Non-structural fuzzy decision support system for evaluation of construction safety management system

International Journal of Project Management 20 (2002) 303±313

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Non-structural fuzzy decision support system for evaluation of construction safety management system C.M. Tam *, Thomas K.L. Tong, Gerald C.W. Chiu, Ivan W.H. Fung Department of Building & Construction, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong

Abstract Because of the poor safety records, there have been a number of safety improvement measures developed in the construction industry of Hong Kong. However, not all of these programs are cost, time and resources e€ective. If the degree of e€ectiveness of these programs can be compared and analyzed, it helps management focus their e€ort and better deploy resources. This study tries to evaluate the safety management systems and prioritize these measures with the consideration of various decision criteria. The Non-structural fuzzy decision support system (NSFDSS) is applied to facilitate the decision making process for these multi-objective problems. Modi®ed NSFDSS is presented that is suitable for the appraisal of complex construction problems, which allows assessment based on a pair-wise comparison of alternatives using semantic operators, even under the condition that insucient precise information is available. # 2002 Elsevier Science Ltd and IPMA. All rights reserved. Keywords: Safety Management; Multi-criteria decision making; Non-structural fuzzy decision support system

1. Introduction The building industry is characterized by continual changes, bombardment of varying technologies, poor working conditions and the involvement and the need for coordination of di€erent interdependent trades and operations [1]. Due to the hazardous nature of work, safety is a serious problem in the industry. In recent years, safety records in the construction industry are so worse that safety has become a matter of grave concern to the government and the public of Hong Kong. As a result, various safety management systems are introduced, aiming at e€ectively monitoring the safety policies, procedures, and practices within companies. However, safety practices encountered in construction sites are as varied as the sites themselves [2]. Also, different companies tend to have di€erent scale of safety management systems because of the limitation of resources. In small to medium ®rms, their safety programs are often very informal and unwritten while in large construction ®rms, such programs are always wellstructured and documented. This study provides a mean to evaluate the importance of various safety management systems so that the * Corresponding author. Tel.: +852-2788-7609; fax: +852-27887612. E-mail address: [email protected] (C.M. Tam).

limited resources can be allocated to these systems according to the order of priority for better resource deployment. To improve the evaluation process, a decision-setting model Ð the non-structural fuzzy decision support system [3] (NSFDSS) is modi®ed and applied to the priority setting process. A modi®ed model called ``NSFDSS II'' is presented with modi®cations to show the contributions of each system to the overall safety performance. This model delivers a method of ranking all elements on the basis of agreed criteria, which facilitates resolving complex multi-criteria problems. 2. Review of previous studies Previous research on construction safety can be generally categorized into two: (1) survey on the safety factors and performance; and (2) sharing practical experience on safety management. Summarizing the past surveys, Jannadi [4] indicated that the most important factors a€ecting the construction safety were: (1) maintaining safe working conditions; (2) establishing safety training; (3) cultivating good safety habits; (4) e€ective controlling of subcontractors; (5) maintaining a close supervision to workers; and (6) assignment of responsibility to all levels of management and workers. Further, Sawacha et al. [5] identi®ed ®ve important issues associated with site safety. They are: (1) management talk

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on safety; (2) provision of safety booklets; (3) provision of safety equipment; (4) providing safety environment; and (5) appointing a trained safety representative on site. As regards improving construction site safety, Hinze and Harrison [6] stated that formal safety training and safety awards were the most e€ective tools in educating site workers and mitigating site accidents. The Commissioner of Labour of Hong Kong [7] advocated the use of safety committees and post-accident investigation to combat site casualties. Lee [8] promoted the use of safety orientation programmes to impress new workers on safety awareness. Nattrass [9] recommended the appointment of safety ocers on sites to strengthen site safety supervision. Lai [10] attributed high site casualty rates to the use of labour-only subcontracting in the construction industry of Hong Kong. Hinze and Raboud [11] advocated top management involvement to reduce site accidents. These early research works provide useful guidelines on site safety. However, no one has attempted to prioritize these measures in terms of cost, time, and resources requirements. This paper attempts to develop a decision-making model for evaluating the e€ectiveness of these safety ideas. 3. Non-structural fuzzy decision support system There are three principles in using the NSFDSS: decomposition, comparative judgment and synthesis of priorities. First, the decomposition principle structures a problem into elements of di€erent levels, each independent of those on succeeding levels, and then working downward from the goal (e.g. the decision to do something) on the

