Fusion Journal of Engineering and Sciences
At Fusion Journal of Engineering and Sciences, our Editorial Board Members (EBMs), Associate Editors (AEs), and external peer reviewers play a pivotal role in maintaining the highest standards of academic excellence. Their expertise and dedication ensure a rigorous and fair peer-review process, helping us deliver impactful research to the global scientific community. Online ISSN : 3105-2193 Print ISSN : 3105-2185
Geomorphic and Hydrological Impacts of River Sand Mining on Recent Floods and Groundwater Decline in Pakistan
This research critically examines the geomorphic and hydrological impacts of riverbed sand mining on flood intensity and groundwater reduction in Pakistan, integrating local data with global perspectives. A thorough review of literature, field reports, and GIS-based analyses, along with case studies from international rivers such as the Mekong, Mississippi, Ebro, Yamuna, Meghna, and Athi, was conducted to assess how sediment extraction alters river morphology and hydrological functions. The findings indicate that excessive sand mining deepens river channels, accelerates bank erosion, disrupts sediment transport, and reduces floodplain storage, leading to geomorphic instability. In Pakistan, rivers within the Indus Basin, including the Ravi, Chenab, Jhelum, Swat, Indus, and Malir, show similar patterns, with deep pits, channel shifts, and loss of agricultural land weakening natural flood defenses. These changes exacerbated the 2022 and recent 2025 floods, resulting in more extensive water overflow, prolonged flooding, and increased damage. Hydrologically, the lowering of riverbeds and disrupted sediment dynamics have weakened groundwater–river connectivity, accelerating groundwater depletion in Punjab, Sindh, and Khyber Pakhtunkhwa. At the same time, climate-related stressors such as extreme rainfall, glacial melt, and rising temperatures further intensify flood risks and water shortages. Despite legal requirements for Environmental Impact Assessments, there is weak enforcement, inadequate monitoring, and widespread informal extraction allowing unsustainable practices to persist. Comparisons with international sediment management frameworks highlight the effectiveness of monitoring, sediment budgeting, and community involvement in mitigating negative impacts. This study underscores the urgent need for integrated, climate-resilient river management, evidence-based policies, and enhanced monitoring strategies to protect Pakistan’s river systems, reduce flood vulnerability, and safeguard vital groundwater resources
Copyright (c) 2026 Fusion Journal of Engineering and Sciences https://creativecommons.org/licenses/by/4.0
Study on the Hydrodynamic Effects of Vegetation in Spur Dike Induced Flow
This study investigates the hydraulic influence of localized vegetation introduced upstream of a spur dike in a rectangular channel using a validated computational fluid dynamics (CFD) model. Two scenarios were simulated: one without vegetation and another with a rigid vegetative patch positioned upstream of the spur dike. Analysis focused on flow distributions to assess the impact of vegetation. Results showed that the vegetation considered resulted to a consistent reduction in flow velocity and wall shear stress along both channel banks. The vegetative resistance weakened recirculation zones, attenuated shear layers, and redistributed momentum across the flow domain. Additionally, the presence of vegetation modified the flow structure by promoting more uniform velocity gradients and dampening turbulence intensity near the bed and banks, especially around the spur dike nose. This altered hydrodynamic behavior contributed to a noticeable decline in erosive forces, particularly in zones vulnerable to scour. The simulation outcomes underscore the potential of strategically placed vegetation in reducing local scour and improving bank protection. These findings demonstrate that even limited vegetation placement can effectively reduce erosive forces and enhance flow stability around spur dikes. The study supports the use of targeted vegetation as a nature-based solution in hybrid river training strategies, contributing to sustainable river engineering and flood management practices.
Copyright (c) 2025 Fusion Journal of Engineering and Sciences https://creativecommons.org/licenses/by/4.0
Cost Effective Nature Based Solutions for Urban Water Quality Improvement in Lahore, A Comparative Review
Rapid urbanization and inadequate wastewater management have intensified water quality degradation in major cities of developing countries, particularly in Lahore, Paki-stan. Conventional centralized treatment systems often fail to deliver sustainable perfor-mance due to high capital costs, energy dependence and institutional constraints. This study presents a systematic narrative review of cost-effective and sustainable urban water quality improvement methods, with a primary focus on Lahore while maintaining broad-er applicability across Pakistan. The review evaluates nature-based solutions, low-cost technological interventions, decentralized treatment systems and policy-oriented ap-proaches using a multi-criteria framework encompassing treatment efficiency, cost-effectiveness, environmental sustainability, scalability and contextual suitability. Comparative analysis indicates that constructed wetlands, biochar-based adsorption sys-tems, solar and photocatalytic disinfection technologies and community-based decentral-ized systems consistently outperform conventional centralized treatment plants under local socio-economic and environmental conditions. Constructed wetlands show high reclamation rates for organic pollutants, biochar systems demonstrate strong heavy-metal removal efficiency and solar-based disinfection offers effective pathogen control under fa-vorable climatic conditions. The findings highlight that no single technology is universal-ly optimal; instead, integrated hybrid treatment configurations aligned with Integrated Urban Water Management principles provide the most viable pathway for sustainable urban water quality improvement. This review offers practical insights for urban plan-ners, policymakers, and water managers seeking resilient, low-cost solutions for rapidly growing cities such as Lahore.
