This study presents a comprehensive scientometric review of cement-less ultra-high-performance concrete (UHPC) with the objective of identifying research trends, key contributors, dominant themes, and critical knowledge gaps in this emerging field. A systematic bibliometric analysis was conducted using the Scopus database, from which 59 peer-reviewed journal articles published between 2014 and 2024 were selected following rigorous screening criteria. Scientometric mapping was performed using VOSviewer to analyze publication trends, keyword co-occurrence, leading journals, influential authors, and active research regions. The findings reveal a sharp increase in research output after 2020, reflecting growing interest in geopolymer-based UHPC due to sustainability concerns. Existing studies predominantly focus on mechanical properties, particularly compressive strength and steel fiber reinforcement, while durability-related aspects such as corrosion resistance, fire performance, and long-term structural behavior remain underexplored. Higher sand-to-binder ratios (up to 0.8) were found to improve packing density and mechanical performance, achieving compressive strengths up to 160.7 MPa, while silica fume contents around 30% enhanced compressive strength by approximately 25% and fracture energy by nearly 50%. The novelty of this work lies in being the first dedicated scientometric assessment of cement-less UHPC, providing a quantitative overview of research evolution while systematically highlighting critical gaps and future research directions to support its effective structural application.
Reliable Digital Terrain Models (DTMs) are crucial for most engineering and environmental applications, especially where accurate elevation is required. While conventional leveling offers high vertical accuracy, it needs long time periods, causing high work costs, particularly for wide regions. GNSS-based methods that provide fast data acquisition may serve as an effective alternative; however, achieving reliable vertical accuracy remains a challenge. Accordingly, this study proposed a practical approach that integrates the Post-Processed Kinematic GNSS technique with Constrained Triangulated Irregular Network (TIN) modeling to improve elevation accuracy. In this method, accurate leveling cross sections distributed along the study area are used as vertical constraints to improve interpolation reliability. The performance of the model is validated using independent cross-section data observed using precise leveling. Statistical analysis demonstrates a strong correlation between generated DTM elevations and leveling data, evidenced by a coefficient of determination (R2) of 0.9915 and a vertical RMSE of 0.0608 m, with residuals mainly within +/- 0.10 m for the majority of observations. The results validated that the Constrained TIN modeling method effectively maintains the accuracy of PPK-derived elevations and decreases vertical discrepancies. The proposed methodology, integrating PPK observations with constrained TIN modeling, achieves reliable decimeter-level vertical accuracy, making it appropriate for various engineering and environmental applications that needs high-precision terrain representation.
Data-driven models offer the computational speed needed for rapid post-earthquake assessment, but their uncertainty estimates must be trustworthy to support safety decisions. This study reveals that Monte Carlo dropout uncertainty for RC frame seismic response prediction is severely miscalibrated: 95% prediction intervals capture only 46.6% of actual responses, meaning Immediate Occupancy assessments under ASCE 41-17 would be unconservative in over half of cases. We address this through post-hoc Temperature Scaling calibration. While a global scaling parameter (T* = 4.40) reduces calibration error by 91.4%, we discover that the optimal calibration factor varies systematically across structural locations: T* ranges from 1.94 at fixed-base nodes to 5.52 at mid-height floors—a 2.8-fold variation that single-parameter approaches cannot capture. This spatial variation reflects physical differences in prediction uncertainty: boundary-constrained nodes exhibit lower uncertainty requiring less scaling, while mid-height nodes dominated by higher-mode contributions show greater uncertainty underestimation. Building on this finding, we propose floor-adaptive calibration using location-specific scaling factors. Compared to global calibration, this approach reduces average calibration error by an additional 62%, with improvements of 61-70% at ground and top floors, where global calibration performs worst. The method requires no model retraining—only a lookup table mapping floor levels to optimal scaling factors. Validation across 12 RC frames (3-7 stories), 2,400 analysis cases, and 35,000+ node-level predictions confirms that spatially adaptive calibration provides more reliable uncertainty estimates across all structural locations, enabling trustworthy confidence intervals for performance-based post-earthquake assessment.
