Tropical environments accelerate rock weathering, which can significantly reduce the stability of mineralized slopes. This study investigates the influence of weathering and groundwater conditions on the stability of a strong–weak–strong multilayered rock slope hosting an iron deposit in the Philippines, where intense rainfall and highly fractured rock masses are common. Detailed geological and hydrogeological investigations were conducted to construct two-dimensional numerical models of the slope system. Mechanical characterization of both weathered and unweathered rock masses was performed using field hardness testing and laboratory geomechanical tests. These parameters were incorporated into numerical simulations to evaluate slope stability under varying degrees of weathering, weak-layer thickness, and hydrological conditions derived from developed phreatic surfaces. Results indicate that when the middle weak layer is thin, a 50% weathering intensity still allows a stable multilayered slope at an Overall Slope Angle (OSA) of 60°. However, as weak-layer thickness increases, the same weathering degree leads to slope instability. Weathering effects recorded at the investigated study area correspond to an average reduction of 10.8 units in the Geological Strength Index (GSI), and are equivalent to a 50% weathering degree, implying that each 0.2 reduction in GSI represents approximately 1% weathering of the rock mass. Considering weathering-induced strength degradation, the optimally safe slope angle decreases from the originally designed 60° to approximately 35°, maintaining a minimum factor of safety of 1.2. Although the stabilizing effect of other countermeasures was excluded from this study. Hydrological analysis further reveals that low rainfall may temporarily increase slope stability due to matric suction induced by capillary effects in clay-like mineral sericite, although prolonged rainfall generates positive pore pressures that reduce stability. The thickness of the weak layer also produces localized increases in hydraulic gradient due to permeability contrasts, increasing susceptibility to rainfall-induced failure. These findings highlight the importance of quantitatively incorporating weathering and hydrogeological effects in slope design for tropical open-pit mines.
The roles of primary road networks in maintaining regional connectivity, supporting economic activities, and enabling emergency response, especially in disaster-prone areas, are well known. Vulnerability analysis of such networks is commonly conducted following disruption-based scenarios, in which external hazards are modeled to disrupt and degrade network performance. This study adopts a different perspective by considering vulnerability as a property that can also arise from the network’s own topological structure. In this approach, vulnerability is assumed to exist even prior to external disturbances, and thus is an intrinsic characteristic of the network. To explore this idea, a Graph Neural Network (GNN)-based framework is proposed to learn structural vulnerability directly from network topology. The primary road network of Central Java Province, Indonesia, was used as a case study. The network was modeled as an undirected, weighted graph representing two configurations: without toll roads and with toll road integration. Vulnerability labels at the node level were derived from a series of node removal experiments, capturing efficiency loss, connectivity degradation, and network fragmentation. A Graph Convolutional Network (GCN) model with two GCN layers was then trained to learn patterns associated with structural vulnerability based on node features and graph connectivity. The results indicate that structural vulnerability can be learned as a network property, with the model achieving an accuracy of approximately 82% across both configurations. This performance exceeds that of conventional centrality-based approximations. The findings also show that the inclusion of toll roads reshapes the distribution of vulnerability across the network. This supports the interpretation that toll roads act as structural modifiers by reconfiguring systemic vulnerability rather than simply increasing redundancy.
Motorcycles are Indonesia’s predominant mode of transportation and contribute to a substantial share of road traffic accidents. The research aims to identify key behavioural factors contributing to motorcycle-related conflict risks at intersections and to propose evidence-based safety interventions. This study analyzed speed characteristics with varying degrees of disruption that motorists would experience at the intersection. These disruptions were categorized into high, low, no disruption, and left-turn movements. High disruption was defined as vehicles stopping in the middle of the intersection as motorcyclists approached, while low disruption was defined as vehicles preparing to enter the intersection from minor roads. In addition to speed characteristics, this study also observed lane-changing behavior when disruptions occurred at the intersection. To obtain detailed data, drone-based cameras were used for observations, and the results were validated against radar speed measurements. The results show that 67.94% of riders exceed the 60 km/h speed limit during the morning period, while 33.27% exceed it in the afternoon, indicating clear temporal differences in operating speeds. Approximately 85 per cent of lane-changing manoeuvres occur within 20 metres of the conflict point, reflecting short decision distances that increase collision risk. Riders also exhibit assertive gap-acceptance behaviour, often requiring main-road vehicles to adjust their speed. Statistical validation confirms that there is no significant difference between drone-derived and radar-based speed measurements, demonstrating the reliability of the drone-based methodology. Based on these findings, the study recommends targeted speed management measures, including automated enforcement and the use of traffic-calming devices; improved lane discipline through more precise road markings and selective physical separation; and junction design enhancements, such as motorcycle waiting areas and advanced stop lines. These measures can improve motorcycle safety at priority junctions and support more efficient and predictable urban traffic operations.