3rd International Conference on Climate Change and Emerging Trends in Civil Engineering CCETC-2026
Evaluation and Improvement of Urban Unsignalized Intersection Safety: A Case Study of Latakia City, Syria
This paper assesses and compares the state of safety on traffic at major urban and unmarked traffic intersections in Latakia City, Syria. This paper is to shed light on the international safety foundations, the level of their application in the chosen local intersections, and to offer the concrete engineering and operational solutions to the traffic safety improvement and the level of congestion reduction, with references to the idea of the sustainable solutions such as solar- powered traffic lights. The approach consisted of embracing the existing safety standards, including the ones of the MUTCD, and conducting an in-depth field research in Latakia. Some major safety gaps were as follows: the absence of adequate sign maintenance, insufficient pedestrian amenities and inoperative electrical in-frastructure, which are some of the contributing factors to key operational and safety risks at major crossroads. Among the recommendations, there should be prompt signage repairs, enhancement of infrastructure (pedestrian tunnels, ramps), and introduction of solar-powered traffic control systems.
An Integrated Hybrid Methodology for Net-Zero Energy Residential Design in Rural Sindh
The decarbonization of the building sector should be approached in a twofold manner that includes the reduction of operational energy consumption and the removal of carbon in the building materials. The paper gives a cumulative evaluation of a net-zero energy residential structure in Garhi Yasin, Sindh, Pakistan, involving active solar energy technologies and passive and low-carbon construction. The 3.6 kWp grid-connected system with battery storage was simulated with PV*SOL premium 7.5 and will satisfy 35.3% of the annual electrical demand (3,324 kWh of 8,608 kWh) with self-consumption of 99.6% and a payback period of 10.4 years. At the same time, a comparative cradle-to-gate Life Cycle Assessment was done on material substitution with traditional concrete blocks, cement plaster and steel reinforcement being substituted with clay bricks, lime plaster and bamboo. This replacement resulted in a 73.01% reduction and saved 27,597 kg of CO2 per 150 m2 house. The integrated solution can be shown as a model that can be replicated and adapted all around the world to residential buildings in the semi-arid zone, which can be seen as both energy and materials carbon footprint. Such integrated solutions such as updated building codes, financial incentives, and local supply chain development are policy recommendations.
Hydrological Model Evaluation Criteria Comparison
Hydrological models are widely used to support water resources planning, flood and drought assessment, watershed management, and climate change impact analysis. The credibility of such applications depends strongly on how model performance is evaluated against observed data. Numerous statistical performance metrics have been proposed for hydrological model evaluation; however, their mathematical formulations, sensitivity to flow regimes, and interpretability differ substantially. As a result, the choice of evaluation criteria can strongly influence conclusions regarding model adequacy and comparative performance. This review synthesizes commonly used hydrological model performance metrics, including the Nash-Sutcliffe Efficiency (NSE), coefficient of determination (R²), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Percent Bias (PBIAS), Kling-Gupta Efficiency (KGE), RMSE-observations standard deviation ratio (RSR), and Willmott’s index of agreement. For each metric, definitions, formulations, interpretation guidelines, strengths, and limitations are discussed based on classical and recent literature. Particular attention is given to the sensitivity of metrics to high-flow and low-flow conditions, systematic bias, variability, and error magnitude, as well as their suitability for different water resources applications. Comparative analysis highlights that no single metric can adequately capture all aspects of hydrological model performance, and metric-dependent ranking of models is common across studies. The review study emphasizes the importance of multi-criteria evaluation frameworks and application-specific metric selection, especially under non-stationary climatic conditions. Practical recommendations are provided to support transparent, consistent, and meaningful performance evaluation in hydrological modeling and water resources decision-making.
Wireless Sensor Networks in Civil Engineering: A Review
This report reviews recent progress in Wireless Sensor Networks (WSNs) for civil engineering, focusing on structural health monitoring, building energy management, and disaster response. By analyzing twelve key studies, it highlights critical advances in dependability, energy efficiency, and real-time data processing. Frameworks like Depend-SHM enhance fault-tolerant monitoring, while hybrid protocols such as HT-MAC prioritize urgent data in emergencies. Integration with IoT and edge computing improves interoperability and enables intelligent, decentralized analytics. However, challenges persist in security, standardization, scalability, and cost. Future development requires robust fault-tolerance algorithms, multi-sensor fusion, AI-enabled edge processing, and resilient hardware for harsh environments. WSNs are evolving from data collectors to proactive decision-support systems, essential for building smarter, safer, and more sustainable infrastructure. Continued innovation is needed to overcome existing barriers and fully harness WSN potential in civil engineering applications.
