To decrease the environmental and climatic effects of rising concrete consumption, more environmentally friendly concretes are required. One approach to achieve this goal is using natural pozzolans (NP) in concrete mixtures. Natural zeolite (NZ) as a highly reactive NP can improve concrete's durability and mechanical properties. However, tests to estimate concrete strength may take a long time and be costly. Therefore, using computational intelligence techniques, particularly artificial neural networks (ANNs), can help speed up and simplify the process. Hence, this study aims to explore the protentional of employing an ANN model to predict compressive strength (CS) and electrical resistivity (ER) of natural zeolitic concrete (NZC). The experimental results of 324 NZC specimens made from 54 different mix designs are used to develop the ANN model. Seven variable inputs are considered when designing the ANN model to predict CS and ER values, including specimen age, water-to-cementitious materials ratio, cement, NZ, gravel, sand, and superplasticiser contents. Extensive computational tests were conducted to evaluate the performance of the proposed model against results obtained by experimental tests and existing gene expression programming (GEP) in the literature. The RMSE values for CS and ER are 1.65 MPa and 3.96 Ω-m, respectively, which confirm the model's accuracy and robust predictive capability. The study's findings have the potential to assist in cutting costs and saving time by using a reliable prediction technique rather than conducting costly and time-consuming tests.