top through criteria (e.g. time, cost, quality, etc.) on the second level, and then to sub-criteria (e.g. capability of sta€, ®nancial conditions, technology level, etc.) on the third level, and so on, working from the general (and sometimes uncertain) to the more speci®c at the lower levels (see Fig. 1). The principle of comparative judgment is applied to construct pair-wise comparisons of the relative importance of elements on some given levels with respect to the shared criterion or property on the level above, giving rise to the corresponding matrix. The third is the synthesis of priorities. In NSFDSS, priorities are synthesized from the second level down by multiplying local priorities with the priority of their corresponding criterion on the level above, and weighting each element on a level according to the criteria it a€ects. (The second-level elements are multiplied by unity, the weight of the single top-level goal.) This gives the composite or global priority of that element, which is then used to weight the local priorities of the elements on the level below, and so on, repeating this procedure to the bottom level. The strengths of NSFDSS are: 1. breaking the problem down into many pair-wise comparisons among the alternatives can reduce the diculty of making judgment; 2. applying logical consistency checks to the pairwise comparison and the consideration of comparison magnitude can enhance the accuracy of problem solving; and 3. using semantic operators that integrate the strength of fuzzy set theory further enhances the analysis of expert judgments.

Fig. 1. Decomposition structure of a multi-criteria problem.

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NSFDSS is similar to the analytical hierarchy Process (AHP), a widely used decision-making operational research technique [12±15]. The similarity of the two is that they both apply the three basic principles as mentioned above; then break down the problem into multilevels and compare each pair, one by one. They simplify the comparison of multi-criteria problems. Also, both o€er consistency checks to the pair-wise comparison matrix, ensuring the rationalization of the ®nal decision. However, in the pair-wise comparison, NSFDSS is obviously superior to AHP by adopting a ``logical checking'' which only consists of three options: 1. ``D1'' is better than ``D2''; or 2. ``D1'' is equally important as ``D2''; or 3. ``D1'' is worse than ``D2''. This approach much simpli®es the nine levels of comparison in AHP. During the consistency check, it is assumed that the upper rows of the matrix are more reliable then the lower rows and the system will re-set the values of the lower rows if inconsistencies are found. In other words, the earlier comparisons made are assumed to be more accurate, which resembles human beings' cognitive behavior. AHP gives a consistency index that has an upper limit of 0.1, exceeding which users should check the inputs manually and re-structure the matrix and the procedures again. However, NSFDSS has another procedure of ``priority ordering'' to measure the di€erence in magnitude of the ®rst ordered decision and others. It has 21 semantic operators, compared with nine of AHP. 3.1. Major elements adopted in safety management According to previous research, seven major elements of safety management system are identi®ed as follows. 1. Safety audit. Auditing is an essential process in the safety management cycle and it provides managers with further information on compliance with standards [16]. It is important to initiate safe work practices that are stemming from reliable and continuing feedback on the safety level observed [1]. 2. E€ective safety training. There have been much research on site safety training [1,16±18]. They emphasize the importance of training needs and believe that training can be used as a vehicle for attitudinal change. 3. Increase competency of supervision. Adequate supervision is a necessity to ensure e€ective safety [16]. Level of supervision needs to be carefully considered by sending site foremen and safety representatives to monitor the whole safety procedures. 4. Increase management involvement. Anton [17] stated that all levels of management must be involved in the activities required for planning, organizing, and controlling job-related health and safety activities.