Simplified Seismic Out-of-Plane Demand and Capacity Assessment for Mortar-Jointed Bricks Masonry Infills in Seismic Regions of Pakistan
In this paper, a practical, district-scale framework to estimate the seismic out of plane (OOP) demand of mortar-jointed brick infills in Pakistan is developed and the demand versus experimental validated capacity is compared. The expression of ASCE 7-16/7-22 OOP is reduced algebraically to an expression with only one coefficient of the district, αdistrict, such that OOP force is computed by simply multiplying αdistrict and WP. Based on the SDS maps of Pakistan in classes A-E, the calculation of αdistrict is presented in this paper to all administrative districts of Pakistan except those soil classes that need site-specific analysis according to BCP 2021. When parametric analysis is performed, the ASCE height amplification term converges after 8 storeys (f ≈1.4 - 1.5 for Ip =1.0; f ≈1.7 - 1.8 for Ip =1.5), justifying an envelope constant for national level rapid screening of infill walls enclosed by RC frame, and it is reasonable to take the constant f-factor for all infills especially for infills after 7th storey. Quasi-static experiment of representative Pakistani infills made of mortar-jointed bricks infill under out-of-plane (OOP) loading were carried out. It was concluded that the Ricci et al. capacity model compared (capacity ratios 0.98) and was used to map capacity against the OOP demand for each district of Pakistan. Comparisons between demands and capacities indicate that in most districts 9-in panels are not safe in high-seismic regions and on weak soils for essential facilities (Ip=1.5). The αdistrict dataset allows high speed screening (Rapid Screening), district-scale vulnerability mapping, and similar insertion of OOP checks into code amendments and standard design practice by practitioners.
Microsimulation-Based Assessment of Traffic Performance and Sustainable Solutions: A Case Study of an Urban Intersection
Traffic jam is one of the most important issues in fast-growing cities, and twin cities of Pakistan is no exception. Traffic Jams lead to traffic congestion problem, which is particularly growing in cities during rush hour, and as a result, it is creating additional delays, emissions, and safety concerns. This study evaluated the traffic performance of the intersection of the T-Chowk located at the southern end of the Islamabad Expressway with the help of the microsimulation modeling. The analysis is based on traffic count data and generated simulations are used to test the efficiency of the urban intersection and tested various sustainable solutions. The findings of the study showed that the adaptive signal control, expansion of the lanes, and the combination of the intelligent traffic systems (ITS) would significantly enhance the traffic flow which would reduce the effects on the environment and delays.
Comparative Evaluation of Machine Learning Models for Predicting Compressive Strength of Concrete Made with Soap Factory Wastewater
This study explores how machine learning (ML) models can predict the compressive strength of concrete when soap factory wastewater is used as a partial replacement for mixing water. Using a dataset with key mix parameters like cement content, water-to-cement ratio, aggregates, and curing age, five ML models—Gradient Boosting, Random Forest, Decision Tree, k-Nearest Neighbors (kNN), and Linear Regression—were tested. Among them, Gradient Boosting performed best, with the highest accuracy (R² = 0.868) and lowest error. Random Forest also showed strong results, while Decision Tree was moderately effective. In contrast, kNN and Linear Regression struggled to capture the complex relationships in the data. These findings highlight the potential of ML in optimizing concrete mix design and promoting sustainable construction practices.
Environmental Life Cycle Assessment of Recycled Coarse Aggregate and Textile Waste Fibers
The use of textile waste and recycled concrete as alternative construction materials is important for advancing sustainability and encouraging a circular economy. In developing countries such as Pakistan, improper disposal of concrete and textile waste significantly adds toward environmental degradation. This study quantifies the Global Warming Potential (GWP) and Cumulative Energy Demand (CED) associated with the production of Recycled Coarse Aggregates (RCA) and recycled textile fibers. A cradle-to-gate Life Cycle Assessment (LCA) was conducted using the OpenLCA framework, incorporating Ecoinvent and NEEDS databases, and tailored to the Pakistani context with the 2025 energy mix. The results indicate that producing 1 ton of RCA results in a GWP of 8.86 kg CO2e, while 1 kg of recycled textile fiber emits 0.212 kg CO2e. Transportation is the primary energy consumer in the textile fiber sector, accounting for 76% of total demand, whereas processing machinery is the main contributor to RCA-related emissions. Recycled textile fibers exhibit a substantially lower carbon footprint compared to conventional synthetic fibers such as PET, basalt, and macro-synthetic fibers. This study sets an important environmental benchmark, demonstrating that RCA and recycled textile fibers are viable low-carbon, circular material strategies for the local construction sector.
