An Artificial neural networks (ANN) model for evaluating construction project performance based on coordination factors

Figures & data

Table 1. The key coordination factors along with their groups

Group	Factor	Description	Sources
Planning and Scheduling (PSF)	X1: Plans (PLA)	Use or write briefing of project execution approaches, such plans cover those delivered by the owner/contractor/consultant	(Iyer & Jha, 2006)
	X2: Scheduling (SCH)	Prepare and update schedules to convey time-related information, based on the new and actual events	(Saram & Ahmed, 2001)
	X3: Meetings (MEE)	To exchange ideas, deal with conflicts and facilitate work, such as regular/irregular meetings	(Hossain, 2009)
	X4: All parties’ participation in plans (APP)	Liaison and communication with specialist consultants and nominated subcontractors to be involved in plan	(Chang & Shen, 2009)
Resource Management and Contacts (RMCF)	X5: Joint site visits (JSV)	To inspect a project’s performance by gathering, sharing, or confirming information on-site	(Iyer & Jha, 2006)
	X6: Team spirit (TEM)	Developing and receiving constructive inputs from all participants with an open mind	(Iyer & Jha, 2003)
	X7: Communication channels (COC)	Open wide and fast communication channels amongst all parties, structured or unstructured	(Hossain, 2009)
Records and Documentation (RDF)	X8: Record maintenance (REM)	All drawings, information, directives, verbal instructions, and documents received from each party to others, hard/soft copies	(Jaffar, Tharim, & Shuib, 2011)
	X9: Drawing documentation (DRD)	For overlapping activities’ coordination and giving execution plan of responsibilities to all parties involved	(Love, Irani, & Edwards, 2004)
Contract Implementation (CIF)	X10: Contract documents (COD)	Understanding generally, including contract articles, drawings, and specifications as complementary documents	(Iyer & Jha, 2003)
	X11: Maintaining contracts (MAC)	Documents and amendments to contracts, and obtaining specifications, and technical details	(Iyer & Jha, 2006)
	X12: Cleared payments (CLP)	The payment is appropriate with contract specified limits and consistent with the work progress status	(Simatupang, Wright, & Sridharan, 2002)
Quality and Value Engineering (QVF)	X13: Work integration (WOI)	From different subsystems and subcontractors to agree on detailed construction methods’ and specifications	(Saram & Ahmed, 2001)
	X14: Design and specification clarity (DSC)	To assign adequate time and resources for project implementation	(Iyer & Jha, 2006)
	X15: Quality assurance plan (QUA)	Prepare for the project quality in line with contract specification	(Iyer & Jha, 2003)
	X16: Value engineering (VAE)	Find new alternatives for higher specifications with less cost	(Chang & Shen, 2009)

Figure 1. The methodology main five stages.

Figure 2. Typical structure of feed-forward ANN.

Figure 3. Processing element of ANN.

Figure 4. The conceptual networks design.

Figure 5. The network training and development flowchart.

Figure 6. Performance plot of cost predication model.

Table 2. ANN trials for time evaluation network

No.	Type	L.A.	H.N	L.R	M.F	T.F	Epochs	MSE
Trial 1	FFBP	TGDM	5,5	0.7	0.9	LogSigmoid	1000	0.0562
Trial 2	FFBP	TGD	10,10	0.8	0.9	LogSigmoid	1000	0.0263
Trial 3	FFBP	TGDM	15,15	0.7	0.9	TanSigmoid	1000	0.0349
Trial 4	FFEP	TGDM	10,10	0.7	0.9	LogSigmoid	1000	0.1356
Trial 5	FFEP	TGD	15,15	0.7	0.8	TanSigmoid	1000	0.1274
Trial 6	FFBP	LM	10,10	0.7	0.9	LogSigmoid	1000	0.0458

L.A.: Learning Algorithm, H.N: Hidden Neurons, L.R.: Learning Rate, M.F.: Momentum Factor, T.F: Transfer Function, FFBP: Feed Forward Back-Propagation, FFEP: Feed Forward Elman-Propagation, TGDM: Tangent Gradient Descent with Momentum, TGD: Tangent Gradient Descent, LM: Levenberg Marquardt.

Table 3. ANN trials for quality evaluation network

No.	Type	L.A.	H.N	L.R	M.F	T.F	Epochs	MSE
Trial 1	FFBP	TGDM	5,5	0.7	0.9	LogSigmoid	1000	0.0725
Trial 2	FFBP	TGD	10,10	0.9	0.9	LogSigmoid	1000	0.0445
Trial 3	FFBP	TGD	15,15	0.7	0.9	TanSigmoid	1000	0.0533
Trial 4	FFEP	TGD	15,15	0.8	0.9	LogSigmoid	1000	0.0656
Trial 5	FFEP	TGDM	10,10	0.7	0.9	TanSigmoid	1000	0.0191
Trial 6	FFBP	LM	10,10	0.7	0.9	LogSigmoid	1000	0.0298

Figure 7. Performance plot of time predication model.

Figure 8. Performance plot of quality predication model.

Table 4. Determination coefficient and Durbin–Watson test results

Network	R^2	Durbin–Watson
Cost evaluation network	0.77	1.65
Time evaluation network	0.765	1.78
Quality evaluation network	0.75	1.70

Figure 9. Predicted against target values for cost, time and quality networks.

Figure 10. Histogram of residuals for cost network.

Figure 11. Histogram of residuals for time network.

Figure 12. Histogram of residuals for quality network.

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