305

Also, Hinze and Raboud [11] advocated top management involvement to reduce site accidents. 5. Safety promotion. It is important to make employees interested in their own safety and wellbeing. Anton [17] stated that safety promotion was persuasion through motivation. In addition, Hinze and Harrison [6] identi®ed that safety award was an e€ective tool to mitigate site accidents. In other words, employees should be motivated by incentive scheme to make them aware of site safety matters. 6. Establishment of safety policy. A prime requisite for any safety program is to leave no doubt in the mind of the employees that management is concerned about the prevention of accidents at the workplace [17]. Therefore, an e€ective safety policy is necessary to demonstrate the management's attitude towards safety matters and should make known to all levels of management and employee alike. It is suggested that the policy should outline the organization's aims and objectives for its safety program and should increase the accountability for the company. 7. Eradication of hazards. Managing the working environment and keeping the current condition safe is the key to enhance site safety. Cox and Cox [16] suggested that a safe system of work should be able to eliminate identi®ed hazards, and to complete the work with the minimum amount of risk. Anton [17] stated that managers must ensure the proper recognition of hazards in all activities where losses could occur. Colvin [18] de®ned the major responsibility of management was to identify the hazards to which employees would be exposed in the workplace and to eliminate the hazards through engineering methods. 4. Decision criteria in adopting safety management systems Seven decision criteria for safety management system are proposed and tabulated in Table 1. 5. Methodology of evaluation When the overall structure of the problem (shown in Fig. 2) has been formulated, the next is to apply NSFDSS II to systematically evaluate the available elements (En) under various decision criteria (Cn). The ¯ow chart of the model is illustrated in Fig. 3. 5.1. Step 1 Ð pairwise comparisons In the process of prioritization, pair-wise comparison is ®rst conducted between any two elements, forming a

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Table 1 Information of decision Criteria (Cn) Cn

Decision criteria

Description

C1

Time

C2

Cost

C3

Resources

C4

Company size

C5

Current legislation

C6

Quality of employee

C7

Project complexity

Time is essential for construction project as it is directly related to cost. Therefore, the time required to successfully implement the safety management system is critical. This refers to the system operating cost. It is a commonplace to incorporate safety cost in tender, thus, thorough consideration is necessary. Labour and technical supports are major resources for implementing an e€ective safety management system. With limited resources, reasonable and e€ective allocation is crucial. Company sizes prescribe di€erent scale of safety management. In other words, time, cost, resources and level of supervision involved may alter. In large countries, local authorities may impose di€erent requirements on construction ®rm, which varies from place to place. In Hong Kong, current legislation is subjected to continual changes to cope with changes in the industry. Therefore, it is important to consider the impact of legislation. Employees who are lack of proper training and education are not aware of the potential traps on construction sites. In addition, their quality directly a€ects the acceptance of new skills and technologies, which in turn may hamper the safety management. The more complicated projects, the more dicult is the safety system to be implemented. Accordingly, for complicated projects, safety systems should be well-structured and well-planned to enhance eciency.

Fig. 2. Overall structure of the safety management system evaluation.

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Fig. 3. Flowchart of the NSFDSS II.

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matrix form shown in Table 2. In this pair-wise comparison, there are three scales: better, same or worse: Mark for x to y 0 0.5 1

Scale In considering a particular decision criterion (Cn), = Element X is worse than Element Y = Two are the same = Element X is better than Element Y

The same evaluation is done eight times (seven for comparing the elements En under each decision criterion C1to7 and one for comparing the seven decision criteria). 5.2. Step 2 Ð consistency checking The principle of logical checking of the input matrix is presented in Eqs. (1) and (2). With respect to each decision criterion Cn, the matrix of pair-wise comparison of the corresponding element is: 2 3 ie11 ie22 . . . ie1m 6 ie21; ie22 . . . ie2m 7 7 ˆ …iekl † iE6 4: 5 : : : …1† iem1; iem2; . . . iemm k ˆ 1; 2; . . . n

l ˆ 1; 2; . . . n

Table 2 Input evaluation matrix form for C1a Element No.

1 2 3 4 5 6 7

Input values 1

2

3

4

5

6

7

0.5

1 0.5

1 0.5 0.5

1 0.5 0.5 0.5

0.5 0 0 0 0.5

0.5 0 0 0 1 0.5

0.5 0 1 0 0 0 0.5

a Assessment marks: 0, one is less important than the other; 0.5, both are equally important; 1, one is better than the other.

where iekl is the logical indicator of pair-wise comparison of element ``k'' and ``l''; m is the number of element to be considered. The evaluation matrix in Table 2 is transformed into the iE form of output matrix in Table 3. Priority matrix iE of pair-wise comparison is derived under the following conditions: …1† when

