Available online at aeme.com/ijciet/issues.asp?jtype=ijciet&vtype= =9&IType=12 ISSN Print: and ISSN

Transcription

1 International Journal of Civil Engineering and Technology (IJCIET) Volume 9, Issue 12, December 2018, pp , Article ID: IJCIET_09_ Available online at aeme.com/ijciet/issues.asp?jtype=ijciet&vtype= =9&IType=12 ISSN Print: and ISSN Online: IAEME Publication Scopus Indexed PREDICTING MAXIMUM SURFACE SETTLEMENTT USING ARTIFICIAL NEURAL NETWORK (ANN) TO EARTH PRESSURE BALANCE TUNNEL MACHINE (EPBM) FORMETRO LINE 1 IN KENNY HILL FORMATION Abd Rashid, A.S, Mohamad, H and Ahmad,N. R 1 Civil and Environmental Engineering Department, UniversitiTeknologi PETRONAS, Seri Iskandar, Perak, Malaysia ABSTRACT The ground surface settlement due to tunneling works in urban area certainly imposes direct ground disturbance and building structural damage effects. The Artificial Neural Network (ANN) was employed to investigate the effects of eight identified parameters in Earth Pressure Balance (EPB) of Tunnel Boring Machine (TBM) for Kenny Hill formation in Klang Valley Mass Rapid Transit(KVMRT) Line 1 project. It was found thatt three major parameters of TBM had the most significant influence for surface settlement which are Foam Injection Pressure, Cutter Head Pressure and Tunnel Face Pressure with significant highest regression and lowest Root Mean Square Error (RMSE) value. The method used in ANN enables the overall mapping of all potential influence parameters in TBM in relation to surface settlement. This paper attempts to find new potential parameters for the first time tested on EPB operational machine in Kenny Hill formation using ANNN method. Key words: EPB, Artificial Neural Network, Surface Settlement, Kenny Hill formation. Cite this Article: Abd Rashid, A.S, Mohamad, H and Ahmad,N. R, Predicting Maximum Surface Settlement Using Artificial Neural Network (ANN) To Earth Pressure Balance Tunnel Machine (EPBM) Formetro Line 1 In Kenny Hill Formation, International Journal of Civil Engineering and Technology (IJCIET) 9(12), 2018, pp et/issues.asp?jtype=ijciet&vtype=9&itype e= editor@iaeme.com