Machine Learning–Based Reconstruction of Missing Streamflow Records in Data-Scarce River Basins
Stable streamflow records are essential in water-resources planning, flood prediction, and effecting climatic change; when plentiful, however, in high-altitude and trans-boundary river basins because of rugged terrain, severe climatic conditions, and insufficient monitoring facilities. The paper fills in missing streamflow records in the Chitral River Basin, a snow- and glacier-fed basin of the Upper Indus Basin, by reconstructing missing discharge at the downstream Arandu station with paradoxical observations of the Chitral station. The statistical and machine-learn models that were used to analyze daily discharge data between 1981-2024 are Linear Regression, Artificial Neural Network (ANN), and Extreme Gradient Boosting (XG Boost). The coefficient of determination (R 2), root mean square error, mean squared error, and mean absolute error were used to assess how well a model works. Although the predictive power of all models was quite robust with R 2 exceeding 0. 95, ANN showed the most balanced and stable performance at both training and testing stages with the lowest prediction errors and best generalization. On these findings, the ANN model was used to recreate missing discharge at the Arandu station to generate a long-run series of streamflow between 1981 and 2024. The restored hydrograph maintains the seasonal snowmelt-based flow regime and fits well with observed records to actually serve as a virtual discharge sensor. The suggested framework is a feasible approach to hydrological reconstruction in mountainous basins with limited data, as well as the enhanced water-resources management in the context of variable climatic conditions
Automating Pavement Health Assessment: Leveraging Machine Learning for Efficient Pavement Crack Detection and Classification
Pavement degradation, including cracks, are a major road maintenance issue and a hazard to the safety of infrastructure. Conventional manual checking systems are time consuming and are subject to human error making automated cracking systems very essential. This research discusses four machine learning (ML) classifiers, including k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Neural Network (NN) and Logistic Regression (LR), as efficient to automate the process of pavement crack detection based on image classification. The models were trained and tested on a set of 360 images (180 cracked and 180 uncracked pavement). The findings revealed that SVM, NN and LR had 100 percent accuracy in the classification of cracked and uncracked pavement whereas the kNN had 97.2 percent accuracy. Although the performance decreased slightly, kNN was found to be very reliable thus it can be effectively used to carry out the task of detecting cracks. Measures of evaluation, such as Area Under Curve (AUC), Classification Accuracy (CA), F1 Score, Precision, Recall, and Matthews Correlation Coefficient (MCC) showed that all models performed excellent. The results indicate the promise of ML in the domain of pavement condition assessment automation, which will result in the improved and less expensive infrastructure management. Future research must aim at extending the models to other more complicated situations, such as multi-class classification and real-time monitoring.
Evaluating Structural Integrity of Green Concrete Modified with Supplementary Cementitious Materials Using Non-Destructive Testing
The growing demand for environmentally friendly construction materials has led to the partial replacement of Portland cement with supplementary cementitious materials (SCMs) such as fly ash and bentonite. This study aims to determine the non-destructive strength of concrete in which cement is blended with fly ash and varying dosages of bentonite. The concrete specimens were created, in which cement was replaced with 0%, 10%, 20%, 30%, 40%, and 50% bentonite, along with a constant dosage of 10% fly ash. The mechanical characteristics of concrete were assessed using non-destructive testing (NDT) methods, such as Ultrasonic Pulse Velocity (UPV) and Schmidt Rebound Hammer test, to determine the strength and surface hardness of the concrete specimens. Study shows that concrete specimens containing 10% fly ash along with 10% bentonite exhibited higher values of pulse velocity and rebound index, which predicts higher compressive strength of concrete due to the formation of dense microstructure and refinement of internal pores of the concrete. However, the specimens containing higher dosages of bentonite and fly ash exhibited a low rebound index, likely due to their porous and loose microstructure. Therefore, non-destructive testing methods can be effectively used for monitoring the structural health of sustainable concrete.
A COMPARITIVE REVIEW OF LINEAR AND NONLINEAR STATIC ANALYSIS METHODS FOR REINFORCED STRUCTURES
This paper presents a comparative review of three major seismic analysis methods used for reinforced concrete (RC) structures: linear static analysis, nonlinear static (pushover) analysis, and nonlinear time-history analysis. The study evaluates their underlying theory, modelling requirements, assumptions, accuracy, and level of computational effort. Published global and regional research was reviewed to assess when each method is appropriate for performance evaluation and design. The findings show that linear static analysis is simple and code-friendly but often unreliable for irregular, taller, or higher–mode-sensitive buildings. Nonlinear static analysis better represents inelastic behaviour, stiffness degradation, and hinge formation but may overlook torsional and higher-mode effects. Nonlinear time-history analysis offers the most realistic predictions of seismic response; however, it requires significant modelling detail, computational resources, and careful selection of earthquake records. This review highlights conditions where simplified methods are acceptable and where nonlinear approaches are necessary for accurate and safe seismic assessment and retrofit decisions.
Use of Waste Marble Powder and Sugarcane Bagasse Ash in the Production of Bricks
Due to their structural stability, thermal drawback and beauty, clay bricks have remained among the most used construction commodities. However, the old methods of producing bricks rely very much on natural clay, which produces severe ecological degradation and drainage of fertile soils. As a solution to these problems, this paper discusses the idea of using waste marble powder (WMP) and sugarcane bagasse ash (SBA) as partial substitutes for clay as a raw material in brickmaking. Compressive strength, water absorption, efflorescence, soundness, and hardness were tested with the mixed proportions made in twenty-five mix combinations with the WMP/SBA contents of 0 to 40 per cent and the specimens were tested in line with the ASTM standards. The experimental results revealed that compressive strength gradually decreased with an increase in the content of waste, particularly at higher content of SBA; nevertheless, water absorption and efflorescence did not surpass the tolerable limits of mixes with the maximum replacement of 30 percent combined. Besides this threshold, the performance reduced drastically, with mixes of 20% SBA and 10% WMP experiencing the highest reduced strengthening. It is interesting to note that the source of WMP in Warsak Road, Peshawar, is local, and this fact also shows that the sustainable production of bricks can be producible in the region. This evidence suggests that even the combination of WMP and SBA by 30 percent can be combined without worsening the structural and durability requirements, which is a positive direction for the conservation of resources, using waste, and building in an ecologically friendly fashion.
