This experimental study examines the contribution of steel fibers from cut tie wires, envisioned as an economical reinforcement for self-compacting concrete (SCC), by simultaneously analyzing their behavior in the fresh and hardened states. The study focuses on fiber length, comparing three lengths (2, 3, and 4 cm) at a constant dosage of 0.5% by volume. Methodologically, the evaluation focuses, on the one hand, on the rheological properties of SCC, and, on the other hand, on the mechanical performance after hardening. Scanning electron microscopy (SEM) analysis was also performed to characterize the microstructure and the interfacial transition zone between the steel fibers and the cementitious matrix. The results indicate that the introduction of fibers preserves the flowability of SCC while significantly improving its mechanical performance, particularly in flexure, with a shift in failure mode from brittle behavior to a more ductile, better-controlled post-cracking response. The best-performing mix, SCC reinforced with 4 cm-long fibers, illustrates this improvement, with toughness increasing from 125 N·mm (reference concrete) to 38,281 N·mm and the ultimate flexural load increasing by 57.1% at 28 days. Thus, this reinforcement enhances the SCC's robustness and adaptability, paving the way for sustainable, economically optimized construction applications, including in demanding structural contexts.
To address the disconnection between aesthetic requirements and parameter configuration in ceramic ornament design, this study proposes a bidirectional coupling model acting as an algorithmic bridge connecting aesthetic artistic expectations and ceramic firing physical constraints, integrating aesthetics and parameters. The model uses the Analytic Hierarchy Process to quantify indicator weights, employs a Generative Adversarial Network for ornament generation, and integrates Particle Swarm Optimization for global parameter optimization, forming a closed-loop framework. Experimental results show that the model achieves a style feature matching rate of 92.53 ± 0.97%, a decorative detail restoration degree of 97.41 ± 0.75% (N=5 independent replicates, 95% confidence interval, p<0.05), and a process parameter compliance rate of 91.47 ± 1.18% in the simple style of Song Dynasty ornaments. Compared with other models, it outperforms the Variational Autoencoder–Analytic Hierarchy Process–Particle Swarm Optimization model (87.42±1.35% style matching), the Generative Adversarial Network with elitist strategy and non-dominated sorting genetic algorithm Ⅱ (82.43±1.58% detail restoration), and the Generative Adversarial Network–Particle Swarm Optimization model (73.34±1.81% process compliance). The model achieves over 90% accuracy in recognizing multiple ceramic styles. Note that the present model is only validated against Song-Ming-Qing Chinese ceramic styles and its generalization to other ceramic cultural systems remains limited. These results demonstrate its effectiveness in coupling aesthetic requirements with parameter configuration and supporting digital inheritance and innovative ceramic ornament design.
The study aims to investigate the strength characteristics, as represented by shear parameters, of various soil types that pose numerous engineering problems in and around Mosul, Iraq. Five soils, including low-plasticity clay, high-plasticity clay, silty organic soil, fill organic materials (10.2%), and gypseous soil (63.4%), were selected for the study, having serious geotechnical problems like swelling, collapse, high organic content, and high gypsum content. The testing program includes direct shear, residual shear, unconfined compression, oedometer, chemical tests, and the index properties of the study soils. The results generally showed that direct shear tests yield strength parameters that lead to overvaluation of bearing capacity. In contrast, unconfined compression tests yield more conservative results, and residual shear tests yield the smallest values due to the test conditions. The results indicated that shear properties can vary significantly across test methods, highlighting the need for careful interpretation of bearing capacity values. Taking safety considerations into account, the bearing capacity may range from 50 to 150 kN/m2. Also, for the design of shallow foundations on Mosul soils, any available field or laboratory strength test for soil should be used in combination with tests that identify geotechnical issues in the soil, relying primarily on the test that yields the lowest value—or at least a reasonable value with engineering judgment—for the strength parameters.
There is a clear impact of urban investment in religious cities resulting from modern expansion projects, which may affect the spatial characteristics contributing to the formation of place identity. However, these relationships have not received sufficient quantitative investigation in the historic center of Karbala, Iraq. This research aims to examine the morphological, functional, and spatial transformations associated with urban investment and their potential implications for the spatial components of place identity by integrating Geographic Information Systems (GIS) and Space Syntax analyses. A comparative spatial analysis was conducted based on planning data, land uses, street network data, Geographic Information Systems were used to measure changes in land use and urban morphology. The street network was also analyzed using DepthmapX 0.8.0, based on the segment map, angular analysis, and a metric radius of 500 meters. The adopted indicators included Connectivity, T1024 Integration, and T1024 Total Angular Depth. The results showed significant morphological and functional transformations; residential land use declined from 35.02% to 28.58%, religious land use increased from 7.83% to 26.73%, and the tourism-related commercial land-use category, after harmonizing the classification, increased from 24.13% to 27.65%. The main streets around the two holy shrines had relatively high Connectivity and Integration values and lower Total Angular Depth values. The findings reveal measurable changes in land use, urban fabric and street-network configuration that could influence the spatial aspects of place identity through demolition and removal of old historic buildings. Integrating GIS and Space Syntax provides a framework for balanced urban development and historic-environment conservation.
Global Digital Elevation Models (GDEMs), such as ASTER, provide near-global coverage at a spatial resolution of 30 m; however, their vertical accuracy varies depending on terrain characteristics, land cover, and sensor-related limitations. This study develops and evaluates a nonlinear Artificial Neural Network (ANN) framework to enhance the vertical accuracy of ASTER DEM within a low-relief urban environment in Baghdad, Iraq. The proposed model is quantitatively compared with deterministic Inverse Distance Weighting (IDW) interpolation. A dataset of 57 high-precision GNSS RTK control points was collected. Forty-seven points were used for ANN training, while 10 independent points were reserved for validation. The ANN model achieved a training RMSE of 4.88 m, while the validation RMSE reached 2.21 m, which is considered the reliable indicator of model performance. Baseline evaluation showed a vertical RMSE of 2.21 m for the original ASTER DEM. After correction, IDW reduced the RMSE to 1.04 m (53% improvement), whereas ANN achieved 0.64 m (approximately 71% improvement), reaching sub-meter vertical accuracy. Residual distribution and comparative statistical analysis confirmed that ANN significantly reduces both systematic bias and error dispersion compared to deterministic interpolation. The findings demonstrate that nonlinear functional modeling substantially enhances the usability of freely available global DEM datasets for high-precision engineering and urban applications. A supplementary cross-validation analysis indicated that this performance advantage is sensitive to the specific data partition used, given the limited sample size; this is discussed as a study limitation, and expanding the training dataset is recommended to enable a more robust comparison.