2 Predicting Maximum Surface Settlement Using Artificial Neural Network (ANN) To Earth Pressure Balance Tunnel Machine (EPBM) Formetro Line 1 In Kenny Hill Formation 1. INTRODUCTION In dense urban populated and sensitive area, underground tunnel is one of the most effective infrastructures built for transportation and utilities system. The construction of tunnels however inevitably causes ground deformation which will then affect building structure integrity, interrupt traffic and ultimately endanger human population within the underground excavation zone. In order to analyze surface deformation for the twin tunnel constructed for KVMRT Line 1, a semi-empirical Gaussian distribution is introduced to validate the predicted and the actual measured settlement. The study of multiple tunnelling is yet to be established in Malaysia although multiple tunnelling effects on surface settlement have been studied extensively by international researchers[1][2]. Other studies which including numerical modelling methods have also been reported [3],[4],[5] for predicting ground movement induced by tunnel construction. For this KVMRT project, a volume loss in the range of 1% to 2% was predicted along the Kenny Hill formation depending on the TBM operation for Semantan Muzium Negara North and South bound. However, the actual performance of ground movements in KVMRT project was not properly analyzed. In this study, ground surface settlements owing to tunnel constructions using two Earth Pressure Balance (EPB) machines of parallel tunnel configuration were investigated along the selected sections within the Kenny Hill formation using on-ground monitoring points. Due to limited greenfield sites and suitability of data array around Kenny Hill alignment, only CH1+200 (Jalan Damansara- Jalan Sultan Abd Halim) is presented in this study to show significant results of ground settlement by the bored tunnels. In addition, some selected results from EPB tunnel parameters (face pressure and grout volume) during the tunnel construction were then correlated in response to surface deformation. 2. PROJECT BACKGROUND 2.1. Klang Valley Mass Rapid Transit (KVMRT Line 1) Tunnel Construction The KVMRT Project Line 1 alignment extends from Sungai Buloh to Kajang, consisting of 49km elevated section including underground tunnel section. The built underground tunnel covers 9.4km which starts from North Portal at Semantan (SB CH 1+048) to South Portal at Maluri (SB CH:10+307) which includes seven underground stations to accommodate the present and future demand for public transportation in Kuala Lumpur (KL). The seven underground stations comprise four vertical shafts and eight mined cross passages for the total UG1 (Semantan North Portal to Bukit Bintang) and UG2 (Bukit Bintang to Maluri South Portal) underground alignment. Along the 9.4km of underground section, there are two tunnels of geometric arrangement of parallel and stacked formation in Kenny Hill and KL Limestone geological formation. Two types of closed face tunnelling shield were introduced, which were Earth Pressure Balance (EPB) and the world sfirst Variable Density (VD) machine. The project utilised eight TBM where four EPB and TBM were used for Kenny Hill formation for the northern and western half of the underground alignment while the remaining were used for the unpredictable karstic Kuala Lumpur Limestone from Bukit Bintang Station to Maluri South Portal. In this research, the output data from EPB machine manufactured by China Railway Engineering Company for Northbound (CREC50) is presented and analysed Kenny Hill Formation The Kenny Hill Formation contains of interbedded shales and sandstone of upper Silurian- Devonian age and lies over the Kuala Lumpur Limestone. The rock has been metamorphosed editor@iaeme.com

3 Abd Rashid, A.S, Mohamad, H and Ahmad,N. R to form metasedimentsin which the degree of metamorphism varied regionally [6]. Weathered Kenny Hill formation is generally classified as residual soil and completely highly weathered sedimentary rock while unweathered Kenny Hill formation can be classified as fresh to moderately weathered sedimentary rock. In respect to weathering Kenny Hill formation, it can be highly variable and includes inter bedded rock. The properties in Kenny Hill formation consisted of lenses, layers, and beds of moderately weathered to fresh sedimentary rock including conglomerates, quartzite, and sandstone. The KVMRT alignment is 60% dominated in Kenny Hill formation where six sections from Semantan North Portal to Muzium Station were selected based on green field area and the best tunnel array instrumentation at CH1+200, CH1+420, CH1+520, CH1+590, CH1+960 and CH2+100 as shown infig.1.the lithology of Kenny Hill sections was studied from the soil investigation and the lithology was dominated by Sandy Silt (28.44%), Silt (18.5%), Sandy Clay (12.54%), Silty Sand (9.17%) while Phyllite Rock (9.03%) and less than 2% contained Gravel and Quartzite. Fig.1. Greenfield Selection for six location in Kenny Hill formation 2.3. Gaussian Distribution The normal distribution (Bell Curve) is commonly applied in statistical method and called as the Gaussian distribution in honour of Carl Friedrich Gauss. However, in predicting subsurface deformation induced by tunnel, an inverted Gaussian distribution is applied, and it was described by normal probability function. The curve fitting by Gaussian distribution is a practical application that describes the volume loss, maximum settlement and trough width and can be used for different tunnel configuration (diameter size, depth), geology and construction method as described by [8]. The unique configuration tunnel for KVMRT project consisted of stacked and parallel which gave individual settlement reading at each Ground Settlement Marker (GSM) placed on the ground. The settlement readings were relatively small in magnitude (in mm) but the settlement increments were observed as the TBM approached and passed the tunnel axis. An observation had been made specifically in this project and it was found that the number of monitoring intervals and spacing is the major influence for producing the curve fitting. Referring to the plotting on x-axis is based on the tunnel advancing to the tunnel array (in m) and the y-axis is the daily measured settlement (in mm). The major analysis of subsurface deformation is dependable on the maximum settlement on the tunnel centre line (S max ), the horizontal distance from the tunnel centre line (y), and the horizontal distance from the tunnel editor@iaeme.com

