Expert system for D&B tunnel construction

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Underground Space the 4th Dimension of Metropolises Bartk, Hrdina, Romancov & Zlmal (eds) 2007 Taylor & Francis Group, London, ISBN 978-0-415-40807-3

Expert system for D&B tunnel construction

C.W. Yu & J.C. ChernSinotech Engineering Consultants, Inc., Taipei, Taiwan

ABSTRACT: To improve the technology for drill and blast tunnel construction in Taiwan, an expert systemconsisted of data bank, tool bank and decision making auxiliary system has been developed. Various toolscan be used efficiently in carrying out data collection, processing, analysis and evaluation work required inthe tunneling process. Using the successful tunnel case histories data learned from the past experiences, thesystem can provide multi-expertise assistance on the decision of support system and excavation procedures to beadopted in the construction site. The system can also provide rational estimation on the tunnel deformation under

the selected support system and construction procedure with the aid of an artificial neural network approach.Application of the system to actual tunnel construction is given for demonstrating the capability of the system.

1 INTRODUCTION

The engineering practice for drilling and blasting(D&B) tunnel construction in Taiwan adopts similarapproach used in the western countries. In the designstage, support requirements and construction proced-ures were pre-determined by using empirical rockclassification method, mainly based on limited geo-logical information and rock mass conditions obtainedfrom the preliminary investigations.

During construction, actual rock mass rating wasobtained at the tunnel face, and appropriate supportsystem and construction procedures were selected andimplemented. Monitoring instruments were installedfor observing the performance of rock masses sur-rounding the tunnel. Revision and remediation weremade on the support system or construction proced-ures, if necessary, to achieve a safe and economictunnel construction.

However, due to the poor rock condition oftenencountered in Taiwan, excavation large span tunnelthrough poor quality rock mass has resulted in numer-ous engineering difficulties in the past two decades.Thesedifficultiesincluded uncontrolled excessive tun-nel deformation, serious damage to the support systemand even tunnel cave-in.

Figure 1 shows an example of driving a largespan tunnel through a fault zone in northern Taiwanby using the traditional rock classification method.It required systematic pre-stressed tendons and re-mining and re-supporting the tunnel to overcome

the uncontrolled excessive deformation. Controver-sies over the adequacy of tunnel support designand construction procedures based on experiences of

Figure 1. Systematic pre-stressed tendons, re-mining andre-supporting had to be used to control large deformation oftunnel through a dry fault zone in the construction of theSecond Freeway in northern Taiwan.

different geological environments have become issuesfor the local tunneling community. This also calls forthe development of a more rational system to dealwith the processes in D&B tunnel construction. Thispaper presents the system and its applicationin a tunnelconstruction project.

2 EXPERT SYSTEM FOR TUNNELCONSTRUCTION

The tunnel expert system compiles expert knowledgeand tools for facilitating the data collection and pro-cessing, data analysis and evaluation, and decision

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Figure 2. Working processes of the expert systemdeveloped.

Table 1. Tool bank of the expert system.

Tools Main functions

TUN_MAP Geological Information Collecting andProcessing

TUN_KBL Key Block AnalysisTUN_AUX Construction Decision-making AssistanceTUN_DEF Deformation Prediction

TUN_CAD Construction SimulatingTUN_SAF Tunnel Safety EvaluationTUN_STA Construction Status Demonstrating

making in the tunnel construction processes. Figure 2shows the working processes of the system.

The working processes contain 3 major blocks, i.e.,(1) construction data collection at thesite, (2)tool bankfor various data processing, analysis, simulation andevaluation, and (3) decision making for support designand construction procedures recommendation, safety

evaluation and construction status demonstration.The functions of each module in the tool bank

are outlined in Table 1. The working process will bedescribed further in the following sections.

3 TOOL BANK

The tool bank of the expert system comprises of 7subsystems as shown inTable 1, andthe main functionsof the subsystems are:

(1) Geological Information Collecting and Process-

ing System (TUN_MAP) utilizes digital imagetaken on the tunnel face to retrieve the geologicalinformation for statistical analysis of joint systems

and production of developed geological map oftunnel surface.

(2) Key Block Analysis System (TUN_KBL) canbe used to perform key block analysis from theactual recorded joint information or statisticallygenerated joint information.

(3) Construction Decision Making Auxiliary System(TUN_AUX) is a case history based artificialintelligence system for making multi-expertiserecommendations on tunnel support system andconstruction procedures.

(4) Tunnel Deformation Prediction System (TUN_DEF) is an numerical analysis data based artifi-cial neural network system for providing a rationalestimation on the tunnel deformation under vari-ous geological and construction conditions.