Intelligent Modelling of Punching Shear Strength in Fibre Reinforced Polymer Concrete Slabs Using Gene Expression Programming and Machine Learning
Running Title ML prediction of punching shear in FRP slabs Abstract The increasing demand for resilient structures in earthquake-prone and high-impact areas has necessitated the accurate prediction of the punching shear strength of concrete slabs reinforced with FRP (Fiber Reinforced Polymer) and without shear reinforcement. The classical approaches, including physical testing and mathematically derived models, tend to have limitations in terms of both accuracy and speed. The present work carries out the prediction of the punching shear strength of FRP-reinforced concrete slabs by employing various machine learning (ML) techniques like decision tree, random forest, stochastic gradient boosting, and so on. The research conducted involved a thorough study through a dataset comprising experiments on slabs with different design parameters, including thickness, strength of the concrete, type of FRP reinforcement, and ratio of reinforcement, which was then used to train and evaluate the ML-based models. Out of all the models that were put to the test, the GEP (Gene Expression Programming) method attained the utmost R² value equal to 0.86, which is an indication of getting shear strength prediction more accurately than traditional models. Furthermore, the ML models were compared through statistical errors, including RMSE, MAE, and R², with the attained values being within the acceptable range. The analysis of feature importance showed that the parameters slab thickness, concrete strength, and reinforcement ratios were the key players in the determination of the punching shear strength. The present study clearly points to the machine learning potential in reducing the effort and time in punching shear strength prediction, the outcome being better design codes and standards for FRP-reinforced concrete slabs in the future. Keywords Punching shear strength; FRP-reinforced concrete; machine learning; gene expression programming; slab thickness; reinforcement ratio Article Type Research Article Subject Area / Discipline Structural Engineering / Civil Engineering Funding Information None Conflicts of Interest The authors declare no conflicts of interest. Ethics Approval Not applicable. This study does not involve human participants, animals, or sensitive personal data. Data Availability The data supporting the findings of this study are confidential and are therefore not publicly available.
Pavement Rutting and Deformation Performance Analysis through FEM
Rutting represents a severe, cumulative symptom of structural distress in flexible pavements, which occurs due to the accumulation of permanent deformation when subjected to repeated traffic loads and therefore adversely influences the quality of the ride, safety, maintenance life and costs. This study assesses the influence of the pavement layer thickness on the rutting depth using the three-dimensional finite element modeling (FEM) in the ABAQUS computing program. The flexible pavement system was built as a detailed numerical model with realistic material properties, nonlinear constitutive behavior and repeated loading in a static situation. There were nine pavement designs which varied in terms of the thickness of asphalt wearing course and base course, and evaluated stress distribution and permanent deformation. The findings showed that adding the pavement layer thickness has a significant effect on rutting depth, which is reduced due to the spreading of loads and the strain concentrations, especially in the asphalt and subgrade layers. On the other hand, thinner pavement layers had a greater deformation and they experienced faster rates of rutting at the same loading conditions. The results offer useful mechanistic information about the rutting behavior and contribute to the optimization of layer thickness of pavements to increase durability, sustainability and long service performance.
Influence of Petrographic Properties on the Strength and Durability of Dolomite and Limestone for Infrastructure Development
The paper presents the basic problem related with practical application of carbonate rocks in construction: are carbonate aggregates produced from such rocks favorable for building engineering, particularly for construction? (1) the geological-engineering properties of aggregates are presented, (2) the correlation between petrographic and engineering. This knowledge has allowed to assess the usefulness made from dolomite and limestone design and construction structures. petrography was characterized using optical microscopy and scanning electron microscopy (SEM) coupled with energy-dispersive X-ray spectroscope (EDS). Engineering properties were determined in accordance with European and Polish norms and guidelines. Statistical and design calculations were performed using dedicated software. The petrographic properties, and selected physical and mechanical parameters of the aggregates, were tested to show their influence on the unconfined compressive strength and unconfined tensile strength. Strong functional relationships between the unconfined compressive strength, unconfined tensile strength and water absorption, have been observed. The support the sustainable use of locally available limestone resources in Pakistan and provide guidance for material selection in pavements, foundations, and other structural applications. These findings have direct economic implications for building and infrastructure design and promote more efficient and rational use of regional rock resources in the construction industry
An apparatus and procedure for 1-g modeling of press-in assembled open caisson shaft construction in soft urban underground spaces
As metropolises expand, to accommodate infrastructure such as transport systems, ventilation networks, and utilities, underground spaces must be developed. In congested metropolitan cities characterized by soft ground conditions, press-in methods for open caisson shafts have emerged as a preferred construction technique. However, the geotechnical response of surrounding soils especially in heterogeneous grounds remains complex and insufficiently understood during press-in assembled open caisson construction. This paper presents a 1-g modelling apparatus and procedure for simulating press-in assembled open caisson construction. The system replicates vertical sinking and staged excavation processes while allowing detailed observation of soil displacement and settlement. Transparent soil was prepared to replicate silty clay and clay region from silica-based mixtures with matched refractive indices. Soil displacement fields in real time were captured using combined laser assisted imaging with Digital Image Correlation (DIC) system. Results reveal typical behavior in silty clay with deformations localized near the cutting edge and remained comparatively restricted with penetration, indicating partial drainage and a stiffer response. However, clay revealed wider propagation zones and larger displacement magnitudes at deeper penetration, consistent with plastic flow and undrained softening. This apparatus effectively investigated soil-structure interaction during caisson installation, showing the substantial influence of soil strata and penetration depth on ground movements. For future studies it offers a platform for improving predictive modeling, geotechnical design, and risk mitigation in urban underground construction.