Floods are the most frequent disasters caused by combinations of natural and anthropogenic factors. Given the increasing intensity and frequency of floods, especially in Asian mega-urban regions, effective Disaster Risk Reduction (DRR) strategies are critical. This study presents a comparative evaluation of the national flood risk assessment methods in Indonesia and China, followed by a flood risk map analysis calculated using the Chinese and Indonesian standards for flood risk assessment, specifically for a case study in Bandung City, Indonesia. We found that the Chinese standard method produces a broader spatial identification of high flood risk areas, influenced by rainfall intensity and topography, which better represents pluvial flood risks. Meanwhile, the Indonesian method produces localized high flood risk near rivers, which better represents fluvial flood risks. In the case study of Bandung City, the occurrence of pluvial floods was more dominant than fluvial floods. Therefore, the spatial accuracy of the Chinese method was slightly higher than the Indonesian method. The study emphasizes the importance of a national flood assessment method that balances accuracy, data availability, computational resources, and local/regional characteristics to cope with the increasing risk in urbanized flood-prone areas.
This study aims to develop a practical and accessible approach for evaluating the mechanical behavior of Cementitious Treated Sand (CTS) under passive confinement using Glass Fiber Reinforced Polymer (GFRP) wraps. A method utilizing three GFRP layer configurations was applied to investigate the confinement effect and assess the role of confining stiffness. Path-dependency was analyzed through derived confining pressure rates, and Mohr-Coulomb failure analysis was used to determine shear-strength parameters. Analysis of plastic volumetric behavior revealed that after an initial elastic state, the material dilates upon yielding—activating the confinement mechanism—before recompacting under sufficient confining pressure due to pore structure collapse. Results indicate that the proposed novel constitutive model successfully predicts both axial and lateral stress-strain responses. It accurately represents the nonlinear stress-strain relationship, the transition in volumetric behavior, and the interaction between axial and lateral strains through the proposed dilation formulation. The model incorporates a plastic dilation rate model to capture the dilation-to-compaction transition and demonstrates excellent agreement with experimental results across all confinement levels. This framework provides a reliable analytical tool for designing soil stabilization schemes using passive confinement, offering engineers a practical alternative to conventional geotechnical analysis while enhancing reproducibility, sustainability, and applicability across diverse construction projects.
This study investigates the hypothesis that mineral fillers with distinct surface characteristics, mineralogical compositions, and morphologies exhibit different reinforcement mechanisms in asphalt mastics. Shale and pumice were evaluated as alternative mineral fillers and compared with conventional granite and limestone at 20% and 30% filler-to-asphalt (F/A) ratios by volume. Filler characterization included X-ray diffraction (XRD) analysis, scanning electron microscopy (SEM), specific surface area (SSA), and hydrophilicity coefficient (HC) measurements. Rheological characterization was performed using dynamic shear rheometer, including temperature sweep, frequency sweep master curves, multiple stress creep recovery (MSCR), linear amplitude sweep (LAS), and Glover-Rowe (G-R) analyses. Pumice, dominated by amorphous volcanic glass with the highest SSA (59.18 m2/g), exhibited rutting-dominant modification with the highest complex modulus enhancement (7.3-9.4 times at 30% F/A) and lowest non-recoverable creep compliance. Shale, composed primarily of quartz and kaolinite with layered morphology and moderate SSA (43.00 m2/g), demonstrated balanced rheological response and achieved the longest fatigue life (Nf,5% = 45,200 cycles at 20% F/A). These findings demonstrate that filler-specific reinforcement mechanisms are governed by mineralogical composition and morphology, supporting performance-based filler selection tailored to climatic and loading conditions.
This study presents a reinforcement-learning framework for real-time strain-rate control in Constant Rate of Strain (CRS) consolidation testing to hasten the testing process using the SARSA algorithm. The controller adaptively adjusts deformation rate based on evolving pore-pressure ratio, with a reward strategy designed to maintain an average pore-pressure ratio near 30% to ensure partially drained conditions consistent with CRS theory. Two normally consolidated clays with contrasting compressibility were modeled numerically using a 1-D CRS consolidation model to evaluate learning and testing performance. The results show that the SARSA agent autonomously learns soil-specific strain-rate policies and maintains smooth effective stress trajectories and stable pore-pressure ratio responses. Test duration reductions of 60-75% were achieved depending on soil type. The interpreted compression index (Cc) remains consistent with the baseline CRS values, confirming that reinforcement-learning-based strain-rate control can accelerate testing without compromising data integrity. The study demonstrates the feasibility of reinforcement learning for CRS testing and highlights practical potential for soil-responsive, adaptive strain-rate control. Current limitations include simulation-based evaluation, discretized action selection, and the need for multiple runs to achieve optimal convergence.