iehk > iehl

…2† when …3† when

iehk < iehl iekl ˆ 1 iehk ˆ iehl ˆ 0:5 iekl ˆ 0:5

iekl ˆ 0 …2†

where h=1,2, . . .,n, which is the reference element. When matrix iE complies with the consistency checking of priority ordering, it is named as the priority matrix with consistent indicators. There are ®ve conditions to check whether matrix iE satis®es the consistency checking of priority ordering; they are: 1. If iehk>iehl, then iekl0 (``greater than zero'' condition) where: iehk is the logical indicator of pairwise comparison of element Eh and Ek; iehl is the logical indicator of pair-wise comparison of element Eh and El; and iekl is the logical indicator of pair-wise comparison of element Ek and El. For example, in Table 3: (a) ie14=1, that is Element No. 1 > Element No. 4 (b) ie15=0.5, that is Element No. 1=Element No. 5 (c) Therefore, Element No. 5 > Element No. 4. 2. If iehk Element No. 4, that is ie54=1. 3. If iehk =0.5 and iehl=0.5, then iekl 0.5 (``Equal to 0.5'' condition). For example, in Table 3: (a) ie23=0.5, that is Element No. 2=Element No. 3 (b) ie24=0.5, that is Element No. 2=Element No. 4 (c) Therefore, Element No. 3=Element No. 4, that is ie34=0.5.

Table 3 iE form of output matrix for C1a Element No.

1 2 3 4 5 6 7 a

Output values 1

2

3

4

5

6

7

ie11=0.5 ie21=0 ie31=0 ie41=0 ie51=0.5 ie61=0.5 ie71=0.5

ie12=1 ie22=0.5 ie32=0.5 ie42=0.5 ie52=1 ie62=1 ie72=1

ie13=1 ie23=0.5 ie33=0.5 ie43=0.5 ie53=1 ie63=1 ie73=1

ie14=1 ie24=0.5 ie34=0.5 ie44=0.5 ie54=1 ie64=1 ie74=1

ie15=0.5 ie25=0 ie35=0 ie45=0 ie55=0.5 ie65=0 ie75=0

ie16=0.5 ie26=0 ie36=0 ie46=0 ie56=1 ie66=0.5 ie76=0.5

ie17=0.5 ie27=0 ie37=0 ie47=0 ie57=1 ie67=0.5 ie77=0.5

Assessment scores: 0, method X is worse than method Y; 0.5, two are the same; 1, method X is better than method Y.

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4. If iehk=1 and iehl=1, then iekl={0, 0.5, 1} For example, in Table 3: (a) ie13=1, that is Element No. 1 > Element No. 3 (b) ie14=1, that is Element No. 1 > Element No. 4 (c) it is possible that: (i) Element No. 3 > Element No. 4; or (ii) Element No. 3=Element No. 4; or (iii) Element No. 3 < Element No. 4. Therefore, in Table 3, ie34=0.5 is valid and not necessary to be revised. 5. If iehk=0 and iehl=0, then iekl={0, 0.5, 1} For example, in Table 3: (a) ie45=0, that is Element No. 4 < Element No. 5 (b) ie46=0, that is Element No. 4 < Element No. 6 (c) it is possible that : (i) Element No. 5 > Element No. 6; or (ii) Element No. 5=Element No. 6; or (iii) Element No. 5 < Element No. 6. Therefore, in Table 3, ie56=1 is valid and not necessary to be revised. After consistency checking eight output matrices are generated for further evaluation as presented in Fig. 4.

criteria Cn. Based on this priority order, experts can assign semantic operator, each represented by a number in the range of 1±21 (see Table 4 for the semantic score), to each elements by comparing each element to the one with the highest value (the bottom-up approach). Taking the priority order for C1 in Fig. 5 as an example, the previous step provides the Element number order of {2, 5, 7, 3, 1, 4, 6}. As Element 6 gets the lowest sum of 0.5, it is ®rst compared with Element 2. Their di€erence is judged by experts who then assign a semantic operator of ``absolutely incomparable'' to describe their relative importance. As a result, the priority score of ``0'' (see Table 4 for the scores) is assigned and the same process is repeated for all elements. The formulation of the semantic operator is detailed in Chen's work [3] and shown in Table 4. Each semantic operator (like marginally di€erent, quite di€erent, etc.) is assigned a score. These scores, ia1j, within the range of [0.5, 1] (0.5=same; 1=di€erent) are mapped into a priority score, irj, in the range of [1, 0] as shown in Fig. 6 by applying the fuzzy set theory through the following equation:

5.3. Step 3 Ð priority ordering and assignment of priority scores to element

irj ˆ

After the consistency check, the priority matrices of pair-wise comparison among the elements with respect to decision criteria Cn are con®rmed. Summing up the values of indicators on each row, the elements are then rearranged in a descending order with respect to decision

where ia1j is the semantic score and irj is the priority score. For example, in the table for C1 in Fig. 5, 0.176 was assigned to Element 4 as the experts have described the di€erence between Element 2 and 4 as ``signi®cantly di€erent'' while in the table for C3, 0.212 was assigned to Element 7 as the experts have described the di€erence between Element 1 and 7 as in-between ``obviously different'' and ``very di€erent''.