4 Predicting Maximum Surface Settlement Using Artificial Neural Network (ANN) To Earth Pressure Balance Tunnel Machine (EPBM) Formetro Line 1 In Kenny Hill Formation centre line to the point of inflexion (i), as described by [9]. The most significant factor in the calculation are the relationship of tunnel depth (Z o ), tunnel soil parameter (K) and settlement volume loss (V L ) in this analysis as shown in Table 1. Table 1 Ground Settlement equations detail using Gaussian distribution Equation (1) S v = S max exp (- y 2 /2i 2 ) S v = Settlement S max = Maximum settlement directly above the tunnel centreline y= Transverse horizontal distance from the tunnel centreline of the trough i = Horizontal distance from tunnel centreline to the point of inflexion on the settlement trough Equation (2) i= KZ o K = Tunnel soil parameter (approximately ranging from 0.5 to 0.25) depending on soil geological type e.g.; clay and sand 0.5 and gravels 0.25 Equation (3) V s =V L π D 2 /4 V s = Volume of the surface settlement trough V L = Excavated tunnel, m 3 S max = 0.31V L D 2 / K Z o Combined equations (2) and (3) Fig. 2. The settlement trough above a tunnel in soft ground [7] In order to produce good curve fittings to real monitoring data, five possible methods can be applied: Direct Calculation (DCJ), Direct Calculation S max (DCSMAX), Non Linear Regression Sum of absolute error (NRSAE), Non Linear Least Squares (NRLS) and visual fit by eye [10]. In this study, the Gaussian Distributions are produced by equation introduced by[9] using spreadsheet calculation as shown in Figure editor@iaeme.com

5 Abd Rashid, A.S, Mohamad, H and Ahmad,N. R For any TBM operation, a series of instrumentation and monitoring are installed at the ground and subsurface deformation is evaluated by comparing the field data with the predicted Gaussian distribution Ground Settlement Response by Twin Tunnel The ground settlement is daily monitored by an extensive Ground Settlement Markers (GSM) at six specified locations of arrays selected. These data are then produced in sectional view before, during and passed the GSM arrays. The 4D distance (4 times the tunnel diameter, 6.65m) is introduced in three phases which are before, during and after the tunnel passed the specific instrumentation arrays. The 200m vertical ground settlement is studied for each section so that the ground response interaction for TBM1 and TBM2 can then be plotted. The tunnel drive for twin tunnels are approximate in one (1) month intervals for example, the first TBM1 mined on the 13 th July 2013, followed by the second TBM2 on the 16 th August The six selected section of greenfield along Kenny Hill formation are CH1+200, CH1+420, CH1+520, CH1+960 and CH2+100 towards to Muzium Negara Station. One of the daily ground settlement sections along CH1+200 as shown in Figure 3. Fig. 3. Settlement trend for TBM1 and TBM2 at CH1+200 The actual settlement data shown in Figure 3showed that ground settlement before the first TBM1 arrived at CH1+200 where S max actual are 6mm at GSM1205 and this settlement continued decreasing to an approximately of 18mm. The actual settlement then continued and maintain constant at 16mm along 100m from CH1+250 to CH The TBM2 however showeda slight settlement for 50m during the mining of maximum 10mm and constant increase of 8mm along 110m drive from CH1+240 to CH Generally, all actual ground settlements were found less than the predicted value however, some cases at CH1+520 founds that the settlement reached more than 30mm due to less than 1D overburden depth. The overall results were reliable in respect to tunnel operation in Kenny Hill formation and the effect of second tunnel (TBM2) ground subsidence was smaller compared to TBM Back-Analyzed Twin Tunnel Volume Loss The volume loss back-analyzed display a unique relationship between volume loss and tunnel depth along the six sections. The maximum volume loss was approximated 0.9% which is not more than 1% for Kenny Hill formation whilst the minimum is very less of 0.01%. As shown editor@iaeme.com