Application of the Gradient Boosting Algorithm for Bond Strength Prediction of GFRP bars with Concrete
In marine environment, chloride-induced rebar corrosion is the principal cause of reinforced concrete degradation, resulting in material loss, cracking, and bond failure, all of which dramatically degrade structural safety, load-bearing capacity, and service life. As a result, using non-corrosive reinforcement, such as Glass Fibre Reinforced Polymer (GFRP) bars, is critical for increasing the service life of structures exposed to marine environments. It has a variety of other benefits over steel rebar as well, including, higher tensile strength, cost-effectiveness, reduced density, and non-magnetic property. The bond behaviour of GFRP bars with concrete differs from that of steel, as it exhibits linearly elastic behaviour with a distinct surface deformation pattern. While several empirical models exist to estimate bond strength, their predictive accuracy is limited; therefore, it is imperative to develop a more robust, data-driven prediction model. This study demonstrates the effectiveness of Gradient Boosting-based Machine Learning model, optimized via Optuna with K-fold cross-validation, on estimating the bond strength of GFRP bars with concrete. Analysis of the results indicates that the model yields superior performance metrics across both the training and testing phases, characterized by a higher coefficient of determination (R2) and a reduced Root Mean Square Error (RMSE). As a result, it may be inferred that the model has a higher predictive capacity than empirical equations.
Finite Element Investigation of Stiffness-Controlled Shear Connector Behavior in Composite Slab–Girder Systems
This study presents a three-dimensional finite element investigation of a composite slab–girder system with emphasis on the mechanical response of different shear connector configurations. The numerical model consists of a reinforced concrete slab with a compressive strength of 40 MPa, an I-shaped steel girder, reinforcement mesh, headed stud connectors, and removable bolted connectors. All steel components were initially modeled using A36 steel properties, while higher-strength and higher-stiffness materials were later assigned to the connectors and nuts as part of parametric study. The connectors were uniformly spaced at 150 mm along the span to ensure consistent composite interaction. The analysis focused on stress distribution, force transfer mechanisms, and stiffness-controlled load sharing within the composite system. The results demonstrate that connectors with higher stiffness and strength attract significantly greater stress than more flexible alternatives due to deformation compatibility and stiffness-based force redistribution inherent in finite element formulations. Although stiffer connectors enhance overall composite action, they also introduce localized stress concentrations that may govern fatigue performance or initiate failure. These findings highlight the importance of considering connector stiffness alongside strength in composite design. Overall, the study provides valuable insight into optimizing shear connector selection and supports the development of composite systems that achieve an improved balance between strength, ductility, and stress efficiency. Keywords Shear studs, Composite beam, Finite element analysis, ABAQUS, Shear connectors, Parametric study, Connector stiffness, Structural performance.
Performance Evaluation of Nano-Stabilized Expansive Soils Using Laboratory Testing
Expansive soils are one of the most widely considered problematic soils and are characterized by high montmorillonite content, significant swell–shrink behavior, and severe volumetric changes with moisture variation. These soils cause significant structural damages globally, especially in dry and semi-arid areas. Nanomaterials have been shown to be one of the best and most advanced ways to stabilize soil. This study uses a comprehensive laboratory testing program to examine effectiveness of nanosilica as a stabilizer for expansive soil. The natural soil had a liquid limit of 56.2% and a plasticity index of 29.9%, and categorized as CH by USCS and A-7-6 by AASHTO classification systems. Nanosilica was incorporated at 0.5%, 1.0%, and 1.5% (dry weight) to assess its influence on the physio-mechanical properties of the soil. According to test results, considerable improvement in volumetric stability was observed by adding up to 1.5% nanosilica. The expansion index's decreased from 92.5 to 48.5. The engineering properties gradually improved with the addition of nanosilica. According to direct shear testing, Cohesion and friction angle increased from 32.3 kpa to 62.8 kpa and from 1.10° to 3.14°, respectively. Similarly, unconfined compressive strength increased from 98 kpa to 141 kpa.