(5) Tunnel Construction Simulation System (TUN_CAD) provides numerical analysis programs for

carrying out back analysis of monitoring data andforward prediction analysis of tunnel performanceby simulating the construction processes in thefield.

(6) Tunnel Safety Evaluation System (TUN_SAF)utilizes the monitoring tunnel deformation forevaluating the safety of tunnel construction byusing a case history based empirical tunnel safetycriterion.

(7) Construction Status Demonstration System(TUN_STA) uses the records collected duringtunnel excavation, including geological informa-

tion, monitoring data and construction progressrecords, for demonstrating the status of tunnelconstruction.

The application for some of the systems will bedemonstrated by the results of case study presented.

4 DECISION-MAKING MODULES

Decision-making modules include the recommen-dation of optimum tunnel support requirement andconstruction procedures, tunnel deformation estima-

tion and safety evaluation during tunnel constructionstages. It also provides the status of tunnel construc-tion for better control of construction progress andtunneling conditions by the tunnel engineer.

4.1 Optimum support requirement

A Decision Making Auxiliary System (TUN_AUX)is a case-based artificial intelligence system togetherwith the traditional empirical rock classification sys-tem. The working processes of system TUN_AUX areshown in Figure 3.

In the field, the parameters, including the tunnelspan, strength of rock material, rock mass classifica-tion and overburden depth, collected are used for the

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Figure3. Flow chart of applicationof rock supportdecision-making auxiliary system.

expert system. TUN_AUX can give a multi-expertise

recommendations on the major support elements suchas the length and spacing of rock bolts, thickness ofshotcrete, size and spacing of steel sets, etc.

4.2 Optimum construction procedures

The Decision Making Auxiliary System (TUN_AUX)also provides a function for the recommendation ofoptimum construction procedure. The excavation pro-cedures suitable for various tunnel spans and groundconditions were categorized into full face, centraldiaphragm (CD) and side drift. Based on the expert

knowledge collected from experienced tunnel engin-eers and the results of construction simulation undervarious tunneling conditions, the optimum construc-tion procedure is suggested by the tunnel span andstrength/stress ratio at the construction site. The chartfor selecting a suitable construction procedure isshown in Figure 4. Figure 5 shows the construc-tion of a four-lane freeway tunnel under poor groundand very shallow rock cover by using dual side-driftmethod. Severe unstable condition was developed in a3-lane adjacent tunnel with top heading and benchingprocedure.

4.3 Tunnel deformation estimation

Tunnel Deformation Prediction System (TUN_DEF)provides an estimation on the magnitude of tunneldeformation expected under various geological andconstruction conditions. The system was developedbased on large amount of 3-D numerical simulationsof the tunnel construction under various geologicalconditions, excavation sequences and support meas-ures. Tunnel deformations, including roof settlement,horizontal convergency, as well as plastic zone thick-ness were given by a back propagation artificial neural

network from the data base. It is intended to providea rough estimate on the order of magnitude of tunneldeformation expected as guidance for tunnel engineer.

Figure 4. Optimized chart for selecting a suitable excav-ation procedure according to tunnel span and the strengthstress ratio.

Figure 5. Four-lane freeway tunnel driven by dual side-driftexcavation method adjacent to a shallow slope.

4.4 Tunnel safety evaluation

Empirical tunnel safety criterion based on case histor-ies in Taiwan (Chern, et al. 1998) is used in the safetyevaluation of tunnel. In the criterion, the information

of the tunnel inward movement (), tunnel radius (R),rock mass strength (UCS) and actual performance oftunnel case histories were used to establish 3 warninglevels of tunnel safety and the necessary measures tobe taken.

Below warning level I, tunnel is considered to be instable condition, and no special measure is required.Between warning levels II and III, tunnel is susceptibleto instability, and review of support system and con-struction procedures adopted should be made. Abovewarning level III, tunnel is in unstable condition, andremedial measures should be taken to stabilize the

tunnel.Figure 6 shows the flow chart of tunnel safety

evaluation using TUN_DEF and TUN_SAF systems.

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Figure 6. Flow chart of tunnel deformation prediction andsafety evaluation.

Figure 7. Geological profile of the flood diversion tunnel.

Figure 8. Tunnel section showing the excavation span andexcavation steps.

5 CASE STUDY

To illustrate the application of the system developed,a case study on a flood diversion tunnel in northernTaiwan is presented.

The diversion tunnel is 2,835 m long drivingthrough sedimentary rock formation of Miocene Age.The geological profile is shown in Figure 7. The max-imum excavation span is 14.1 m and was excavated in3 stages as shown in Figure 8. The major difficultiesencountered in tunneling are associated with the faultzones and the disturbed zones adjacent to a volcanic

intrusion.The rock support system was designed according

to the traditional RMR rock classification method.