Crack-width control is a critical serviceability limit state (SLS) requirement in reinforced concrete (RC) structures, as excessive cracking can compromise durability and accelerate reinforcement corrosion. This study evaluates the accuracy of crack width prediction models within major international design standards. An experimental investigation was conducted on a RC beam subjected to four-point bending, where crack propagation, beam deflections, and reinforcement stresses were monitored throughout the loading process. The measured crack widths were compared with analytical predictions from Eurocode 2 (EN 1992-1-1), DIN 1045-1, and ACI-based formulations. The results indicate that while all evaluated codes capture the general trend of increasing crack width with rising steel stresses under incremental loading, significant discrepancies exist in their predicted magnitudes. In general, it is Eurocode 2 that consistently provides the most conservative estimates, whereas DIN 1045-1 yields slightly lower but also consistent values of the same. Conversely, ACI-based approaches tend to underestimate crack widths at higher load levels. This study highlights the influence of modeling assumptions—specifically those related to bond-slip behavior, crack spacing, and tension stiffening—on the reliability of crack-width predictions. The results provide experimental evidence regarding the reliability and limitations of common predictive methods, contributing to a refined understanding of design rules for the serviceability of RC structures.
The Canary Islands, a volcanic archipelago off the northwest coast of Africa, are frequently exposed to geohazards. The 2021 Tajogaite eruption on La Palma provided an opportunity to assess how volcanic activity and related seismicity influence rockfall dynamics. This study statistically analyzed a dataset of 1,111 road-related rockfall incidents recorded between 2019 and 2024, comparing event occurrence, severity, and lithological distribution across pre-, syn-, and post-eruption periods. Events were classified based on operational descriptors, and linked to geological units to evaluate lithological controls. While the total number of events remained nearly identical before (519) and after (517) the eruption, the normalized rate of rockfall occurrence increased during the eruptive phase. Lithological distributions also differed across periods: altered basalts consistently recorded the highest number of incidents; pyroclasts and colluvium increased syn-eruption likely due to seismic shaking; and fresh basalts declined post-eruption, suggesting prior mobilization of unstable material. This study provides empirical insight into how eruptive processes influence infrastructure-related rockfall hazards on volcanic islands characterized by steep topography and narrow, low-redundancy mountain road networks. Nevertheless, rockfalls also occurred consistently during non-eruptive periods, highlighting the need for continuous slope hazard monitoring in environments such as La Palma where both eruptive and non-eruptive processes threaten exposed infrastructure, population, and high tourist activity.
Bridges are considered critical components of transportation infrastructure and play an integral role in public welfare and economic development. Bridge authorities in Iraq face multiple challenges in maintaining the efficiency and serviceability of the bridge network while developing a maintenance plan within limited budgets. Thus, this study aims to develop a systematic condition assessment methodology as a tool to prioritize maintenance projects and optimize available budgets to enhance the management of bridge networks. For this purpose, the bridge structure is broken down into four components: deck, superstructure, substructure, and accessories, and each component is divided into a number of elements. Bridge maintenance experts were surveyed to assign weights for the identified components and elements using the Fermatean fuzzy Analytic Hierarchy Process (FF-AHP). The weighted averaging approach was then used to aggregate components' condition ratings with expert-determined weights to obtain the overall Bridge Condition Index (BCI) of each bridge. Bridges with the lowest BCI get higher priority for maintenance. The proposed methodology was applied to thirteen bridges in Baghdad to demonstrate its practicality. The results indicate its reliability and capability to evaluate and rank bridges based on their urgency for maintenance. The proposed method would help bridge engineers and policymakers to make informed maintenance investment decisions during the budget allocation process.
