The study of performance-driven optimization (PDO) in urban block design is essential in the context of architectural form and urban sustainability. PDO focuses on the integrated and comprehensive optimization of various quantifiable performances of buildings, such as solar energy usage, thermal comfort, and energy efficiency. This method aligns urban spaces with sustainable development principles, ensuring they are not only aesthetically pleasing but also functionally efficient. This study explores the existing deficiency in the literature by conducting an in-depth scientometric analysis of PDO in urban block design. Employing science mapping coupled with bibliometric analysis using Python, this study meticulously analyzes the prevailing literature to map out the current intellectual landscape, understand trends, and identify key themes within this domain. This review identifies the key trends, methodologies, and influential works shaping the dynamic field of PDO. It emphasizes the critical roles of computational simulation, artificial intelligence integration, and big data analytics in refining urban block design strategies. This study highlights the growing importance of energy efficiency, environmental sustainability, and human-centric design elements. This review points to an increasing trend towards using sophisticated modeling techniques and data-driven analysis as essential tools in urban planning, crucial for developing sustainable, resilient, and adaptable urban spaces.

The long-term prestress relaxation of soil anchors embedded in saturated clay is a critical issue affecting the safety of geotechnical structures such as slopes and foundation pits. Traditional integer-order constitutive models are often unable to accurately describe the nonlinear and time-dependent relaxation behavior observed in such anchorage systems. Based on fractional calculus theory, this study establishes a constitutive model for the coupled stress relaxation behavior of soil anchors and saturated clay. The Riemann–Liouville fractional derivative and the two-parameter Mittag-Leffler function are introduced to represent the material memory effect and continuous relaxation characteristics. To achieve reliable parameter identification, a hybrid optimization strategy combining the Adaptive Hybrid Differential Evolution (AHDE) algorithm and the Levenberg–Marquardt (L-M) method is proposed. The proposed model and identification approach are validated using field monitoring data from soil anchors in a slope engineering project at the Guangxi Friendship Pass Port. The results show that the proposed model can accurately reproduce the entire stress relaxation process, with a coefficient of determination of R² = 0.9517. Parameter sensitivity analysis further clarifies the influence of key parameters, including the fractional order and viscosity coefficient. The proposed approach provides a systematic theoretical framework and practical reference for the analysis and prediction of long-term prestress relaxation in soil anchorage systems.