6 Predicting Maximum Surface Settlement Using Artificial Neural Network (ANN) To Earth Pressure Balance Tunnel Machine (EPBM) Formetro Line 1 In Kenny Hill Formation in Fig 4, tunnel volume loss was dependable to the tunnel depth whereby the higher tunnel depth, the lower the volume loss would be at each section. This trend was however not applicable only at CH1+200 where the maximum volume loss was 0.87% at 15.02m tunnel depth in which the author found out that the actual maximum settlement was 16.32mm as shown in contrast, two major components affecting the tunnel volume loss were apparently the tunnel depth and actual settlement data. In view of the results obtained, an average volume loss of 0.3% found to be significant to all selected sections in Kenny Hill formation. Vol Loss (%) Tunnel Volume Loss at Six Sections Tunnel Sections Tunnel depth (m) max VL TBM1 & TBM2 Min VL TBM 1 & TBM2 depth Fig 4. Overall Back-analysed Volume Loss at Six Sections 3. TUNNEL SHIELD PARAMETERS AND AFFECTING TO SURFACE SETTLEMENT The shield operation parameters are listed Table 1 where Suwansawat and Einstein[11] used the inputs to predict maximum surface settlement. 3.1 Earth Pressure Balance (EPB) TBM Operation Factors EPB TBM is most commonly used for soft ground and high-water level in the tunnel excavation where there is sensitive soil interaction tunnel and ground surface. EPB machines are more suitable for soft, cohesive soil with high clay and silt. The important EPB parameters studied are face pressure (chamber pressure control), soil conditioning and backfill injection pressures. Another important parameter is the injection of foam or grout through the shields [13]. The phenomenon of subsurface deformation, either settlement or heave due to the tunnelling, predominantly depending during the mining operation with the correct stabilization pressure, suitable soil conditioning method, controlled backfill grout for the annulus void together with skilled TBM operator. The EPB TBM used rotating cutter disc as tool excavation which are designed based on the geological profile and the excavated tunnel face are pressured using equilibrium face pressure by the TBM. The face pressure are calculated using Terzaghi Silo Method where the total horizontal lithostatic pressure[14] taken considers at the rest, centre and top of the tunnel. The Earth Pressure Balance (EPB) Shield which is applied if the ground contains a sufficiently high number of fines to allow its usage for face support. The muck is extracted from the chamber via screw conveyor and subsequently transported by belt conveyor to the end of the machine, where it is discharged onto a train or on a tunnel belt. Adequate conditioning of the soil is essential for EPBs as their face support can only be facilitated when the soil properties are plastic enough where the case normally in clayey, silty soils [15]. The summary of the factors affecting maximum surface settlement are shown in Table editor@iaeme.com

