## 7 Conclusions  
The aim of this study was to propose a structured decision-support methodology for predicting the most applicable PCCTs using ANN. Eighteen (18) post-contract cost controlling techniques were identified through literature search and their applicability in practice was obtained from QSs and CPM through a questionnaire survey in the Nigerian construction industry. The ANN outputs had three iterations taken at the initial phase of construction, mid-level, and the close-out phases, respectively. The ANN analysis provided normalised importance of the post-contract cost controlling techniques. At the initial phase, the results of the multilayer perceptron show that 90.9% of QS will normally combine monitoring of material, equipment, and overhead cost. In the mid-level phase, the results show that 88.3% QS will use historical data along with profit and loss statements, cost forecasting and cash flow registers. It is concluded that the QS is the primary decision maker for the type of PCCTs at the initial and middle phases of construction project delivery. In the closing out phase of a project, the ANN output classified 81.8% of the CPM as the primary decision-maker. It is concluded that the CPM is the primary decision maker for the type of PCCTs at the closing phase of construction project delivery. The CPM at the final stages of construction adopts site meeting and post-project reviews; identification of cost overruns; interim valuations and historical data. As a construction project progresses, the CPM influences the decision-making process and thus, the post-contract cost controlling techniques adopted. Furthermore, the network plot reveals more interactions between the post-contract cost controlling techniques for the QS and fewer associations for the CPM. The conclusion is that the QS has the greater involvement and influence than the CM in the decision making for selecting post-contract cost controlling techniques for construction project delivery.  
The innovation of this study are as follows. The ANN has been presented as method for analyzing cross-sectional survey data to predict the decision making of construction professionals in choosing the PCCTs in different phases in construction project delivery. The standardised rescaling of operationalised variables for pseudo-probability demonstrates how data collected using the questionnaire can be adapted for ANN analysis. Furthermore, this study shows that the decision making on PCCTs covers three distinct phases of construction project delivery (initial phase, mid-level phase and closing phase) and that the influence of the decision makers differs across the phases. This study presents the stages where QS and CPM are influential in ensuring that the right PCCTs are selected for successful project delivery and client satisfaction.  
Conceptualization, T.O.; formal analysis, T.O.; methodology, T.O. and A.B.; project administration, T.O. and O.O.A.; data curation, T.O; resources, T.O. and A.B.; software, T.O.; supervision, T.O.; validation, T.O. and A.B.; writing--original draft, O.O.A.; writing--review and editing, O.O.A., T.O and A.B. All authors have read and agreed to the published version of the manuscript.  
This research was self-funded by the authors.  
The authors declare no conflicts of interest