Water acidity is a critical determinant of rice productivity in tidal swamp irrigation systems. In the Terusan Tengah Irrigation Area, irrigation water is characterized by high acidity, conditions that may adversely impact rice cultivation. Although extensive field measurements have confirmed the presence of acid water within the network, observational data alone cannot disentangle how acidity patterns in tidal systems are controlled by tidal advection or generated by rainfall-driven runoff. Therefore, two contrasting hydrological scenarios rain-free and post-rainfall were simulated to isolate the mechanism governing pH dynamics. This study employs conservative hydrodynamic modelling using HEC-RAS, representing pH as hydrogen ion concentration, to investigate shifts in the controlling mechanisms of acidity under two contrasting hydrological conditions: rain-free and rainfall-affected periods. The simulations were designed to distinguish pH responses driven by tidal transport from those triggered by rainfall. Under rain-free conditions, tidal inflow transports acidic water into the primary canal, causing acidity to accumulate near segments underlain by shallow pyrite layers, but pH partially recovers during ebb tide as the acidic mass is flushed seaward. In contrast, after rainfall, runoff-driven acidity converges toward the estuary and persists even during low tide, as elevated water levels and pseudo-tidal retention inhibit flushing and prolong the residence time of acidic water within the network. This behavior reflects a governing-mechanism transition: tides act merely as distributors of existing acidity during dry periods, whereas rainfall initiates acid generation and retention within the network, concentrated near the estuary. These findings enhance the understanding of acidity dynamics in the Terusan Tengah irrigation network—driven by tidal fluctuations and rainfall—and provide an initial basis for developing adaptive operational strategies to mitigate acidity accumulation and its impacts on ecosystems, crop productivity, and local livelihoods.
The shift from traditional human-driven to autonomous vehicles represents a transformation that is not merely technological, but also social. Currently, the deployment of autonomous vehicles remains largely at level where the human driver continues to be the primary decisionmaker. However, this dynamic is expected to change significantly as higher levels of autonomy are achieved. This study aims to identify the impacts that occur during process transition through emerging trends and theoretical lens of transition studies. The initial stage involved mapping trends in Autonomous Vehicle (AV) research to identify key related topics. This study uses bibliometric analysis of 82 key articles selected through a screening process in a systematic review. VOSviewer and CiteSpace were employed to map the research landscape, identify dominant topics, and trace the evolution of AV studies over time. The analysis highlights both well-established research areas and underexplored gaps. Furthermore, the articles were classified into categories based on transition study dimensions and key actors involedinvolved in facilitating a successful transition. The findings reveal a dominant research focus on users, human factors, and mobility behavior, whereas studies concerning vulnerable road users and the industry transition remain limited. To address this gap, this study investigates the socio-technical factors arising from the identified trends and transition studies while also exploring the roles of key stakeholders. This approach aims to achieve a more balanced perspective. Specifically, the study proposes a novel framework that connects critical actors with core socio-technical dimensions to support a sustainable autonomous ve-hicle transition. This research thus provides a strategic framework designed to guide and accelerate the shift toward autonomous mobility.
Reservoir Operating Rules (ROR) have been well-known in Indonesia as guidelines for reservoir operation that comprise the release rules and rule curves derived from the dependable inflow time series in dry, normal, and wet conditions. However, likely because of the incoherence of the original application concept of ROR, the real performance of ROR in reservoir operation has so far remained unclear. This paper proposes a practical application concept of ROR and evaluates the performance of its implementation for annual storage conservation and periodic demand satisfaction under inflow uncertainty in the integrated system of Segawe Regulating Weir, Wonorejo Multipurpose Dam, and Tiudan Regulating Dam. The release rules for the three hydraulic structures were integrated into a unified “Wonorejo ROR”throughoptimization of water allocation over 36 ten-day operational periods. The performance of the application of Wonorejo ROR was evaluated based on several operational success and failure events in Monte Carlo simulations, for which 1,000 years of unique and independent synthetic inflow time series were generated from the Thomas–Fiering model. Under the application concept of ROR, the release decisions for the three hydraulic structures were made in real-time solely based on the actual water level of Wonorejo Reservoir and the rule curves of Wonorejo ROR. For comparative purposes, the Monte Carlo simulations were carried out to evaluate two alternatives of Wonorejo ROR, namely ROR-D65 and ROR-D80, which had different dry inflow exceedance probabilities. Based on the specified events in the Monte Carlo simulations, ROR-D65 and ROR-D80 resulted in reliabilities of 76.4% and 81.3% for annual storage conservation, and the lowest reliabilities of 73.9% and 52.4% for periodic demand satisfaction, respectively. In addition, other reservoir operational performancesandacomprehensive interpretation oftheproposed applicationconcept of ROR arepresented in this paper.