7 Abd Rashid, A.S, Mohamad, H and Ahmad,N. R Table 1 Summary of factors affecting maximum surface settlement Category Factors research by [11] Factors consideredin this study Shield operation factors 1. Face pressure (kpa) 2. Penetration rate (mm/min) 3. Pitching angle ( ) 4. Tail void grouting pressure (bar) 5. Percent tail void grout filling 1. Cutter Head (1) Rotation (r/min) (2) Torque (N/m) (3) Pressure (bar) 2. Screw Conveyor (1) Rotation (r/min) (2) Torque (N/m) 3. Earth Pressure (bar) 4. Total Grouting (m 3 ) 5. Tail Sealing Front and back(bar) 6. Foam Injection (1) Average (m 3 ) (2) Foam Additive (m 3 ) (3) Water Per Ring (m 3 ) (4) Solution Per Ring (m 3 ) 7. Soil Conditioning (1) Bentonite/Ring (m 3 ) (2) Cutter Head Flushing Water/Ring (m 3 ) Cutter Head The cutter heads are considered as TBM s excavation tool and located in front of the mechanized tunnel. These cutter heads are direct front contact between TBM and any geology of excavation face. The cutter heads are equipped with different types and customised discs with specific rotational and torque depending to type of soft ground or hard rock. From 5(a), the maximum rotational cutter head was recorded at 234Bar for Kenny Hill formation Thrust Speed The main hydraulic thrust cylinder is located inside the shield TBM which to secure segmental rings front advancement and at the same time to secure the position of each segmental rings. This telescopic cylinder also controls the TBM s alignment and significantly influences the surface settlement during the excavation work. In Figure 5(b) the maximum thrust speed was recorded as mm/min while the average reading was 32.44mm/min Screw Conveyor Every excavated material is transported from the pressurised cutter head section through screw conveyor which located at the invert of every TBM. The element of screw conveyor rotation is studied which any geological excavated material will affecting the muck and also any significant to surface settlement. The maximum rotation of CREC50 was 19.16r/min with an average of 6.70r/min as shown in Figure 5(c) Earth Pressure Every TBM earth pressure operation is controlled by the amount of excavated soil in front of the cutter head and transported by screw conveyor. Tunnel face pressure is primarily element in any tunnel excavation where it is been monitored inside the TBM control cabin. The average of face pressure applied within the range 1.25Bar with maximum pressure applied was 2.23Bar as shown in Figure 5(d) editor@iaeme.com

8 Predicting Maximum Surface Settlement Using Artificial Neural Network (ANN) To Earth Pressure Balance Tunnel Machine (EPBM) Formetro Line 1 In Kenny Hill Formation (a) (b) (c) (d) (e) (f) Fig. 5. Analyses across six sections for (a) Settlement vs cutter head, (b) Settlement vs Thrust Speed, (c) Settlement vs Screw Conveyor, (d) Settlement vs Earth Pressure, (e) Settlement vs Total Grouting, (f) Settlement vs Tail Sealing, (cont editor@iaeme.com

9 Abd Rashid, A.S, Mohamad, H and Ahmad,N. R (g) Fig. 5. (Cont.) (g) Settlement vs Foam Injection, and (h) Settlement vs Soil Conditioning Total Grouting The total quantity of grouting also plays a vital factor for ground settlement in every TBM operation. Two stage of bi-component grouting are pressurized outside of the installed after each segmental ring fully installed. The performance of total grouting volume is dependable to theoretical void filling between the segmental rings and TBM shield skin. The average total grouting for each segmental was 5m 3 while maximum grouted was 10.14m 3 as shown in Figure 5(e) Tail Sealing The tail sealing is located at the exterior of each segmental ring and seals the annular gap of tail skin and the outside of the tunnel ring. A special sealant is applied continuously while TBM advancement and during TBM start up process. Although this procedure is minor, but this operation shows significant influence on surface settlement. In Figure 5(f) scattered data was recorded for tail skin pressure applied which minimum less than 3kPa on site Foam Injection The amount of injected foam during an EPB machine is relatively low compared to water injection during the excavation. This application is normally used in saturated sand in which the front cutter head section is unable to produce sand or soil muck before transported to TBM conveyor. The application is mainly dependable to the soilin front of the cutter head and usually used in dry soil compared to saturated soil. Experience in KVMRT has shown that the maximum amount usage was below 3m 3 as shown in Figure 5(g) Soil Conditioning The usage of soil conditioning is to improve the ground during the TBM advancement. These chemicals are injected in front of the cutter head which significantly improve the permeability, plasticity and the soil texture. The aims of this application are to expedite the work and most importantly acts as lubricant to reduce the friction in front of the cutter head. The material used varies from foam, bentonite and water. From Figure 5(h) the usage was below 15m 3 with an average of 8.8m 3 /rings for KVMRT project. (h) editor@iaeme.com

