Sediment yield estimation and evaluating the best management practices in Nashe watershed, Blue Nile Basin, Ethiopia.

Sediment yield estimation along with identification of soil erosion mechanisms is essential for developing sophisticated management approaches, assessing, and balancing different management scenarios and prioritizing better soil and water conservation planning and management. At the watershed scale, land management practices are commonly utilized to minimize sediment loads. The goal of this research was to estimate sediment yield and prioritize the spatial dispersion of sediment-producing hotspot areas in the Nashe catchment using the Soil and Water Assessment Tool (SWAT). Moreover, to reduce catchment sediment output, this study also aims to assess the effectiveness of certain management practices. For calibration and validation of the model, monthly stream flow and sediment data were utilized. The model performance indicators show good agreement between measured and simulated stream flow and sediment yields. The study examined four best management practice (BMP) scenarios for the catchment's designated sub-watersheds: S0 (baseline scenario), S1 (filter strip), S2 (stone/soil bunds), S3 (contouring), and S4 (terracing). According to the SWAT model result, the watershed's mean yearly sediment output was 25.96 t/ha. yr. under baseline circumstances. The model also revealed areas producing the maximum sediment quantities indicating the model's effectiveness for implementing and evaluating the sensitivity of sediment yield to various management strategies. At the watershed scale, treating the watershed with various management scenarios S1, S2, S3, and S4 decreased average annual sediment yield by 34.88%, 57.98%, 39.55%, and 54.77%, respectively. The implementations of the soil/stone bund and terracing scenarios resulted in the maximum sediment yield reduction. The findings of this study will help policymakers to make better and well-informed decisions regarding suitable land use activities and best management strategies.

Review of Modeling Techniques for Analysis and Assessment of RC Beam-Column Joints Subjected to Seismic Loads.

Beam-column connections are the most critical components of reinforced concrete (RC) structures. They serve as a load transfer path and take a significant portion of the overall shear. Joints in RC structures constructed with no seismic provisions have an insufficient capacity and ductility under lateral loading and can cause the progressive failure of the entire structure. The joint may fail in the shear prior to the connecting beam and column elements. Therefore, several modeling techniques have been devised in the past to capture the non-linear response of such joints. Modeling techniques used to capture the non-linear response of reinforced-concrete-beam-column joints range from simplified lumped plasticity models to detailed fiber-based finite element (FE) models. The macro-modeling technique for joint modeling is highly efficient in terms of the computational effort, analysis time, and computer memory requirements, and is one of the most widely used modeling techniques. The non-linear shear response of the joint panel and interface bond-slip mechanism are concentrated in zero-length linear and rotational springs while the connecting elements are modeled through elastic elements. The shear response of joint panels has also been captured through rigid panel boundary elements with rotational springs. The computational efficiency of these models is significantly high compared to continuum models, as each joint act as a separate supe-element. This paper aims to provide an up-to-date review of macro-modeling techniques for the analysis and assessment of RC-beam-column connections subjected to lateral loads. A thorough understanding of existing models is necessary for developing new mechanically adequate and computationally efficient joint models for the analysis and assessment of deficient RC connections. This paper will provide a basis for further research on the topic and will assist in the modification and optimization of existing models. As each model is critically evaluated, and their respective capabilities and limitations are explored, it should help researchers to improve and build on modeling techniques both in terms of accuracy and computational efficiency.