Table 4 Semantic operators, scores and transformed priority scores Semantic operators

Step

ia1j

irj

Same In-between MargInally di€erent In-between Slightly di€erent In-between Quite di€erent In-between Markedly di€erent In-between Obviously di€erent In-between Very di€erent In-between Signi®cantly di€erent In-between Very signi®cantly di€erent In-between Extremely di€erent In-between Absolutely Incomparable

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

0.5 0.525 0.55 0.575 0.6 0.625 0.65 0.675 0.7 0.725 0.75 0.775 0.8 0.825 0.85 0.875 0.9 0.925 0.95 0.975 1

1 0.905 0.818 0.739 0.667 0.6 0.538 0.481 0.429 0.379 0.333 0.29 0.25 0.212 0.176 0.143 0.111 0.081 0.053 0.026 0

1

ia1j ; 0:54ia1j 41 ia1j

…3†

5.4. Step 4 Ð derivation of weightings by normalizing semantic scores After obtaining the priority order of decision criteria and elements in Step 3, it is necessary to measure the magnitude of the pair-wise comparison by assigning

Table 5 Normalization of decision criteria priority scores into weighting Cn

Priority score

Normalization

Weighting (w)

C1 C2 C3 C4 C5 C6 C7 Total

0.333 0.429 0.333 0.053 0.538 0.053 1 2.739

0.333/2.739 0.429/2.739 0.333/2.739 0.053/2.739 0.538/2.739 0.053/2.739 1/2.739

0.1216 0.1566 0.1216 0.0194 0.1964 0.0194 0.3651

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weightings to these decision criteria and elements. The set of weightings (!) is developed from normalization of the semantic scores as shown in Tables 5 and 6. Let !=(w1, w2, . . . wn) be the weightings of decision criteria C1, C2, . . ., Cn.

5.5. Step 5 Ð determination of the results Knowing the weightings of each decision criteria and elements, a weight allocation diagram can be constructed to show the relative importance of elements under each

Table 6 Weighting of each element after normalization Element criteria

C1

C2

C3

C4

C5

C6

C7

E1 E2 E3 E4 E5 E6 E7

0.1234 0.2876 0.1547 0.0506 0.1918 0.0000 0.1918

0.2213 0.0476 0.1456 0.2706 0.2706 0.0300 0.0143

0.2595 0.0550 0.113 0.2595 0.2595 0.0000 0.0550

0.3555 0.0395 0.0188 0.1184 0.1913 0.2371 0.0395

0.0295 0.2658 0.2658 0.0295 0.0295 0.1140 0.2658

0.2706 0.0676 0.2213 0.0300 0.1623 0.1805 0.0676

0.0619 0.0619 0.1651 0.1332 0.1651 0.1651 0.2476

Fig. 4. Output matrices after consistency checking.

C.M. Tam et al. / International Journal of Project Management 20 (2002) 303±313

decision criteria (Fig. 7). Then, Eqs. (6) and (7) can be applied to calculate the contribution of each element (SPij) for a particular problem. SPij ˆ wi  rij

…6†

311

where: SPij=contribution of each element in the decision problem; wi=the weight of decision criteria ``i''; and rij=the weight of element for decision criteria ``i'' subject to:

Table 7 Contribution of each element (SPij) En

E1 E2 E3 E4 E5 E6 E7 Total

Cn C1

C2

C3

C4

C5

C6

C7

1.50% 3.50% 1.88% 0.62% 2.33% 0.00% 2.33%

3.47% 0.75% 2.28% 4.24% 4.24% 0.47% 0.22%

3.16% 0.67% 1.35% 3.16% 3.16% 0.00% 0.67%

0.69% 0.08% 0.04% 0.23% 0.37% 0.46% 0.08%

% 5.22% 5.22% 0.58% 0.58% 2.24% 5.22%

0.52% 0.13% 0.43% 0.06% 0.31% 0.35% 0.13

2.26% 2.26% 6.03% 4.86% 6.03% 6.03% 9.04% 100%

Fig. 5. Priority ordering and assignment of semantic score.

