# Artificial Neural Network Model to Predict Final Construction Contract Duration
###### Abstract  
Forecasting the final construction contract duration at an early stage plays a vital role in the progress of a project. An inaccurate project duration prediction may lead to the project's benefits being lost. It is essential to precisely predict the duration due to the presence of several different factors. This paper contributed to developing a model to predict final construction contract duration (_FCCD_) in the early stages based on parameters characterized as few and shared for any contract. (contract cost, contract duration, and sector). This paper developed an Artificial Neural Network (ANN) model based on 135 Saudi construction project data. The development model has three stages. The first stage was standardization and augmentation using [PERSON] logarithmic and Pasini methods. The second and third stages were the first and second analyses of the ANN models, respectively. The first analysis aimed to promote the used data and integrate them into the second analysis to develop the ANN model. The ANN models were compared with three linear regression (LR) models (LR1, LR2, and LR3) and other models in the literature. The results revealed that the accuracy of the ANN model provides reasonable accuracy with an average mean absolute percentage error (MAPE) of 12.22%, which is lower than the LR3's MAPE by 27.03%. The accuracy of the ANN model is similar to that of earned value management (EVM) in the previous study. This paper supports research to deal with relatively little data and integrate them into a neural network. The ANN model assists the stakeholder in making appropriate decisions for the project during the pre-tendering phase by predicting the actual contract duration based on the CC, CD, and project actor.  
duration; contract; logarithmic; transformed; cost; sector; errors +
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Researchers in multiple nations have revealed numerous harmful consequences of delays. [PERSON] [6] claimed that significant cost overruns ranging from 40% to 400% occurred on various infrastructure projects in the United States of America. In the United Kingdom of Great Britain, just 38% of projects were finished within 5% of the contract timeline program, and only 70% were completed within 5% of the tender cost [7]. Additionally, according to [8], only one-eighth of Australian building projects were finished by the deadlines outlined in the contracts, and the typical schedule overrun was greater than 40%. Moreover, [PERSON] and [PERSON] [9] stated that about 70% of projects in Nigeria encountered delays, demonstrating that delays are a significant issue in Nigerian construction. Only 30% of building projects in the Kingdom of Saudi Arabia (KSA) were finished on time, while the typical time overrun was up to 30% [10].  
Most studies have focused on identifying and analyzing the factors affecting delays in projects [4; 11; 12; 13]. They pointed out that the factors that cause poor project performance still need to be fully understood. The causes of time delays were different from region to region. There is a need for more studies to predict the final construction contract duration in the early stages to assist stakeholders in deciding whether to continue or halt the project. Therefore, this paper aims to develop forecasting models for predicting the final construction duration project-based Artificial Neural Network (ANN) based on contract cost, contract duration, and sector type of 135 Saudi Arabia construction project data. Developing ANN models goes through three processes. This paper contributed to designing a model to forecast _FCCD_ in the early stages based on parameters represented as few and shared for any contract. (_CC_, _CD_, and sector). The theoretical contribution of the paper is to utilize small data to develop the ANN model. In addition, the practical implications of the paper are to forecast the final construction duration at the pre-tendering stage.