Sulaimania Journal for Engineering Sciences

This study mainly focuses on identifying and calibrating the Hardening Soil Model HSM parameters for reconstituted soil samples representing the clay core of Darbandikhan Dam. Data from previous investigations and laboratory tests were coupled with numerical modeling to estimate the key parameters, such as stiffness moduli, stress dependency power of stiffness, and shear strength parameters. The reference secant modulus E50 was derived from replicated consolidated undrained triaxial tests at different confining pressures using PLAXIS, with a calibrated value of ????????50????????????????????????= 3100 kN/m². Oedometer stiffness Eoed was estimated from one-dimensional consolidation tests under different density and moisture content conditions, yielding a reference modulus of 3320 kN/m². Key findings indicate significant variability in soil stiffness and stress dependency, with a stiffness exponent (m) ranging from 0.66 to 0.80, influenced strongly by initial density and moisture content. The friction angle (φ) was calibrated to 25°, aligning closely with historical data. Finally, the simulated results based on the parameters numerically calibrated against undrained triaxial tests using PLAXIS finite element software showed strong agreement with the laboratory results.

Place attachment represents a fundamental concept for understanding how individuals establish emotional, social, and cognitive ties to the built environment. In the setting of traditional marketplaces, this relationship is commonly shaped by repetitive human activities that reinforce both spatial identity and cultural meaning. The Qaisaria buildings situated in the Kurdistan Region of Iraq reflect this interplay, functioning as both commercial spaces and community anchors that conserve historical heritage. Despite the popularity of Qaisaria buildings as vibrant gathering spaces, there is limited understanding of how activity influences people's social interaction and attachment to these historic environments. This study aims to investigate how daily activities—such as trade, social gatherings, and cultural exchanges—contribute to strengthening place attachment in two historical Qaisaria buildings located in Koya and Kifri. A mixed-method research design was employed, incorporating qualitative interviews alongside a structured questionnaire as a secondary data collection tool. One hundred sixty questionnaires were distributed to local users (80 per site), assessing six dimensions of place attachment using a five-point Likert scale. The results revealed a contrast between the two sites: while activity in Kifri significantly influenced all dimensions of place attachment, Koya impacted only functional aspects such as Place dependence and place expectation. Koya's emotional and social bonds and other dimensions appeared to depend on other contextual factors beyond activity. The study concludes that the impact of activity on place attachment is context-dependent, shaped by each site's urban, historical, and social conditions. These findings emphasize the importance of integrating dynamic activities in heritage preservation policies to strengthen historic marketplaces' social and functional continuity.

The demand of the city of Duhok for electricity is increasing and this requires efforts to provide the required load. Building new electricity generation units and/or importing electricity from neighboring countries are available to fulfill these needs. Therefore, it is vital to have earlier information about electrical demand. Midterm electrical load forecasting is useful to estimate the required power to be extracted from power systems in a time span of one year. In addition, it provides information that is necessary for planning the maintenance, expansion, and improving system performance of electrical power generation. These have an impact on reducing the operational costs and the emission of power plants through reducing the fuel consumption. Therefore, this paper proposes a medium-term load forecasting based Artificial Neural Network (ANN) and applies it on Duhok city power system. Available data for the power system of the city is used in the ANN to predict the load demand up to the year 2025. Using ANN in forecasting has importance because of its simplicity, easy implementation, yet effective and superior performance.