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C.M. Tam et al. / International Journal of Project Management 20 (2002) 303±313 n X m X SPij ˆ 1

…7†

iˆ1 jˆ1

Fig. 6. Mapping of ia1j to irj.

where n=total number of decision criteria and m=total number of elements. The calculated SPij is tabulated in Table 7. By summing up the values of each row, the total weight of each element is obtained. They are then subjected to the ®nal priority ordering by arranging the total weight of each element in descending order. The element with the highest weight indicates the most important contribution to the e€ectiveness of the safety management system (Table 8). Safety managers can then allocate resources with reference to the priority of each element.

Fig. 7. Weight allocation diagram of En under each Cn.

C.M. Tam et al. / International Journal of Project Management 20 (2002) 303±313 Table 8 Results of the safety management system evaluation Calculated weight Element No. Description of each element 0.17694 0.17230 0.17018 0.13738 0.12600 0.12173 0.09548

7 3 5 4 2 1 6

Eradication of hazards Increase competency of supervision Increase safety promotion Increase management involvement E€ective safety training Implement safety audit scheme Establishment of safety policy

6. Conclusions This paper demonstrates a systematic approach to evaluate the importance of each element in safety management. The results provide a guideline for contractors or safety managers to e€ectively determine their resource allocation to di€erent safety management systems. NSFDSS II systematically analyzes professional human judgments to generate the relative weightings for the decision factors and elements, making the decisionmaking model more realistic. The model provides an alternative technique for decision-making in the construction settings, where multi-objective problems are often encountered. Acknowledgements The work described in this paper was fully supported by a grant from City University of Hong Kong with the project number of 7001053. References [1] Laukkanen T. Construction work and education: occupational health and safety reviewed. Construction Management and Economics 1999;17:53±62.

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[2] Wilson JM, Koehn E. Safety management: problems encountered and recommended solutions. Journal of Construction Engineering and Management 2000;January/February:77±9. [3] Chen SY. Engineering fuzzy set theory and application. Beijing: State Security Industry Press, 1998. [4] Jannadi MO. Factors a€ecting the safety of the construction industry. Building Research and Information 1996;24(2):108±11. [5] Sawacha E, Naoum S, Fong D. Factors a€ecting safety performance on construction sites. International Journal of Project Management 1999;17(5):309±15. [6] Hinze J, Harrison C. Safety programs in large construction ®rms. Journal of the Construction Division, ASCE 1981;107(CO3):455± 67. [7] Labour Department of Hong Kong. Risk management handbook. location: The General Insurance Council of Hong Kong & Report of the Commissioner for Labour, Labour Department of Hong Kong, Government Printer, 1993. [8] Lee HK. Safety management Ð Hong Kong experience. Hong Kong: Lorrainelo concept Design, 1991. [9] Nattrass S. Chief inspector of construction, health & safety executive, Great Britain. Green Cross Magazine 1994;4(4):11± 16. [10] Lai MY. A review of the subcontracting systems in the Hong Kong construction industry, 1987, MSc thesis, University of Hong Kong. [11] Hinze J, Raboud P. Safety on large building construction projects. Journal of Construction Engineering and Management 1988;114(2):286±93. [12] Saaty TL. Fundamentals of decision making and priority theory with the analytic hierarchy process, Vol. VI. Pittsburgh: RWS Publications, 1994. [13] Fong SWP, Choi KYS. Final contractor selection using the analytical hierarchy process. Construction Management and Economics 2000;18:547±57. [14] Shen QP, Lo KK, Wang Q. Priority setting in maintenance management: a modi®ed multi-attribute approach using analytic hierarchy process. Construction Management and Economics 1998;16:693±702. [15] Alhazmi T, McCa€er R. Project procurement system selection model. Journal of Construction Engineering and Management 2000;May/June:176±84. [16] Cox S, Cox T. Safety systems and people. London: Reed Educational and Professional Publishing Ltd, 1996. [17] Anton TJ. Occupational safety & health management. 2nd ed. New York: McGraw-Hill Book Company, 1989. [18] Colvin RJ. The guidebook to successful safety programming. Boca Raton,: Lewis Publishers, 1992.