In areas of high seismicity, a common and efficient method for resisting lateral loads is by incorporating a structural wall in the building design. Structural walls can provide sufficient strength, stiffness and deformation capacity when proportioned and detailed correctly. To achieve more efficient, economical and reliable designs, it is important to understand the response and behaviour of structural walls. With the use of high-strength materials becoming increasingly popular, the need for analytical methods for predicting behaviour and deformation capacity of high-strength structural walls is crucial. Two high-strength material flexural walls were constructed and tested under reversed cyclic lateral loading. Based on observations of recent earthquakes in Chile and New Zealand, a number of researchers felt that the current ACI 318-11 standards may have some deficiencies with regards to the structural wall provisions. This study aimed to compare the performance of two specimens, one of which was designed and detailed according to the current provisions, and the second in accordance with the proposed code change requirements for the next series of ACI 318, ACI 318-14. However, due to the premature fracturing of longitudinal steel, large inelastic strains in the confining steel were not developed and a definitive conclusion could not be made. Also in this study, the current ACI recommendations for the effective stiffness used when calculating lateral displacements is evaluated and compared with the experimental results. It was found that the code recommendations overestimated the wall stiffness by over 40%. This result suggests that further research into the stiffness degradation of members constructed with high-strength material is necessary Simplified computational methods to estimate the force-displacement response of a flexural wall were examined, a comprehensive fibre sectional analysis and basic bilinear stiffness relationships were considered. The results showed that the response of the high-strength walls can be predicted effectively.