The increase in global temperature has caused climate change, resulting in changes in the distribution of rainfall patterns, seasonal shifts, changes in water availability, and water scarcity. At present, water scarcity in Semajid watershed in Pamekasan Regency is increasing with climate change. Water scarcity will be increasingly difficult to predict due to highly complex dynamics of atmospheric circulation and local climate phenomena such as El Niño-Southern Oscillation (ENSO). This research aims to develop an assessment model to evaluate the impact of climate change on water scarcity using the Semajid watershed of Pamekasan Regency as a case study. The prediction of water scarcity is based on atmospheric circulation dynamics data from the General Circulation Model (GCM-MIROC5) under different climate change scenarios namely Representative Concentration Pathways (RCP). A statistical downscaling model was developed to overcome the limited resolution of the GCM output. The rainfall prediction model was developed using a deep learning-based downscaling model i.e. Long-Short Term Memory (LSTM), while streamflow or water availability prediction was conducted using the Soil Water Assessment Tools (SWAT) model. The Standardized Precipitation Index (SPI) and the Water Scarcity Index (WSI) were used to assess water scarcity. The results showed that the LSTM-based downscaling model provided satisfactory rainfall predictions under different climate change scenarios (RCP) with a reliability average of R2 = 0.741. The SWAT model results also provided satisfactory predictions of water availability with an average reliability of R2 = 0.668. The assessment of water scarcity using SPI and WSI indices showed that water scarcity ranged from moderate to high levels and coincided with the occurrence of El Niño events. Overall, this study demonstrates that the integration an LSTM-based rainfall downscaling model and the SWAT hydrological model can be used as an effective tool to predict water scarcity in the Semajid watershed.
Concrete is the most widely used construction material due to its versatility and ability to be molded into various shapes. However, it inherently exhibits little tensile strength, limited ductileness, and poor crack resistance, often leading to brittle failure. To address these limitations, modern construction increasingly incorporates fibers into concrete to enhance its mechanical properties, durability, and overall performance. Among various fiber types, steel fibers have demonstrated superior crack resistance and improved structural behavior. This study focuses on evaluating the flexural strength behavior of Steel Fiber Reinforced Concrete (SFRC) using M30 grade concrete. An experimental program was conducted involving the casting of 180 prisms (100 × 100 × 500 mm) and 360 cubes (100 × 100 × 100 mm) with steel fiber contents of 1%, 1.5%, and 2% and aspect ratios of 50, 60, and 70. The fiber used had a diameter of 1 mm. The experimental program was limited to evaluating the mechanical performance of the concrete using compressive strength, flexural strength, and splitting tensile strength tests. Special tamping. micromechanical analysis and different workability methods have been omitted. The results reveal that incorporating steel fibers significantly enhances the mechanical properties of concrete. Notably, a mix containing 1.5% steel fibers with an aspect ratio of 70 exhibited the highest strength improvements across all tests, including an 18% increase in compressive strength, a 35% increase in split tensile strength, and a 36% increase in flexural strength compared to control specimens. These findings demonstrate that optimized steel fiber reinforcement not only improves flexural behavior but also contributes to superior structural integrity, making SFRC a promising material for high-performance construction applications.
