Pipelines are subjected to many damaging agents, such as, earthquake, ground movement, and
aging which are responsible for important financial expenses. Structural Health Monitoring (SHM)
of civil structures using arrays of sensors is promising such that data form the monitoring systems
enable us to trace the structural anomalies and performance for early treatments. The need for
introducing faster and intelligent methods has helped researchers propose novel approaches for such
monitoring procedures. In this study a new method is introduced for monitoring of surface pipelines
used primarily for oil and gas. The framework takes the advantage of Gaussian Process Regression
Method (GPRM) to create a probabilistic predictive model for damage detection and the subsequent
localization of the defect. To this end, an isotropic pipeline is modeled numerically and validated with
an experimental setup. Afterwards, the model is extended to the real-life application to establish
a meta model. Damages are introduced as small holes at different locations (one at each time).
The GPRM is used to map the system responses to the selected statistical features which are
utilized as indicators for the existence of the damages and their locations. GPRM reveals more
promising results compared with conventional regression analysis. It considers the uncertainties due
to lack of observation. In addition, it is an updatable approach with having local effects on the
model. In another words, it affects the model in the vicinity of new observations. Moreover, among
selected statistical features, number of peaks greater than or equal to 20% and 60% of the maximum
peak values show better results corresponding to damage localization. Also the curve length and
correlation coefficient of the system response (induced signal) are found to be efficient for damage
detection. The novel method has been validated with filed measurements and experimental data
and found to work efficiently.