# Concrete Silos: Failures, Design Issues and Repair/Strengthening Methods
## 3 Design Issues in Reinforced Concrete Silos
### Design Factors
#### 3.1.2 Silo Design Geometry  
The utility of the silo design codes reviewed in the preceding sections in mitigating failure is fundamentally dependent on the silo design geometry. There are three principal designs, which are preferred in the design of silo structures, namely circular, rectangular, and hexagonal configurations [54]. The configurations are further grouped into single and grouped sil arrangements [61]. A fundamental concern is the preference of one design over another involves a tradeoff between aesthetics, capacity, structural strength, material factors, structural failure, and durability. A study conducted by [PERSON] [54] argued that grouped cylindrical silos were more economical and structurally appropriate compared to single silos, especially in handling lateral loads [54]. However, the claim that grouped cylindrical silos were ideal from an engineering point of view could be contested. The contestation is based on the structural concerns and possible buildup of pressure at the transition zones, linked to force transfer and continuity. Experimental data show that grouped cylindrical silos have radial shear forces within the interstice walls that result in frequent bending moments. The impact of the radial shear forces is further amplified by the compression hoop forces, which occur during the loading of the silo structures [54]. The synergistic influence of these forces shows that structural weaknesses in one silo in a grouped silo configuration would be transferred to the next silo structural, which might, in turn, result in mass structural failure. The possibility of such failure is evident given the limitations of the existing building design codes. A review of the ACI 313-97 1997 and TSE-6989 1989 silo design codes show the codes provide little guidelines on how engineers can quantify the forces in a grouped silo configuration [62; 63]. The ACI 313-97 standard/code, under the silo foundation design, proposes that \"unsymmetricalloading of silo groups and the effect of lateral loads shall be considered in foundation design\" [64] (p. 10) On the downside, there is a minimal explanation of how the procedure should be undertaken. Similar constraints were observed in the TSE-6989-1989 standard [55]. Beyond the lack of reliable guidelines, there are concerns about the general utility of existing standards, considering that they were developed more than three decades ago. Since significant changes have been made in the silo design, including the fluidized floor designs, material withdrawal, and homogenization by Lafarge company [63], the lack of integration of new innovations in existing design codes poses new challenges because the innovations could be incompatible with existing designs. The risk of silo design configuration and geometry-related failure can be mitigated by varying the geometrical parameters \(\alpha\), \(\mathrm{t}_{\mathrm{W}}\), D/t in the interstice walls under interstice loading conditions. A variation in the geometric parameter influenced the design coefficients for the bending moments, hoop forces, and shear forces. Based on this approach, the relationship between the geometric parameters and the design coefficients was moderated by the pressure applied by the bulk solids and the radius of the silo hopper [54]. Alternatively, [PERSON] and [PERSON] [63] proposed the utilization of artificial neural networks (ANN) to estimate the design moments and forces in grouped silo configurations. Empirical evidence suggests that the ANN models were ideal in the estimation of the design moments and the design forces. On the downside, there were 11 different ANN back-propagation methods (including the BFG, CGF, and SCG algorithms), and each had a distinct test error, training error, and hidden layer neurons (HN) as shown in Table 6. Following the comparison of the superiority of different ANN Algorithms, it was deduced that the \(\mathrm{M}_{\mathrm{SUPPORT}}\) and \(\mathrm{M}_{\mathrm{CROWN}}\) values are best estimated using the SCG ANN algorithm [63].