In the framework, the sustainable local development of the Adrar region is one of the largest in the Algerian Sahara. The Algerian government has launched a search for useful local substances to cover the need for building materials in the construction sector. However, the Algerian Sahara has a variety of mineral resources, including clays. This work aims to characterize and identify a natural Algerian clay from the Reggane basin (Paleozoic sedimentary basin) in southwestern Algeria. This is for use in the manufacture of ceramic products. For this, numerous analyses were carried out using techniques such as X-ray Diffraction (XRD) to determine the different crystalline mineral phases, X-ray Fluorescence (XRF) to identify the elemental composition, and Infrared Spectroscopy (FTIR) to study the molecular structure along with the geotechnical identification in order to better understand the main properties of this clay. The findings indicated that Reggane clay is silty and highly plastic (21.94-31.7). It contains a mixture of illite, kaolinite, and quartz, in very significant proportions, as well as hematite, orthoclase, and palygorskite. Furthermore, elemental chemical analyses were conducted, and the results showed that the main constituents of this clay are SiO2 (58.19%-61.71%), Al2 O3 (13.32%-13.50%), and Fe2 O3 (6.13%-6.40%). These findings could eventually be used to target applications of this clay in the production of local fired materials.
Cementitious composites play a vital role in construction due to their favourable strength, durability, and workability. Nonetheless, these materials are susceptible to cracking. Although incorporating glass fibres has improved mechanical properties, achieving uniform fibre dispersion remains a significant challenge. The objective of this study was to examine the effect of mixing sequence on the engineering properties and fibre dispersion of glass fibre-reinforced cementitious mortars (GFRCMs). There were four mixing sequences including: four mixing sequences for glass fibre-reinforced cementitious mortars (GFRCMs): (i) S1(fibres were incorporated into dry mortar mixtures), (ii) S2 (fibres were incorporated into fresh mortar mixtures), (iii) S3 (fibres added alongside gradual water addition, (iv) S4 (fibres were included during incremental water additions). This study examined various properties in accordance with the American Society for Testing and Materials (ASTM) standard test methods, including compressive strength, hardened density, setting time, flowability, and flexural strength. Scanning electron microscopy and fibre-distance analysis were also employed to evaluate the fibre dispersion of the specimens. The results indicate that fibre addition reduced the flowability and shortened the setting time of the mortar, whereas improvements in hardened properties depended strongly on dispersion quality. The most uniform fibre distribution was observed in S4 (beta = 0.685), resulting in maximum compressive and flexural strengths of 15.88 MPa and 10.39 MPa, respectively, at 28 days. The strong correlations observed between density and porosity (R2 = 0.8035) and between density and compressive strength (R2 = 0.8184) indicate that reduced void content and enhanced fibre distribution are key contributors to the observed performance gains. This work establishes relationships among mixing sequence, fibre dispersion, and key engineering properties to guide fibre-mixing processes in cementitious composites.
This study evaluates the performance of the Sentinel-2 multi-spectral instrument (MSI) and Landsat-7 Enhanced Thematic Mapper Plus (ETM+) for soil salinity mapping across contrasting agroecosystems in Egypt, with particular emphasis on subsurface salinity conditions (>0.5 m). A multi-stage calibration framework was implemented, in which historical Landsat-5 imagery (1995) was first integrated with field-measured electrical conductivity (EC) data to establish a spectral baseline. This baseline was subsequently applied to Sentinel-2 and Landsat-7 imagery acquired in 2015 and validated using in-situ total dissolved solids (TDS) measurements. Among the evaluated spectral indices, Salinity Index 5 (SI5) demonstrated the strongest relationship with field data and was selected for salinity mapping. Comparative analysis revealed that Sentinel-2 significantly outperforms Landsat-7, achieving a higher predictive accuracy (R-2 = 0.89) compared to Landsat-7 (R-2 = 0.72), primarily due to its finer spatial resolution (10 m) and reduced mixed-pixel effects. In addition, the application of second-degree polynomial regression substantially improved model performance relative to linear approaches, confirming the non-linear nature of soil salinity-spectral relationships. The results further indicate that surface spectral indices can provide meaningful estimates of subsurface salinity under specific environmental conditions. Overall, the integration of multi-temporal satellite data, robust spectral indices, and non-linear modeling provides an effective framework for soil salinity assessment in arid environments. This approach enhances the reliability of remote sensing-based monitoring and supports sustainable land management in salinity-affected regions.
