Abstract: Two of regularization techniques, i.e., QRD (QR Decomposition) with column pivoting and TSVD (Truncated Singular Value Decomposition), are studied respectively in order to solve ill-posed optimization problems transformed from structural damage identification. The improvement on the stability of identification results is demonstrated. Then, two parameters affecting identification results, i.e., valve for implementing regularization techniques and step-size limit for limiting iterative increments in solving identification problems, are investigated using the example of the BENCHMARK structure proposed by IASC (International Association for Structural Control) and ASCE (American Society of Civil Engineers). The case study of the BENCHMARK structure illustrates how to implement the two regularization techniques, as well as how to determine the proper values of the two parameters, valve and . The results show that, the location of damage pattern II in the 1st case of the phase I problem of the BENCHMARK structure are identified successfully, and the errors of damage severity calculated using TSVD and QRD with column pivoting are 6.36% and 7.33% respectively. The discussion about the two parameters, valve and , shows that longer step-size limit (≥0.5) is suitable for QRD with column pivoting, but the step-size limit can be longer or shorter ( ≥0.25) for TSVD.