10 Predicting Maximum Surface Settlement Using Artificial Neural Network (ANN) To Earth Pressure Balance Tunnel Machine (EPBM) Formetro Line 1 In Kenny Hill Formation 4. ARTIFICIAL NEURAL NETWORK 4.1. Background A neural network is a computing method which has layered structures that are similar of neurons in the brain, with layers of connected nodes. A neural network can learn from data so that it can be trained to recognize patterns, classify data, and forecast future events. An Artificial Neural Network (ANN) method has been used in complex nervous system interacting with neurons or nerve cell with large data which interconnecting assembly of simple processing elements, units or nodes based on animal neuron as mentioned by [11] as shown in Figure 6.The general model of ANN as described in the figure below where x= input, w=weight and =summation, f= activation or transformation function. Fig. 6 General detail of a neuron [11] 4.2. Feedforward back-propagation neural networks For this paper, the author used feedforward back-propagation method in which the nodes do not form a cycle and the information s is in one direction through the hidden nodes to output nodes. In other words, these are the simplest single-layer perceptron and commonly used as mentioned by [11]. In this paper, the author used single layer feedforward network performed in MATLAB which consisting of an input layer, one hidden layer and an output layer. Some researchers [16] implemented Particle Swarm Optimization (PSO) in ANN using MATLAB software where each analysis is conducted in three times cycles and the best value selected as representative for the whole model. This method shall be studied in the future publication Design of optimum ANN The design of optimum network for predicting surface settlement requires first step of training and testing of ANN using the available subset data with a pilot trial. Inside the data pilot trial, it would refer an onset of input data with observed or measured information. These data are divided into two sets which are training and testing while the network with the different hidden nodes are trained to convergence to the training samples. Later on, the network will measure the performance with the validation set and choosing the best performance from the whole network. Table 3 shows the selected parameter models for TBM1 with Network Architecture (1hidden layer with 30 hidden notes and 2000 training epochs) were trained and tested with the validation set. All input parameters from the validation set are nourished by the input nodes and weighted by hidden layer to output layer. If the output results and measured output indicated small different, the network is considered as functioning. Root Mean Square (RMSE) is used to evaluate the performance of the model according to these equations. RMSE also been used to do comparison the performance and the network editor@iaeme.com

11 Abd Rashid, A.S, Mohamad, H and Ahmad,N. R Equation (4) where, N is the number of patterns in the validation set, o is the output produced by the network and t is the target (desired) output as shown in Equation (4). The previous author [11] also mentioned that the ANN feed forwarding back propagation was successfully studied for Bangkok MRTA. Table 3 Performance of Neural Network Models using Levenberg-Marquardt algorithm for TBM1 North Bound Parameter Models Network Architecture Training epochs Regressio n, R Validation samples, RMSE (mm) Cutter Head Thrust Speed Screw Conveyor hidden layer, 30 Earth Pressure hidden notes 2000 Total Grouting Tail sealing Foam Injection Soil Conditioning ANN FOR PREDICTING SURFACE SETTLEMENT The major effects for surface settlement have been described previously by three factors namely tunnel geometry, geological conditions and EPB operational parameters. Therefore, a unique relationship needs to establish between all selected parameters and surface settlement for this case study. This ANN method using MATLAB approach has been selected with the aim to develop predictive relations and mapping all influencing parameters. The ANN model has been applied overall in six sections to selective greenfield locations within the Kenny Hill formation to CREC50 TBM and moving towards North Bound direction from Semantan North Portal to Muzium Negara alignment. At this stage, the author only focused on one direction of TBM and eight scenarios were tested by ANN with different parameters. ANN Result in EPB TBM for Kenny Hill formation from Semantan to Muzium Negara All eight parameters were tested and trained within six greenfield sections in Kenny Hill Formation or namely Underground Ground 1.Five additional parameters were chosen and compared[11]in which the author studied for Bangkok MRTA project editor@iaeme.com