Structural Health Monitoring (SHM) is crucial for maintaining the sustainability and safety of civil infrastructure. The Z24 Bridge in Switzerland remains one of the benchmark datasets used to validate vibration-based damage detection methods. Traditional approaches based exclusively on modal parameters are frequently limited by data scarcity and environmental variability. Recent advances in artificial intelligence have enabled data-driven neural networks to learn discriminative features directly from raw measurements. Meanwhile, hybrid methods such as Physics-Informed Neural Networks (PINNs) incorporate governing physical laws into the learning process. This study presents a comparative analysis of three successive artificial Neural Network models (NN V1–V3) and One Physics-Informed Neural Network (PINN V1), all applied to the Z24 Bridge dataset. The NN models progressively improve in depth, optimization strategy, and regularization, achieving ≈97.7% validation accuracy and a macro AUC ≈1.00 with NN V3. However, they remain completely dependent on the quality and quantity of training data. In contrast, the PINN incorporates the differential equation of a damped oscillator into its loss function, balancing a data-driven term with a physics-based residual. This approach enables more stable learning with limited labeled data and ensures consistency with structural dynamics. Experimental results highlight the trade-off between accuracy and robustness: while NN V3 yields the highest predictive performance (≈97.7% validation accuracy, macro AUC ≈1.00), PINN V1 achieves slightly lower accuracy (≈92%) but offers improved stability and interpretability. This dual perspective demonstrates that hybrid physics-informed models provide a more reliable basis for decision-making in SHM. The findings underscore the potential of combining machine learning with physical knowledge, paving the way for future developments such as hybrid PINNs (HPINNs), multi-sensor integration, and high-performance computing deployment.
Enggano Island is situated above the southern segment of the Sunda megathrust, making it highly vulnerable to earthquake and tsunami hazards. In remote coastal villages, such as Kaana, the lack of adequate evacuation infrastructure presents significant challenges for disaster risk reduction. This study aims to evaluate tsunami evacuation strategies using an agent-based modeling approach implemented in a three-dimensional simulation environment. A purposive sampling survey involving 83 residents was conducted to collect socio-demographic data, tsunami awareness, preparedness levels, and evacuation preferences. These inputs were used to calibrate agent behavior and movement patterns to reflect realistic community dynamics in the simulation. The model simulates multiple evacuation configurations to examine survival rates and evacuation times under different spatial layouts, building distributions, and shelter capacity assumptions. Results show that horizontal evacuation via a single inland route leads to severe congestion and low survival outcomes, with only 8.2% of agents reaching safety within ten minutes. In contrast, the addition of vertical evacuation buildings significantly enhances evacuation performance, yielding survival rates above 90% under all conditions. Even when shelter capacity is limited to 70% of its full design, over 93% of agents are still able to evacuate successfully, although with increased delays. Vertical-only evacuation produces stable performance with average completion times of approximately five minutes. These findings emphasize the importance of integrating vertical shelters in strategic locations, optimizing route accessibility, and adapting building capacity to physical and demographic constraints. This study contributes to tsunami risk mitigation planning by offering empirical insights into evacuation dynamics in isolated island environments such as Enggano Island, Indonesia.
Enggano Island is situated above the southern segment of the Sunda megathrust, making it highly vulnerable to earthquake and tsunami hazards. In remote coastal villages, such as Kaana, the lack of adequate evacuation infrastructure presents significant challenges for disaster risk reduction. This study aims to evaluate tsunami evacuation strategies using an agent-based modeling approach implemented in a three-dimensional simulation environment. A purposive sampling survey involving 83 residents was conducted to collect socio-demographic data, tsunami awareness, preparedness levels, and evacuation preferences. These inputs were used to calibrate agent behavior and movement patterns to reflect realistic community dynamics in the simulation. The model simulates multiple evacuation configurations to examine survival rates and evacuation times under different spatial layouts, building distributions, and shelter capacity assumptions. Results show that horizontal evacuation via a single inland route leads to severe congestion and low survival outcomes, with only 8.2% of agents reaching safety within ten minutes. In contrast, the addition of vertical evacuation buildings significantly enhances evacuation performance, yielding survival rates above 90% under all conditions. Even when shelter capacity is limited to 70% of its full design, over 93% of agents are still able to evacuate successfully, although with increased delays. Vertical-only evacuation produces stable performance with average completion times of approximately five minutes. These findings emphasize the importance of integrating vertical shelters in strategic locations, optimizing route accessibility, and adapting building capacity to physical and demographic constraints. This study contributes to tsunami risk mitigation planning by offering empirical insights into evacuation dynamics in isolated island environments such as Enggano Island, Indonesia.