Comparison and evolution of satellite-based precipitation product for flood-prone Kunhar River basin, KPK, Pakistan
Abstract Floods are one of the major natural hazards affecting the northern regions of Pakistan. Kunhar River basin lies in the northern territory of Pakistan and is highly vulnerable to flooding due to its steep terrain and heavy monsoon rainfall. Reliable precipitation data is essential for accurate hydrological modeling and flood forecasting. However, ground-based rainfall observations are often limited, making satellite-based precipitation products an important alternative. This study focuses on the comparison and evaluation of different satellite-based precipitation datasets for their suitability in the hydrological modeling of the flood-prone Kunhar River basin. Several global products such as Integrated Multi-satellite Retrievals for the Global Precipitation Measurement (GPM) mission and Tropical Rainfall Measuring Mission (TRMM) analyzed and compared with available ground station data. The selected precipitation datasets were applied in the Hydrologic Engineering Center - Hydrologic Modeling System (HEC-HMS) hydrological model to evaluate their role in simulating rainfall–runoff processes. Statistical performance indicators including correlation, bias, Root Mean Square Error, and Nash–Sutcliffe efficiency were used to assess their accuracy. Integrated Multi-satellite Retrievals for GPM mission was the most suitable satellite-based precipitation product for enhancing flood prediction and water resource management in the Kunhar River basin, KPK, Pakistan.
Numerical Investigation of Flow Hydrodynamics Around the Gap Region of Finite Coastal Vegetation Patches
This study investigates the role of coastal vegetation patches in reducing tsunami damage by addressing the "danger zones" that form between vegetation gaps. These gaps, where the velocity and turbulence of tsunami flows are higher, increase the risk of destruction. The research explores three gap configurations, including straight, inclined, and staggered, by using numerical simulations to evaluate the internal flow properties within these gaps. The Reynolds-Averaged Navier-Stokes (RANS) equations were used in setup with a seven-equation Reynolds stress model (RSM) for turbulence closure. The analysis, conducted using ANSYS Workbench, focuses on the flow dynamics around short submergent and tall emergent mangrove trees. Initially, the model was validated by comparing results with experimental data. The results showed that the inclined gap between vegetation patches (named as Case 2) reduced near-bed velocity by 18.6% and turbulence kinetic energy (TKE) by 40%, while the staggered gap (Case 3) provided the most effective mitigation, reducing velocity by 22.7% and TKE by 41.8%. These findings suggest that adjusting the gap configuration can optimize the effectiveness of coastal vegetation in mitigating tsunami damage. The study holds practical significance for policymakers and coastal engineers, as it provides insight into optimizing vegetation-based coastal defenses to reduce disaster risks, emphasizing the potential of staggered vegetation gaps with a mix of tree types as a promising strategy for enhancing coastal protection and promoting sustainable solutions for disaster resilience.
Flood Vulnerability Assessment of Sujawal Taluka, Sindh, Pakistan: A GIS-Based Multi-Criteria Analysis Approach
Flood vulnerability assessment is critical for disaster risk reduction in deltaic regions prone to recurrent flooding. This study develops a spatially explicit flood vulnerability map for Sujawal Taluka, Sindh Province, Pakistan, using an integrated approach combining the Analytical Hierarchy Process (AHP), Geographic Information Systems (GIS), and remote sensing. Seven flood-conditioning factors (rainfall, distance to river, elevation, slope, land use/land cover, NDVI, and population density) were systematically weighted using AHP (Consistency Ratio = 0.0177) and integrated through raster-based multi-criteria analysis. Results classified Sujawal into five vulnerability zones: Very Low (97.06 km², 13.7%), Low (148.89 km², 20.9%), Moderate (177.21 km², 24.9%), High (216.44 km², 30.5%), and Very High (71.02 km², 10.0%). Validation against 809,863 historical flood events confirmed model accuracy, with 40.45% of floods occurring in the High and Very High zones (collectively 40.5% of area). Population exposure analysis revealed 71,544 persons (36.05%) reside in high-risk zones. These findings provide an evidence-based roadmap for prioritizing flood mitigation investments, emergency response planning, and land-use regulation in this vulnerable Indus Delta region.
Concrete Demolition Techniques: A Global Review with Sustainable and Environmentally Friendly Insights for Pakistan
Concrete demolition is increasingly important due to global infrastructure renewal, urban densification, and the need for sustainable end-of-life management of built assets. Yet in many developing countries, including Pakistan, demolition practices remain labor-intensive, environmentally damaging, and technologically outdated. This review synthesizes global knowledge on mechanical, explosive, hydraulic, chemical, thermal, and robotic demolition techniques, evaluating them through sustainability, safety, operational efficiency, and circular-economy lenses. A systematic review of international literature, technical standards, and policy reports enabled the development of a structured classification framework and performance indicators encompassing cost, time, safety, environmental impacts, and material recoverability. Results show that although mechanical demolition remains the most widely used method, advanced and low-impact approaches such as hydrodemolition, diamond-wire cutting, selective dismantling, and robotic demolitionachieve lower dust and vibration levels, higher worker safety, and improved resource recovery. Emerging innovations, including BIM-enabled planning, AI-based decision support, remote-controlled robotics, and automated waste segregation, offer substantial potential for Pakistan. The review concludes that achieving sustainable demolition in Pakistan requires regulatory reform, workforce capacity building, and strategic investment in modern technologies to reduce environmental impacts and enhance circular material flows.
