## Literature Review
### Artificial Neural Network in Construction Management Research  
The ANN has been described as a machine learning tool designed to identify dynamic or non-linear behaviour [50; 51]. Unlike linear regression or bivariate correlation, ANN utilises multilayer perceptron \begin{table}
\begin{tabular}{c c c} \hline
**CODE** & **Post-Contract Cost Controlling Techniques** & **Sources** \\ \hline CFLOW & Cash flow & [14; 20; 30] \\ WORKBUD & Working budget & [14; 20; 30] \\ TCACT & Taking corrective action & [14; 20] \\ MONOV & Monitoring overheads & [14; 20; 30; 31; 32] \\ MONLC & Monitoring labour cost & [14; 20; 30; 33] \\ MONMC & Monitoring material cost & [14; 20; 34; 35; 36] \\ MONEC & Monitoring equipment cost & [14; 20; 37; 38] \\ VARMAN & Managing variations & [14; 15; 20; 39; 40] \\ FORCOM & Forecasting at completion & [41; 42] \\ COMPUNI & Compounding unit & [14; 15] \\ INTERIMV & Interim valuations & [14; 43] \\ INCMIL & Incremental milestone & [36; 37; 44] \\ SIMP & Site meeting and post-project reviews & [45; 46] \\ IDCOV & Identifying indicators of cost overruns & [14; 15; 20] \\ PamPL & Summarising profit and loss & [14; 20; 43] \\ HDATA & Historical data & [47; 48] \\ COSTR & Cost ratio & [20; 49] \\ COSTFOR & Cost forecasting & [20; 39; 40] \\ \hline \end{tabular} Source: Authors’ compilation (2020).  
\end{table} 
Table 1: Post-contract cost controlling techniques used in construction projects. in identifying useful patterns in a system [52]. The multilayer perceptron is a network of synaptic mimicking brain neurons. Consequently, in a neural network, there will be an input variable, Synaptics leading to hidden layers and final outputs. Also, in contrast to other modelling techniques such as regression, the artificial neural network (ANN) captures the non-linearity and complexity of the changeable environment of different construction projects better [53]. High levels of uncertainty in construction are better captured using the ANN [54], even to higher levels of accuracy [55]. As a result, the ANN is increasingly applied for modelling and profering solutions to construction problems. According to [PERSON] and [PERSON] [56], and [PERSON] et al. [57], ANN has been applied to understanding and resolving construction problems such as cost estimation, safety, productivity, and risk assessment. Meanwhile, the comprehensive overview of such application is considered broad, with very little information about the technical application of ANN to construction problems provided therein. Subsequently, a detailed narrative of the technical application of ANN to construction problems is provided. This narrative comprises the differences between ANN estimation and prediction, the prediction performance of ANN models and hybridisation. ANN is commonly used in prediction and estimation problems in construction. In statistics, prediction and estimation models are different in terms of purpose. The purpose of an estimation model is to know the property of the actual state of nature, while a prediction model seeks to guess the outcome of a random variable [58]. As a result, the data used in building a model is also used in testing the model in estimation modelling, while prediction modelling is to substitute a random data value into a model when the data value was not used in building the model [58]. ANN is used for estimation only purposes by modelling relationships among variables to address construction problems such as construction duration [55], cost performance [54] and safety management [59]. In some research, the ANN models are mostly descriptive. For instance, [PERSON] and [PERSON] [53] modelled the relationship among hauling distance, earth conditions, age of equipment and workspace restrictions using the ANN to estimate dozer productivity in construction.