Reinforced Concrete (RC) structures, though strong and economical, may need to be strengthened due to increased load demand for upgraded room functions. Strengthening an RC beam element with Glass Fiber Reinforced Polymer (GFRP) offers flexural strength enhancement, corrosion resistance, and cost efficiency. However, the study that considers the full-scale dimension of a beam strengthened with GFRP is still limited. Therefore, more studies on the flexural strength enhancement of RC beams with GFRP need to be conducted. This research investigated the flexural performance of full-scale RC beams strengthened with externally bonded GFRP. This study involved testing five beam specimens, each with a different number of GFRP layers attached to the outermost tensile zone of the cross-section. Flexural testing was conducted using a four-point bending setup with a loading–unloading scheme to capture the specimens’ elastoplastic behavior, considering recovery during unloading. The analyzed parameters included stiffness, yield strength, debonding strength, ultimate strength, and ductility. Furthermore, the flexural strength was predicted through analytical calculations based on the fiber section method, while the shear strength was estimated following the ACI 318M-14 code. The experimental results showed that GFRP strengthening considerably increased stiffness and first flexural strength of RC beams as a proportion of the number of layers during the pre-debonding state. Despite the debonding occurrence initiating a temporary lapse in the role of GFRP at 0.67% to 0.93% of displacement-span-ratio, it decreased the flexural resistance momentarily. Then, the strengthened beams with two-to-four-layer GFRP still exhibited second ultimate flexural strength enhancement within the range 14.35% to 39.22%. Furthermore, GFRP strengthening generally preserved beam ductility at the second ultimate flexural strength due to the catenary action from debonded GFRP in the plastic hinge zone. Thus, additional GFRP for strengthening RC beams could be effective in the case of a positive bending moment to enhance the stiffness, strength, and ductility
Bulukumba Regency is renowned for its rich cultural heritage and diverse tourism potential, positioning it as a prominent destination at both national and international levels. However, limited transportation infrastructure—particularly the absence of a local airport—presents significant accessibility challenges. Travel from Sultan Hasanuddin International Airport to Bulukumba requires approximately six hours by land, which hinders tourism development. This study aims to address this gap by: (1) identifying the most suitable location for a tourism-focused airport using the Analytical Hierarchy Process (AHP), and (2) generating a spatial suitability map for potential airport sites through GIS-based analysis. A weighted hierarchical quantitative approach was employed, using selection criteria based on the Indonesian Ministry of Transportation Regulation No. PM 55/2023 outlines seven key aspects and associated sub-criteria for airport site selection. Each criterion was assigned a weight reflecting its relative importance, followed by AHP analysis to determine the priority values of sub-criteria. The resulting weights were integrated into a Geographic Information System (GIS) to conduct spatial overlay analysis and identify optimal locations. The analysis identified Ara Village and Caramming Village as the most suitable locations for a tourism airport, with the highest composite score (226). Three alternative airport site maps were also produced, offering spatial options for future development. This study provides crucial insights to improve regional connectivity and support sustainable tourism growth in Bulukumba Regency.
Adobe remains an essential construction material for rural housing in the Andean highlands, yet its performance is limited by high water absorption, insufficient durability, and moderate mechanical strength. This study systematically evaluated the effect of combined coconut fiber and expired cement additions on the mechanical, hygrothermal, and economic properties of adobe blocks and walls. Soil was characterized, and blocks were reinforced with coconut fiber (0.6%, 0.9%, 1.2%, 1.5% by weight) and expired cement (3%, 6%, 9%), both by weight of dry soil. A total of 135 samples underwent tests for compressive, tensile, and flexural strength, water absorption, thermal conductivity, and wall compressive performance. Statistical analysis using ANOVA confirmed highly significant improvements (p < 0.0001) across all evaluated properties. The optimal mixture, comprising 0.9% coconut fiber and 9% expired cement, achieved a compressive strength of 37.86 kg.cm-2, nearly double that of the control sample (adobe without additives), which reached 17.69 kg.cm-2, while wall compressive strength reached 33.9 kg.cm-2. Tensile and flexural strengths increased to 11.43 kg.cm-2 and 19.78 kg.cm-2, respectively; water absorption decreased to 7.82%, and thermal conductivity was reduced to 0.52 W.m-1.K-1. Economically, the improved adobe presented a unit cost of S/ 154.14 per m2 (Peruvian soles), 27% higher than the control sample, although offset by notable gains in durability and overall performance. In summary, the combined use of coconut fiber and expired cement in adobe yields statistically validated improvements in structural, hygrothermal, and economic behavior, offering a practical and sustainable alternative for resilient rural housing in high altitude regions.