The Role of SHM in Retrofitting and Life-Cycle Management of Aging Buildings: A Comprehensive Review
Aging building stock and the increasing complexity of urban infrastructures have highlighted the urgent need for effective strategies to assess, maintain, and retrofit civil structures. Traditional inspection methods, including visual assessment and destructive testing, are often insufficient for capturing the subtle and progressive deterioration inherent in aging infrastructure. Structural Health Monitoring (SHM) has emerged as a transformative approach, enabling continuous, real-time assessment of structural integrity through advanced sensing technologies, data-driven analytics, and integration with digital tools. This comprehensive review examines the role of SHM in the retrofitting and lifecycle management of buildings, focusing on technological advancements, methodological frameworks, and practical applications across diverse structural typologies, including bridges, high-rise buildings, and heritage masonry structures. The review critically evaluates sensor systems, including fiber-optic, wireless, and smart sensor networks, as well as data analytics techniques incorporating artificial intelligence and machine learning for automated damage detection and predictive maintenance. Furthermore, the integration of SHM with Building Information Modeling (BIM) and Digital Twin platforms is discussed as a key enabler for lifecycle optimization and retrofit decision support. Challenges in sensor deployment, data management, environmental robustness, and standardization are highlighted, alongside emerging trends and research gaps. The findings underscore SHM’s potential to enhance safety, extend service life, reduce economic risks, and support sustainable infrastructure management, providing a roadmap for future research and practical implementation in civil engineering.
Integration of Structural Health Monitoring with Sustainability Indicators: A Systematic Review Bridging Resilience and Green Construction Goals
The increasing demand for resilient and environmentally responsible infrastructure has driven a shift from conventional structural safety assessment toward integrated, performance-driven sustainability management. Structural Health Monitoring (SHM), traditionally used for damage detection and condition assessment, is increasingly recognized as a key enabler of sustainable infrastructure by providing continuous, data-driven insights across the asset life cycle. This systematic review synthesizes peer-reviewed literature published between 2000 and 2025 using a PRISMA-based methodology to examine how SHM can be integrated with sustainability indicators to bridge resilience objectives and green construction goals. The review traces the evolution of SHM technologies, sensor systems, and data analytics, highlighting their growing role in performance assessment, predictive maintenance, and post-event resilience evaluation for bridges, buildings, and heritage structures. It further examines sustainability indicators relevant to infrastructure systems, including energy performance, embodied carbon, material efficiency, durability, adaptability, and life-cycle impacts, emphasizing their importance in long-term decision-making and asset management. Through thematic synthesis, the review identifies emerging approaches that link SHM data with sustainability outcomes via digital tools such as BIM, digital twins, IoT-enabled monitoring, and machine learning–based predictive models. Persistent challenges are identified, including fragmented research, lack of standardized metrics, data interoperability issues, and barriers to cross-disciplinary adoption. To address these gaps, a conceptual framework is proposed that maps SHM outputs to sustainability indicators through feedback loops supporting adaptive management, life-cycle optimization, and resilience-informed sustainability strategies. Overall, the review establishes a foundation for advancing SHM as a core component of sustainable and resilient infrastructure systems.
Integration of BIM and SHM For Sustainable Management of Existing Structures: A Systematic Review
The integration of Building Information Modeling (BIM) with Structural Health Monitoring (SHM) represents a transformative approach for the sustainable management of existing structures. This review comprehensively examines the evolution, methodologies, applications, and future directions of BIM–SHM systems, emphasizing their potential to enhance structural safety, optimize maintenance, and support sustainability goals. Various integration techniques, including direct sensor-to-BIM linking, database-driven methods, and IoT-based real-time streaming, are critically analyzed, highlighting their advantages, limitations, and suitability for diverse infrastructure types. Application domains encompass bridges, high-rise buildings, heritage structures, industrial facilities, tunnels, and residential developments. The review further discusses the role of artificial intelligence and machine learning in predictive maintenance and automated BIM model updates, as well as the importance of embedding sustainability metrics into digital twins. Key challenges, such as lack of standardization, sensor data management, interoperability, scalability, and human skill gaps, are identified, providing insight into barriers to widespread adoption. Future research directions emphasize autonomous digital twins, robotics and UAV-assisted inspection, blockchain-based data security, and interdisciplinary collaboration. By synthesizing current knowledge and highlighting technological, operational, and sustainability considerations, this review provides a roadmap for researchers and practitioners aiming to advance BIM–SHM integration and realize resilient, efficient, and environmentally responsible infrastructure management.