Table 2. Suggested support elements predicted by thesystem.

Advance Rockbolt Shotcrete Steel-Support type per round length thickness rib size

Design Type III 1.01.4 m 4 m 20 cm G220Design Type IV 0.81.2 m 6 m 25 cm G220

Suggested 1.52.0 m 68 m 2025 c m H125Type III

Suggested 1.01.5 m 810 m 2025 c m H150Type IV

Use lattice girdle. Use H-beam.

Figure 9. The highly disturbed rock exposed at 2k+008.

Five support types were proportioned according to the

RMR rating. The rock support requirements were alsoexamined by using the expert system TUN_AUX dur-ing construction. The results for type III and type IVrock areshown inTable 2 along with theoriginal designof support system.

Most of the tunnel sections belong to the good qual-ity rock with type II and type III support systems. Thetype III support adopted worked quite well. However,in a disturbed zone adjacent to the volcanic intrusionnear station 2k+008, the poor quality rock mass (Fig-ure 9)withRMR values in the range of 28 to 34showedsqueezing behavior under 140 m overburden stress.The monitoring results during tunnel excavation are

shown in Figure 10.The monitoring data for top heading excavation

showed potential unstable condition according to thetunnel safety evaluating system TUN_SAF as shownin Figure 11. This proved to be true by the tunnelconvergence occurred during subsequent benching.

Reviewing the support requirement and excavationprocedure, it was found that the expert system wouldsuggest a more conservative support system and exca-vation procedure. These can be shown by the longerbolt length as suggested in Table 2, and excavation byCD or side drift method recommended as shown in

Figure 12.Before benching, various remedial measures were

further studies by using 3-D numerical simulations.

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Figure 10. Monitoring results during tunnel excavation indisturbed zone near 2k+008.

Figure 11. Potential unstable condition according toTUN_SAF evaluation.

Figure 12. Excavation by CD or side drift method asrecommended.

The predicted tunnel deformations after benching

down are shown in Figure 11. Due to limited optionavailable at the construction site, only additional9-meter long systematic bolts were installed to

enhance the tunnel stability. The tunnel did not sufferfrom severe unstable condition during benching, butrather large tunnel convergence occurred with accom-panying cracks of shotcrete in the tunnel crown area.This is also predicted by the tunnel safety evaluationsystem as shown in Figure 11.

6 CONCLUSIONS

An expert system consisted of tools and expert know-ledge for performing construction data collection andprocessing, data evaluation and analysis, and decisionmaking assistance in the working process of D&Btunneling was developed. The results of preliminaryapplication of the system in the construction of aflood diversion tunnel showed the system can provideefficient tools for carrying out various data collec-

tion, analysis and evaluation work in the tunnelingprocess. Rational suggestions on support require-ment, construction procedure and safety evaluationcanalso assist thef ield engineer in thedecisionmakingprocess.

However, In a practical application sense, the sys-tem developed is considered to be a prototype dueto the lack of precedence and case histories withsufficient accuracy covering wide range of tunnelconditions. For further improvement of the system,trial application and collection of additional caseinformation are still needed.

REFERENCES

Hoek, E. and Brown, E.T. 1980,Underground excavations inrock, London: Institution of Mining and Metallurgy.

Dershowitz, W.S. and Einstein, H.H. 1984, Application ofArtificial Intelligence to Problems of Rock Mechanics,25th W. S. Symp. Rock Mechanics, pp.483494.

Zhang, Q., Mo, Y. and Tian, S. 1988, An Expert Systemfor Classification of Rock Masses, Proc. 29th U.S. Symp.Rock Mech., pp.283288.

Butler, A.G. and Franklin, J. A. 1990, Classex: Expert Systemfor Rock Mass Classification,Proc. ISRM Int. Symp. RockMech., Mbabane, Swaziland, pp.7380.

Zhang, Q., Nie, X.Y. and Tian, W.T. 1995, A Case-BasedReasoning for Tunnel Support, Proc. 8th Int. Cong. RockMech., Tokyo, pp.907909.

Chern, J.C., Shiao, F.Y. and Yu, C.W. 1998, An empiricalsafety criterion for tunnel construction. Proc., Reg. Symp.Sedimentary Rock Engineering, Taipei, 325330.

Palassi,M. andFranklin, J.A.1998,Tunnex:An Expert Systemfor Tunnelling through Rock. Tunnelling Association ofCanada (TAC) News, Vol. 16, No. 1, pp.1218.

Hoek, E. 2001. Big tunnel in bad rock. Journal of Geotech-nical & Geoenvironment Engineering, Vol.127, 726740.

Liu, J.M. 2005. River Yuanshantzu flood-diversion project.

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Expert system for D&B tunnel construction

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