12 Predicting Maximum Surface Settlement Using Artificial Neural Network (ANN) To Earth Pressure Balance Tunnel Machine (EPBM) Formetro Line 1 In Kenny Hill Formation Table 4 Summary of Regression, R and RMSE for Six Sections for TBM Parameters TBM Parameters Regression, R (Max to Min) TBM Parameter RMSE (Max to Min) Foam Injection Tail Sealing Cutter Head Thrust Speed Earth Pressure Soil Conditioning Soil Conditioning Screw Conveyor Screw Conveyor Total Grouting Total Grouting Earth Pressure Thrust Speed Cutter Head Tail Sealing Foam Injection From Table 4, the highest regression data contributed by Foam Injection of and followed by cutter head regression of Earth Pressure regression in third of which was slightly lower than cutter head. Soil condition and Screw Conveyor regression was between and 0.400, respectively. Both Thrust Speed and Tail Sealing for combined North Bound were between to The RMSE for each parameter also has been tested and Tail Sealing showed the highest RMSE of and Thrust Speed was slightly lower giving a reading of RMSE for soil conditioning was and both Soil Conditioning and Screw Conveyor were between 4.89 and Earth Pressure was while the lowest was Foam Injection of All graphs performance of ANN trained and tested are shown in Figure CONCLUSION The surface settlement study by EPB requires extensive on-site data and the authors have used online Maxwell Geosystem which was established for KVMRT Line 1 project. The data for EPB TBM by China Railway Engineering Corporation (CRE50) for North Bound has also been collected and manually keyed-in which involved extensive data. These data are important and have been analyzed in detail to study the first large diameter of transportation tunnel in Kuala Lumpur, Malaysia. The data studied in relation to TBM operational parameters in Kenny Hill formation has produced a unique relationship based on eight parameters. (a) (b) editor@iaeme.com

13 Abd Rashid, A.S, Mohamad, H and Ahmad,N. R (c) (d) (e) Fig. 7 ANN for TBM 1 (a) Foam injection, (b) Cutter head, (c) Earth pressure, (d) Soil conditioning, (e) Screw conveyor, (f) Total grouting (cont.) (f) (g) (h) Fig. 7 (Cont.) (g) Thrust Speed, and (h) Tail Seal editor@iaeme.com