Design errors remain a persistent challenge in infrastructure delivery, particularly when strategic errors introduced during early design stages propagate into later project phases. This study develops a Building Information Modeling (BIM)-integrated stage-gated framework to mitigate strategic design errors across the infrastructure design lifecycle. The proposed approach embeds interdisciplinary coordination, iterative model federation, and structured verification checkpoints throughout conceptual, preliminary, and detailed design phases. The framework was implemented through a BIM workflow using Civil 3D, Revit, and Navisworks and applied to the Al Najaf Airport Road project in Iraq as a case study. A standards-based geometric and functional assessment was conducted to evaluate both the baseline design and the redesigned solution developed through the proposed framework. The analysis revealed that the baseline design satisfied only 39% of the evaluated design criteria, indicating significant geometric and operational deficiencies. After applying the BIM-integrated framework, the redesigned scheme achieved full compliance with the evaluated standards while eliminating previously undetected coordination conflicts. Model-based analyses also enabled targeted traffic and drainage assessments, helping identify and mitigate potential risks such as flooding susceptibility and unsafe junction configurations prior to construction. The findings demonstrate that early and continuous BIM integration can function as a proactive design assurance and risk management mechanism rather than a late-stage coordination tool. The proposed framework contributes a structured methodology for preventing strategic design errors and improving reliability in BIM-enabled infrastructure projects.
Road maintenance costs play a critical role in government budgeting, as they represent a recurring expenditure required to sustain transportation infrastructure performance and traffic safety. Accurate cost prediction enables long-term efficiency by ensuring that maintenance budgets are allocated appropriately. This study aims to develop a predictive model for road maintenance cost using the Extreme Gradient Boosting (XGBoost) algorithm, optimized through iterative training to improve prediction accuracy based on deviations between predicted and actual costs. Model performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R2), all of which indicate a strong model fit and high predictive reliability. The model was developed using simulated and empirical data from 30 road sections with varying characteristics, incorporating key predictors such as road length, cold mix asphalt, asphalt emulsion, diesel fuel, gasoline, water consumption, working area, asphalt removal volume, and labor requirements. The results demonstrate that the proposed XGBoost-based model can effectively estimate maintenance costs and associated resource requirements. The findings provide practical insights for government agencies in planning material usage and workforce allocation for road maintenance activities.
FRP bars have been utilized widely to replace steel bars in concrete beams due to their excellent corrosion resistance. Therefore, this paper aims to propose an efficient procedure based on artificial neural network (ANN) and reliability analysis to predict the moment capacity, the failure modes, and the resistance reduction factor for the design of concrete beams reinforced by FRP bars. In particular, 200 FRP RC beams are collected to train and verify the ANN model. In addition, a source code based on the Monte Carlo method is developed in MATLAB for the reliability analysis. The ANN model and the Matlab code are integrated to determine the failure probability, the reliability index, and the resistance reduction factor of FRP RC beams by rigorously considering the uncertainty of numerous variables. According to the findings of this study, ANN can be applied to predict the ultimate moment of FRP RC beams well since the mean and CoV of the model error are only 0.98 and 0.12, respectively, which are better than those obtained from ACI 440.1R. Furthermore, the resistance reduction factors for the design of FRP RC beams by ANN can be taken as 0.65 corresponding to the target reliability index of 4.0.
This study aims to enhance the mechanical and thermal performance of lightweight expanded clay aggregate (LECA) concrete through geopolymerization using iron oxide (Fe2O3) and polyethylene glycol 400 (PEG400), combined with surface treatment of LECA aggregates. An experimental program was conducted to evaluate workability, density, water absorption, compressive strength, splitting tensile strength, and bulk electrical resistivity (BER). Various mixtures with different proportions of Fe2O3 and PEG400 were prepared with and without aggregate surface treatment. The findings indicate that surface treatment significantly improves the interfacial transition zone, resulting in enhanced overall performance. The optimal mix (treated LECA with 3% PEG400 and 20% Fe2O3) achieved a compressive strength of 45 MPa and a splitting tensile strength of 4.0 MPa, representing increases of over 70% compared to the control mix. Additionally, water absorption decreased by 35.6%, while BER increased by 127%, reflecting improved durability and reduced permeability. Workability was also enhanced, with up to a 100% increase in slump without compromising strength. The novelty of this study lies in the synergistic integration of treated LECA, PEG400, and iron oxide within a geopolymer matrix to produce a high-performance, durable, and thermally efficient lightweight concrete. This approach offers a sustainable solution for advanced construction applications.