Converting sewage sludge into biochar shows promise as an eco-friendly and cost-effective method for remediating pollutants. In this study, aerobic digested sewage sludge was evaluated as a low-cost carbon-based catalyst through a facile one-pot pyrolysis process. The sludge biochar (SBC) was then used as a persulfate (PS) activator for the degradation of Bisphenol-A (BPA). The effect of pyrolysis temperature on the physicochemical properties of SBC and catalytic activity was observed. Then, chemical quenching analysis was carried out to identify reactive species. Increasing the pyrolysis temperature from 350 to 700 °C resulted in an enhancement of the degradation rate constant of BPA from 0.95 × 10-2 min-1 to 8.9 × 10-2 min-1. SBC pyrolyzed at 350 °C (A350), characterized by a high iron content (40%wt) in the form of amorphous Fe (e.g., ferrihydrite) and C=C functional group promoting the radical formation which is dominated by presence of hydroxyl radicals. However, iron in an amorphous form limited the catalytic activity of A350. By contrast, non-radical pathway dominates SBC pyrolyzed at 700 °C (A700) with highest BPA removal as the result of more hydrophobic nature (lower O/C) therefore attracting more BPA and PS to the biochar surface. Graphitic structure of A700 (lower ID/IG) supports the mediated electron transfer pathway for persulfate activation. A pH range of 2–9 and the of inorganic anions (e.g., Cl-,NO3-,SO4-, and HCO3-) had negligible effects on the A700 system. This study introduces a novel approach to the value-added reuse of sewage sludge as an efficient persulfate activator for pollutant remediation with good resistance to water matrices conditions.
Heat-reflective pavement coatings are commonly employed for road cooling and to mitigate Urban Heat Island (UHI) effects by reflecting solar radiation and reducing surface temperatures. However, their cooling efficiency diminishes over time due to abrasion, soiling, UV exposure, and environmental aging, which degrade the reflective polymer layer. As a cost-effective alternative, hot-rolled hydrated lime (HL) applied to pavement surfaces has emerged, forming a light-coloured mineral layer that enhances reflectivity and potentially reduces pavement temperature. This study investigates hydrated lime (HL) as a mineral-based alternative, applied through hot-rolling to form a reflective surface layer that is compatible with conventional asphalt practices. Its performance was evaluated through laboratory thermal simulations (day–night cycling) and abrasion wear testing and compared with three commercial paint-based HRCs: epoxy resin–TiO₂ and acrylic emulsion–TiO₂. The results show that HL coatings achieved surface temperature reductions of up to 21.89 °C compared to uncoated asphalt, exceeding the best-performing paint-based sample (White-AE, 19.29 °C), suggesting that HL has strong potential as an effective reflective coating. This was achieved with a formulation of fine HL particles (No. 400 mesh) at a higher dosage (200 g/m²). In abrasion resistance tests, HL outperformed paint based HRCs, with lower mass losses (0.6–1.3 g vs. 0.8–1.5 g), which was attributed to stronger adhesion and particle embedment. In addition, post-abrasion tests revealed that HL samples retained better thermal stability, with smaller temperature increases (ΔT: 5.9–6.8 °C) than HRCs (ΔT: 6.3–7.2 °C). Based on these outcomes, HL applied at 200 g/m² using fine particles (No.400 mesh) is recommended as the optimal formulation for maximizing cooling performance and surface durability. Overall, these findings suggest that hot-rolled HL is a durable, low-cost, and effective alternative cooling strategy to popular HRCs for UHI mitigation.
