PSI - Issue 64

5

Author name / Structural Integrity Procedia 00 (2019) 000–000

Maysam Jalilkhani et al. / Procedia Structural Integrity 64 (2024) 161–167

165

�� � ���� � � �� � � � 1 � � � � ��� � � � �� � ���� � � ��� � max � �� , �� �

(1)

(2)

(3) where, E / G is the Young’s modulus to shear modulus ratio for the beam material, ε bmax and ε smax represent the maximum tensile strain resulting from pure bending and pure shear deflections of the beam, respectively. Also, ε h is the horizontal ground strain at the bottom edge of the beam, which varies based on the ground subsidence profile. The building’s damage class is determined by comparing the peak strain value, ε max , with threshold values corresponding to different structural damages. However, due to simplified assumptions inherent in the equivalent beam model, it inadequately estimates structural damages from ground subsidence. Factors such as soil type, building typology, position, and excavation shape influence ε max , yet these effects are not properly accounted for in the simple beam model (Camós and Molins, 2015). Furthermore, ε h along the primary horizontal directions (x, y) is approximately calculated from horizontal ground movements. To accurately consider soil stiffness beneath the building, the Winkler model is used, assuming continuous support with inelastic springs. However, without appropriate equations for parameter calculation, numerical analysis is recommended to compute ground subsidence and related structural damages. Different analytical methods, though rooted in similar basic concepts, often yield different structural damage predictions for the same buildings. Such discrepancies arise from variations in assumptions regarding parameters like neutral axis position, ground subsidence profile, and beam loading distribution (Saeidi et al, 2012). 4. Numerical Methods Numerical methods often use finite element models (FEM) to evaluate damage induced by mining subsidence. These methods incorporate ground subsidence values into the model alongside typical loads such as self-weight or occupancy loads. This is achieved by representing subsidence as differential settlements in foundation elements. More sophisticated models may also include soil-structure interaction. Although these methods offer detailed insights into damage rates across different building parts, they are computationally intensive and time-consuming. They also require precise input regarding building characteristics (materials, dimensions, etc.) compared to simpler methods. Furthermore, their applicability is mainly limited to assessing damage in single structures and cannot directly evaluate building performance at an urban scale (Cai et al, 2020). 5. Mining-induced ground motions Besides ground subsidence, mining-induced ground motions, termed as mine tremors or rockbursts, result from the sudden release of strain energy during mining operations. The effect of these tremors on surface structures varies based on factors such as mining depth, intensity, geological conditions, and structural design and construction quality. Damage ranges from minor cosmetic issues such as wall and ceiling cracks to severe structural faults endangering building safety and integrity. The occurrence of mining-induced ground motions holds significant importance in non-seismic risk areas. Buildings in these areas likely lack seismic-resistant detailing necessary to withstand lateral movements. As a result, the building stock in these areas may be highly vulnerable to such deformations, necessitating a thorough assessment of their capacity to resist seismic loads. In regions with minimal seismic activity, obtaining reliable ground motion data for numerical models can prove challenging. The scarcity of local seismic events, coupled with limited instrumentation, often results in a lack of comprehensive recordings of ground motions crucial for accurately assessing nearby building behaviour. As an alternative, data from more seismically active areas may be utilized; however, this approach requires careful judgment. For example, differences in geological and seismic characteristics between the source and target locations necessitate the use of amplification factors. These factors require meticulous evaluation to ensure they accurately reflect the local conditions of the non-seismic area, effectively compensating for disparities in the original data used.