## 1 Introduction
### Damage Assessment in Reinforced Concrete  
The damage detection becomes simple in some structures where system of homogenous materials is considered. As examples from literature, a model using ANN is used in structural health monitoring of curtain-wall system where mode of failure is clearly defined as total or partial destruction of its connections with the bearing structure [41]. In addition, a successful model identifying damages in sandwich composites by using Bayesian probabilistic neural network gave good agreement with the actual damage detection [42].  
In contrast to homogenous materials, concrete is a very complex material. In modeling concrete, scale is introduced to limit the content of the defects. Three scales were introduced by [PERSON] [43]: microscale, mesoscale, and macroscale. Microscale focuses on cement paste and aggregates where defects are crystal defects or atomic voids that are analyzed in material science models. Mesoscale considers unit cell with defects as microcracks and large pores that are studied using micromechanical models. Lastly, macroscale represents macrocrack defects where continuum and fracture mechanics theories can be applied. A previous study using mesoscale micromechanical damage model for concrete proposes kinetics of microcrack evolution [44]. The geometry of the model is characterized by the volume, random shape and disposition of aggregates. Another model using microplane for concrete focuses on the cracking localization [45]. This gives a good response on the quasi-brittle materials with a certain stress-strain boundary. A property of concrete that is significant to investigate is the vertex effect. This is when inelastic behavior of concrete for highly non-proportional loading paths leads to rotating principal stresses [46]. Some researches consider multiscale, i.e., a micro-to-macro damage scale, to initially investigate the natural behavior of materials when the test is applied.  
To further investigate reinforced concrete (RC), multiscale studies focused first on plain concrete. As an example, development of prediction models to investigate plain concrete using cylinder test results as training patterns in ANN gave good results. Two major techniques were applied to increase accuracy, these are parameter condensation and weighing technique [47]. In addition, data from laboratory tests using masonry clay bricks and cement mortar was also modeled using ANN and fuzzy logic to determine its compressive strength [48]. However, the addition of reinforcing bar greatly influenced the reinforced material behavior hence more research should be done.  
Beam is an example of structural members in reinforced concrete. From study, artificial intelligence using ANN applied to reinforced concrete beams gave good agreement with the measurements to quantify and localize the damage or determine the damage patterns [49; 50; 51; 52; 53].  
Reinforced concrete structures have different mode of failure. It can be classified as compression, flexure and shear where crack formations form depending on its failure mode. To investigate different structural members and its mode of failure, ANN were used in studies that showed good agreement with experimental tests [54; 55; 56; 57; 58; 59; 60; 61; 62; 63].  
In this paper, the mode of failure for the beam specimens was designed to be in flexure. No shear cracks were observed near support. The damage metrics used was the load percentage applied to the four-point bending test. Using the experimental database with five input parameters: A1, A2, A3, PPA, and NA, selection of optimum ANN model was done. The selection process had two criteria: highest R and soundness of the ANN model. Soundness of the ANN model was validated using simulations where A1, A2, A3, and PPA was decreasing with load, on the other hand, NA was increasing with load.