his study investigates the potential of custom-formulated epoxy-based concrete repair mortar as an alternative material for structural applications. Conventional commercial mortars, while practical, often exhibit limitations in long-term strength and durability. This research evaluates the mechanical and microstructural performance of epoxy mortar using a self-mixed composition consisting of epoxy resin, cornice adhesive, and silica sand. Three variations were developed based on the ratio of epoxy resin to cornice adhesive (50%, 70%, and 100%), and were labeled as RE0.5, RE0.7, and RE1. A commercial epoxy–cement-based mortar, Sikafloor-81 Epocem (SF), was used as a benchmark for comparison. Specimens were prepared in the form of cubes and prisms and tested at curing ages of 7, 14, and 28 days for compressive and flexural strength. Microstructural characteristics were analyzed using X-ray fluorescence (XRF), X-ray diffraction (XRD), and scanning electron microscopy (SEM). At 28 days, the compressive strength values were 30.88 MPa for RE0.5, 48.56 MPa for RE0.7, 51.84 MPa for RE1, and 18.00 MPa for SF. Flexural strength results at 28 days reached 25.74 MPa (RE0.5), 31.18 MPa (RE0.7), 32.54 MPa (RE1), and 8.32 MPa (SF). Elemental analysis confirmed that the high silica content in the fine aggregate and the presence of calcium sulfate in the filler contributed to a denser and more rigid matrix. Crystalline phase analysis revealed quartz as the dominant structure, and microstructural observations indicated fewer pores and cracks in RE1 and RE0.7 compared to SF. These results indicate that a carefully optimized epoxy mortar formulation can exceed the performance of commercial products such as SF, offering enhanced mechanical strength and improved microstructural integrity for use in concrete repair.
Reinforced Concrete bridges are widely used in highway infrastructure due to their cost-effectiveness and structural redundancy. However, they are highly vulnerable to seismic hazards, particularly in near-fault regions where ground motions exhibit extreme intensity and short-duration energy pulses. Near-fault ground motions are characterized by high-energy velocity pulses with long periods, pulse-like waveforms, and significant peak values, which can lead to severe structural damage. As modern design practices shift toward performance-based design, the vulnerability of bridges under these different types of near-fault ground motions have become an emerging area of interest for researchers and designers. However, a common practice is to assume fixed-base conditions for bridge piers during vulnerability assessments, which may lead to inaccurate results. The effect of assuming fixed-base conditions on the vulnerability assessment of bridge piers remains an open question. This study presents a comprehensive comparative analysis of seismic damage propagation in a simply supported multi-span RC bridge subjected to near-fault pulse-like ground motions with directivity effects. The bridge is modeled under two distinct foundation conditions: fixed-base and flexible-base, with the latter incorporating soil-structure interaction through a pile group foundation. The analytical framework employs Incremental Dynamic Analysis to develop seismic fragility curves, offering a thorough evaluation of the system-level performance. The results reveal that SSI significantly alters the structural response, with median normalized changes of approximately 27% in drift and 30% in base shear. In some cases, the normalized drift demand increased by up to 76.8%, whereas the normalized base shear decreased by up to 51.1%, indicating substantial shifts in deformation and force distribution. These variations significantly affect the energy dissipation capacity of the bridge, which is essential for mitigating damage progression and enhancing seismic resilience.
As sustainability becomes a central focus in the construction industry, the combined use of supplementary cementitious materials and discrete fiber reinforcement offers an innovative pathway to enhance both environmental and structural performance. This study investigates the mechano-microstructural interaction between a dense Ground Granulated Blast Furnace Slag (GGBFS)–based matrix and polypropylene (PP) fibers in reinforced concrete (RC) beams, emphasizing the performance trade-offs among key mechanical properties. The experimental program comprised two phases. First, GGBFS replacement levels of 30% and 45% (by binder mass) were evaluated for compressive strength to identify the optimal matrix. Second, PP fibers were incorporated at 0, 3, 5, and 7 kg/m³ into the selected matrix. Tests under standardized curing conditions measured compressive strength, flexural load capacity, ductility, toughness, and stiffness. Microstructural analysis assessed fiber–matrix bonding quality and crack-bridging mechanisms. The 30% GGBFS mixture achieved the highest compressive strength in the optimization phase. Fiber inclusion produced distinct performance trade-offs: 3 kg/m³ delivered the best combination of strength and toughness, 5 kg/m³ maximized ductility, and 7 kg/m³ yielded the highest initial stiffness but slightly reduced post-peak energy absorption. These findings demonstrate that no single fiber dosage is universally optimal; instead, the choice should be based on prioritizing specific performance criteria. Microstructural observations revealed dense interfacial transition zones and effective fiber anchorage in GGBFS-rich matrices, enhancing crack control and delaying propagation. This study’s primary contribution lies in establishing a clear link between microstructural features and quantified mechanical trade-offs, providing a framework for performance-based mix design. The identified trade-offs also offer direct guidance for performance-based design, enabling engineers to tailor mix compositions to targeted applications such as seismic resilience, deflection-sensitive spans, or impact-resistant members.
