## 1 Introduction  
The neural network using artificial intelligence, which originated in the 1940s as part of a widespread computerization effort, is a sophisticated bionic computational structure that is able to resolve complicated logical and nonlinear issues. It has a structure made up of a variety of small, simple neurons that are interrelated with one another in specific ways [1]. ANNs have widely been used throughout forecasting models in the building sector during the past several decades. Scientists utilized this approach across every facet of the building industry to maximize the benefits associated with its superiority over traditional modelling techniques. Some of the instances of ANNs utilized in the building sector are financial performance prediction, risk analysis, resources improvement, and procurement predicted results including performance evaluation [2]. ANN is a flexible computational tool that simulates the human mind's capacity to use reasoning and methods of pattern identification efficiently. Although knowledge of ANNs has already been present for a while, their utilization in civil engineering only really began in the late 1980s, predominantly within the construction industry [3]. Two aspects of neural networks bear similarities to the human's brain: Learning is the method by which a neural system gets information: Knowledge of a neural network is held in what are called synaptic weights, which are inter-neuron interaction strengths [4]. Neural network representation that includes linear as well as non-linear relations, along with the capacity to infer such relationships based on modelled data oneself, are the actual characteristics and advantages of neural networks as a tool. For the goal of modelling data with non-linear features, conventional linear mathematical models are merely insufficient [4]. In addition to providing an overview of the uses of artificial intelligence (AI) and neural networks (ANNs) in many fields, [PERSON] [5,6] and [PERSON] et.al [7] also attempted to investigate novel applications of these technologies. Regarding building projects, cost will frequently be the initial factor that must be taken into account. The accomplishment of any construction project depends critically on a reliable estimation of the materials and expenditures involved [8]. The intricate design of the construction business and the uniqueness of each project endeavor suggest that a variety of factors could influence the project's total cost [8]. In order to obtain the most accurate results in this field, the aim of this paper was to compare two ANN-based tools for cost prediction focused on building renovation projects in Greece. This is since, as far as the authors are cognizant, no such models exist, which led to an extensive literature review. To create a comparable type of cost estimating model, information from prior remodeling projects needs to be collected. As ANN models are able to identify patterns and relationships, they can generate projections that are more precise when they use historical project data. Whenever there are few project details accessible, they may require adjustments even in the early phases of a project. Comparing by using ANN models to estimate renovation costs necessitates making adequate use of data for model training and validation. Builders and engineers may benefit from the most optimal ANN model by using its realistic forecasts after it has been implemented and identified. As of this writing, no such model which resulted from the comparison of RBF and MLP approaches in order to implement it in a building's renovation cost estimation based on a particular set of work packages exists. A substantial dataset of actual project cost data was obtained from a Greek contractor involved in building renovations in order to fill the gap in the field of research. In order to create ANNs for cost prediction, the information gathered was organized, evaluated, and suitably encoded. The network was developed, trained, and tested using IBM SPSS Statistics software 28.0.0.0.