thixotropy. Elevated alumina and iron oxide further improved particle bonding and strength regain. These findings highlight the critical role of mineralogy and microstructure in thixotropic behavior, providing valuable insights for geotechnical applications in clay-rich environments.

Implementing data-driven machine learning technique to estimate the shear strength of FRP-RC deep beams without stirrups

2025-11-29T06:26:23Z

Implementing data-driven machine learning technique to estimate the shear strength of FRP-RC deep beams without stirrups Mahmood, Dr. S.M. Faisal; Khan, Abid Ahsan; 1018042311; 624.177/ABI/2025 The use of fiber-reinforced polymer (FRP) rebars as a substitute for steel rebars has introduced notable variations in the shear behavior of concrete members. To anticipate the shear strength of FRP-RC deep beams, numerous models, codes, standards, and guidelines have been established. The majority of existing shear design provisions for FRP-RC deep beams are empirically calculated or calibrated based on limited test results. The present study aims at developing a simple yet efficient shear strength prediction model for FRP-RC deep beams without stirrups using machine learning (ML) algorithm that does neither rely on crude assumptions nor require complicated calculations. Twelve ML models,including Linear Regression (LR), Ridge Regression (RR), Lasso Regression (LaR), Decision Tree (DT), K-Nearest Neighbour (KNN),Artificial Neural Networks (ANN),Categorical Boosting (CB), Adaptive Boosting (AB), Gradient Boosting (GB), Light Gradient Boosting (LGB), Extreme Gradient Boosting (XGB), and Random Forest (RF), were developed using a datasetof 245 data consisting of 161 experimental and 84 numerical resultsconsidering all key variables. The performance of the ML models was evaluated using various statistical measures and a comparison among the various design provisions was conducted to assess their effectiveness in shear capacity estimation of FRP-RC deep beams. Results revealed that the ensemble ML models exhibited better performance compared to the single ML models. The superiority of the ensemble models such asXGBoost (XGB), CatBoost (CB), and Random Forest (RF) models was confirmed with an accuracy of 92%, 91% and 90%, respectively significantly outperforming the current design practices and widely used empirical formulas. Among the twelve ML models, the XGBoost model is the most accurate model with the highest coefficient of determination (R^2) of 0.920 and least root mean square (RMSE), and mean absolute error (MAE) of 48.280, and 33.310 respectively.The model interpretation was performed through Feature Importance Analysis (FIA), SHapley Additive exPlanations (SHAP), and Individual Conditional Expectation (ICE) for the ensemble ML models to explain the model output compared with a black box. The Feature Importance Analysis (FIA) revealed that for the XGBoost model, beam depth (d), shear span-to-depth ratio (a/d), and beam width (b_w)were the most influential factors in predicting the shear strength, contributing 56.91%, 16.96%, and 12.84%, respectively. SHAP analysis and ICE plots demonstrated that beam width (b_w), depth (d), compressive strength (f_c^'), longitudinal reinforcement ratio (ρ_f), and modulus of elasticity (E_f) positively influenced the shear strength, while the shear span-to-depth ratio(a/d) had a negative impact.Additionally, the model performance was analyzed using Taylor diagram which further confirmed that the superiority of the XGBoost model. The proposed data-driven ML models demonstrated a high level of accuracy and excellent performance and were superior to the existing shear strength models. Finally, a simplifiedGraphical User Interface (GUI) was developed to aid practicing engineers when estimating shear strength without the need for complicated design procedures.