Prediction of falling weight deflectometer parameters using hybrid model of genetic algorithm and adaptive neuro-fuzzy inference system

doi:10.1007/s11709-023-0940-7

Frontiers of Structural and Civil Engineering

2023, Vol. 17

Issue (5): 812-826 https://doi.org/10.1007/s11709-023-0940-7

本期目录

Prediction of falling weight deflectometer parameters using hybrid model of genetic algorithm and adaptive neuro-fuzzy inference system

Long Hoang NGUYEN¹, Dung Quang VU¹, Duc Dam NGUYEN¹, Fazal E. JALAL², Mudassir IQBAL³, Vinh The DANG¹, Hiep Van LE¹, Indra PRAKASH⁴, Binh Thai PHAM¹(

)

¹. Faculty of Civil Engineering, University of Transport Technology, Hanoi 10000, Vietnam
². Department of Civil Engineering, State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200025, China
³. Department of Civil Engineering, University of Engineering and Technology, Peshawar 25120, Pakistan
⁴. DDG (R) Geological Survey of India, Gandhinagar 382010, India

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Abstract：

A falling weight deflectometer is a testing device used in civil engineering to measure and evaluate the physical properties of pavements, such as the modulus of the subgrade reaction (Y1) and the elastic modulus of the slab (Y2), which are crucial for assessing the structural strength of pavements. In this study, we developed a novel hybrid artificial intelligence model, i.e., a genetic algorithm (GA)-optimized adaptive neuro-fuzzy inference system (ANFIS-GA), to predict Y1 and Y2 based on easily determined 13 parameters of rigid pavements. The performance of the novel ANFIS-GA model was compared to that of other benchmark models, namely logistic regression (LR) and radial basis function regression (RBFR) algorithms. These models were validated using standard statistical measures, namely, the coefficient of correlation (R), mean absolute error (MAE), and root mean square error (RMSE). The results indicated that the ANFIS-GA model was the best at predicting Y1 (R = 0.945) and Y2 (R = 0.887) compared to the LR and RBFR models. Therefore, the ANFIS-GA model can be used to accurately predict Y1 and Y2 based on easily measured parameters for the appropriate and rapid assessment of the quality and strength of pavements.

Key words： falling weight deflectometer modulus of subgrade reaction elastic modulus metaheuristic algorithms

收稿日期: 2022-09-22 出版日期: 2023-07-14

Corresponding Author(s): Binh Thai PHAM

引用本文:

. [J]. Frontiers of Structural and Civil Engineering, 2023, 17(5): 812-826.
Long Hoang NGUYEN, Dung Quang VU, Duc Dam NGUYEN, Fazal E. JALAL, Mudassir IQBAL, Vinh The DANG, Hiep Van LE, Indra PRAKASH, Binh Thai PHAM. Prediction of falling weight deflectometer parameters using hybrid model of genetic algorithm and adaptive neuro-fuzzy inference system. Front. Struct. Civ. Eng., 2023, 17(5): 812-826.

链接本文:

https://academic.hep.com.cn/fsce/CN/10.1007/s11709-023-0940-7
https://academic.hep.com.cn/fsce/CN/Y2023/V17/I5/812

Fig.1

Fig.2

Fig.3

Fig.4

No.	parameter	unit	value
1	specified dimension	–	–
	length × width × height	mm × mm × mm	3900 × 1700 × 1400
	weight	kg	450
2	load	–	–
	type of pulse load	–	full wave
	cycle of pulse load	$m s$	0–120
	pulse rise time	$m s$	10–20
3	load cell	–
	diameter of the load plate	mm	300
	resolution	kN (kPa)	0.1 (1)
	accuracy	%	2
4	geophone-device used to measure deflection	–	–
	type of sensor	–	variations of seismic velocity
	deflection sensor locations [29]	mm (in)	0; 203 (8); 305 (12); 457 (18); 610 (24); 914 (36); 1219 (48); 1524 (60); ?305 (?12)
	resolution	μm	±1
	accuracy	%	2
5	temperature sensor	–	–
	type of sensor	–	pt100
	resolution	°C	0.1
	accuracy	%	0.5
6	distance measurement instrument	–	–
	resolution	m	0.1
	accuracy	%	0.1

Tab.1

Fig.5

parameter	code	unit	min	max	average	std
inputs	–	–	–	–	–	–
surface deflections	–	–	–	–	–	–
D₀	X1	μm	52.1	641.2	161.522	59.639
D₈	X2	μm	36.6	618.0	146.997	56.911
D₁₂	X3	μm	36.4	576.1	142.843	54.788
D₁₈	X4	μm	36.1	516.4	136.928	51.941
D₂₄	X5	μm	33.8	448.4	126.701	47.510
D₃₆	X6	μm	30.1	317.4	103.154	37.800
D₆₀	X7	μm	18.5	148.0	62.366	21.645
surface temperature (T)	X8	°C	23.1	42.4	29.412	47.419
surface load (P)	X9	$k N$	38.70	77.41	61.902	37.708
slab thickness (hpcc)	X10	cm	24.0	30.0	27.041	21.616
8% cement-treated aggregate base course thickness (hb)	X11	cm	17.0	20.0	18.486	4.057
rigid pavement thickness (h)	X12	cm	41.0	50.0	45.527	6.383
compressive strength of concrete slab (Rc)	X13	MPa	18.32	33.33	25.750	1.996
outputs	–	–	–	–	–	–
modulus of subgrade reaction (k-value) for SHRP sensor configuration (k60)	Y1	$k P a / m m$	25.13	265.43	77.429	37.581
elastic modulus of the slab for SHRP sensor configuration (epcc60)	Y2	GPa	3.47	62.11	25.274	9.638

Tab.2

Fig.6

Fig.7

Tab.3

Fig.8

Fig.9

Fig.10

Fig.11

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