## 1 Introduction  
Over the decades, researchers and engineers have proposed many underlying theories [1, 2, 3, 4, 5, 6] and tools [7, 8] to accurately predict the structural response of reinforced concrete (RC) members at their ultimate limit state (ULS) by keeping a balance between safe and economical design solutions at the same time. Based on these theories, different Current Design Codes (CDC) [1, 2, 3, 4, 5, 6] were established and used around the globe. However, the predictions from these CDC mostly underestimate the member response at ULS [9, 10, 11, 12, 13]. All these CDC are based on the truss analogy theory (TA), which is a sectional approach. This TA based on the contribution of the stirrups, the uncracked portion of a concrete member and aggregate interlocking mechanism, for measuring the RC member response at ULS as illustrated in Figure 1. However, this is not true in all cases, as the literature shows that the prediction of these CDCs are mostly underestimated compared to the experimental values. Therefore, another theory named the compressive force path (CFP) [14] based on the new, modified design approach or opposite assumptions of the TA, predict RC member responses very close to their experimental values [9, 10, 11, 12]. The CFP based on the member response included the critical region of the member between points of the counter flexural, unlike the sectional approach of the TA, as shown in Figure 1. The supports points are denoted by A, B, and D and vertical upward load is applied on C.  
With the computer science advancements in the last two decades, many researchers have proposed the use of soft computing (SC) methods to solve complex problems [15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26]. Artificial neural networks (ANNs), response surface methodology (RSM), fuzzy logic (FL), particle swarm optimization (PSO) and genetic algorithms (GAs) are among the most popular SC methods [27]. In this work to compare the CDC and CFP, an ANN is used to assess the member response of simple and complex RC members at ULS. As previously indicated, ANNs can be used to predict not only the material behavior [28; 29; 30; 31; 32], but also the member response [33; 34]. Furthermore, ANNs are also used to solve different problems associated with analysis domain assessments [35; 36; 37; 38], design optimizations [39; 40; 41], and especially with reliability assessments [42; 43; 44; 45]. Therefore, an ANN is chosen over the other tools in this present work. In the present work, an effort is done to compare the prediction of CDCs (i.e., ACI and EC2) [1; 2], CFP [14], and ANN for different RC members, i.e., (i) RC beams in simply supported conditions without transverse steel or stirrups (BWOS) and RC beams in simply supported conditions with transverse steel or stirrups (BWS), (ii) RC columns in cantilever supported conditions (CWA), (iii) RC T-beams in simply supported conditions without transverse steel or stirrups (TBWS) and RC T-beams in simply supported conditions with transverse steel or stirrups (TBWS) and (iv) RC flat slabs in simply supported conditions under a punching load (SCS) at the ULS. For this purpose, four different databases (DB) are developed from the previous experimental studies. The first DB-I is about the simple geometry with simple support conditions, i.e., RC beams in simply supported conditions  
Figure 1: The basic difference of TA and CFP Models [14]. without transverse steel or stirrups (BWOS) and RC beams in simply supported conditions with transverse steel or stirrups (BWS), the second DB-II is about the simple geometry but with cantilever support RC columns (CWA) with fixed support only, the third DB-III is about the complex geometry but in simple support conditions i.e., RC T-beams without transverse steel or stirrups (TBWS) and T-beams with transverse steel or stirrups (TBWS) in simply supported conditions and the last fourth DB-IV is about the complex geometry with complex loading conditions, i.e., RC flat slab (SCS) under a punching load [13].