14 Predicting Maximum Surface Settlement Using Artificial Neural Network (ANN) To Earth Pressure Balance Tunnel Machine (EPBM) Formetro Line 1 In Kenny Hill Formation The Artificial Neural Network (ANN) model was successfully developed to determine maximum surface settlement at selected GSM and eight parameters on board of the previous Earth Pressure Balance (EPB) in Kenny Hill formation. In this study, the authorsused one direction of TBM only instead of using both directions in six greenfield sections along 1km of alignment. It was found that ANN method can be used to predict surface settlement in correlation with regression between 0.70 to The summary of the findings is listed below: i. The Foam Injection Pressure in front of the cutter head has the highest regression to overall surface settlement. ii. The overall cutter head of TBM which includes rotation, torque and pressure have the significant influence for surface settlement. iii. The Face Pressure was the third major ranking for surface settlement influence for TBM1 drive while the lowest are Tail Sealing Pressure. This study also found out that the tested ANN parameters studied by the author however contradicted with previous researcher [17] according to three major ranking parameters; Face Pressure, Total Grouting and Tail Sealing influenced ground loss and surface settlement. On the other hand, RMSE results have shown that the tested ANN were in between of 3.7 and5.2 to each item mentioned above. It shows that each parameter tested in ANN will give highest regression value and lowest RMSE. REFERENCES [1] V. Fargnoli, D. Boldini, and A. Amorosi, Twin tunnel excavation in coarse grained soils: Observations and numerical back-predictions under free field conditions and in presence of a surface structure, Tunn. Undergr. Sp. Technol., vol. 49, pp , [2] R. P. Chen, J. Zhu, W. Liu, and X. W. Tang, Ground movement induced by parallel EPB tunnels in silty soils, Tunn. Undergr. Sp. Technol., vol. 26, no. 1, pp , [3] L. Ma, L. Ding, and H. Luo, Non-linear description of ground settlement over twin tunnels in soil, Tunn. Undergr. Sp. Technol., vol. 42, pp , [4] X. Xie, Y. Yang, and M. Ji, Analysis of ground surface settlement induced by the construction of a large-diameter shield-driven tunnel in Shanghai, China, Tunn. Undergr. Sp. Technol., vol. 51, pp , [5] E. Namazi., H. Mohamad, M.E. Jorat, and M. Hajihassani, Investigation on the effects of twin tunnel excavations beneath a road underpass, Electronic Journal of Geotechnical Engineering, vol. 16 (1), 1-8, 2011 [6] S. S. Gue and M. Singh, Design and Construction of LRT Tunnel in Kuala Lumpur, Malaysia, Semin. Tunn., pp. 1 21, [7] R. J. Mair, R. N. Taylor, and J. B. Burland, Prediction of ground movements and assessment of risk of building damage due to bored tunnelling, Geotech. Asp. Undergr. Constr. Soft Gr., no. August, pp , [8] B. Jones and C. Clayton, Guidelines for Gaussian curve-fitting to settlement data, pp. 1 8, 2013.World Tunnel Congress 2013 Geneva Underground the way to the future! G. Anagnostou & H. Ehrbar (eds) [9] R. B. Peck, Deep Excavation and Tunnelling in Soft Soils, 7thInt. Conf. on Soil Mechanics &Foundation Engineering. pp , [10] B. D. Jones and C. R. I. Clayton, ICE Research and Development Enabling Fund Grant 1021 Funders Report Surface settlements due to deep tunnels in, no. October, [11] S. Suwansawat and H. H. Einstein, Artificial neural networks for predicting the maximum surface settlement caused by EPB shield tunneling, Tunn. Undergr. Sp. Technol., vol. 21, no. 2, pp , editor@iaeme.com

15 Abd Rashid, A.S, Mohamad, H and Ahmad,N. R [12] Geotechnical Engineering Office, Ground Control for EPB TBM Tunnelling Ground Control for EPB TBM Tunnelling, no. 298.Civil Engineering and Development Department, The Government of Hong Kong, Special Administrative Region,GEO Report No.249, Golder Associate (HK) Ltd February [13] M. C. Gatti and G. Cassani, Ground loss control in EPB TBM tunnel excavation, Proc. 33rd ITAAITES World Tunn. Congr. Undergr. Sp. 4th Dimens. Metropolises, vol. 2, pp , [14] V. Fargnoli, D. Boldini, and A. Amorosi, TBM tunnelling-induced settlements in coarsegrained soils: The case of the new Milan underground line 5, Tunn. Undergr. Sp. Technol., vol. 38, pp , [15] W. Schaub, Multi-Mode and Variable Density TBMs Latest Trends in Developments, 2014Multi-Mode TBMs State of the Art and Recent Developments. Underground Space Engineering Conference. Kuala Lumpur. [16] S. Moosazadeh, E. Namazi, H. Aghababaei, A. Marto, H. Mohamad, and M. Hajihassani, Prediction of building damage induced by tunnelling through an optimized artificial neural network, Eng. Comput., vol. 0, no. 0, pp. 1 13, [17] N. Loganathan, An Innovative Method For Assessing Tunnelling-Induced Risks To Adjacent Structures, no. January, 2011.PB 2009 William Barclay Parsons Fellowship Monograph editor@iaeme.com