Dam sedimentation poses critical environmental and operational challenges worldwide, requiring sustainable valorisation strategies. This study investigates how post-calcination cooling protocols influence the pozzolanic performance of Ksob dam sediments (Algeria) as a partial cement replacement in self-compacting concrete (SCC). Raw sediments were calcined at 750 degrees C for 5 h and subjected to three cooling methods: water quenching (WQCS), air cooling (ACCS), and slow furnace cooling (SCCS). Ten SCC formulations were prepared with 10%, 15%, and 20% cement substitution rates. Despite the reduced binder content, all mixtures maintained self-compacting properties (spread: 700-735 mm; T500: 1.06-1.39 s) with moderate superplasticiser adjustment, up to 1.2% of binder mass. WQCS formulations exhibited superior performance: at 10% substitution, compressive strength reached 97% of the control at 180 days, while water absorption and permeable porosity decreased relative to the control by 7.1% and 1.9%, respectively. TGA/DSC analysis attributed these gains to enhanced pozzolanic C-S-H formation. These findings demonstrate that cooling kinetics critically govern the mineralogical transformation and reactivity of calcined sediments. Water quenching proved optimal for producing high-performance, eco-efficient SCC, offering a viable pathway for large-scale dam sediment valorisation while lowering the cement industry's carbon footprint.
This research is focused on examining the influence of crack position on slope stability, pore water pressure dynamics, and groundwater level rise induced by prolonged rainfall using seepage analysis. The adoption of a novel integrated method that coupled finite element (SEEP/W) and limit equilibrium (SLOPE/W) analyses led to the introduction of a systematic methodology for evaluating the effectiveness of horizontal drains specifically designed to suit pre-existing crack locations. The results showed that surface cracks in unsaturated soil triggered pore water pressure build-up and groundwater rise, forming localized saturated zones at crack tips after 40 days of continuous rainfall (6 hours/day). Horizontal drains significantly improved stability when positioned near cracks, increasing safety factors by approximately 13% and 6% for slope-surface and mid-slope cracks, respectively. However, it proved ineffective for drains located away from the slope edge. The main novelty centered on quantifying the location-dependent efficacy of drains, establishing a critical zone of influence for optimal drain placement. In addition, the conventional one-size-fits-all method adopted for drain installation was disputed. Field validation using Electrical Resistivity Tomography (ERT) showed that deeper rainwater infiltration realized through cracks during wet seasons supported numerical predictions. The results obtained were in line with the critical role of crack location in drain design. These also provided actionable, location-specific insights for landslide reduction on cracked slopes.
Post-earthquake reconstruction raises two governance questions that are rarely addressed jointly. Whether affected provinces receive allocations proportional to measured damage, and whether physical delivery keeps pace with official plans, remain open in the empirical literature. This study addresses both the 2023 Kahramanmara & scedil; sequence, which affected eleven Turkish provinces and generated recovery needs of approximately USD 103.6 billion. Two rule-based diagnostics are specified, the Damage-Aid Alignment index, which combines Spearman rank correlation with Theil T divergence, and the Reconstruction Pace Index, a monthly delivery-to-plan rate governed by a pre-specified run rule. Both diagnostics operate on an author-compiled corpus of 15,928 building-level records aggregated to a province-month panel spanning March 2023 to August2024 and cross-checked against independent remote-sensing products. A two-way fixed-effects panel regression complements the analysis. Alignment with need is strong, with a Spearman correlation of 0.836 and a Theil T of 0.087, though Hatay is over-allocated by 10.4 percentage points and Adryaman is under-allocated by 6.0. Persistent pace shortfalls in three provinces are clear within two months and reflect mobilization frictions rather than systemic failure. The framework provides a low-friction, auditable pathway to routine post-disaster performance monitoring.
