CIB112 An Approach For Hazard Assessment On Construction Sites Using Fuzzy Sets

4 TH Triennial International Conference Rethinking and Revitalizing Construction Safety, Health, Environment and Quality Port Elizabeth South Africa 17-20 May 2005 ISBN 0-620 -33919-5 pp. 832-848 CIB112 An Approach For Hazard Assessment On Construction Sites Using Fuzzy Sets G. Emre Gürcanli *, Murat Kuruoglu*, Ugur Müngen* ABSTRACT Complexity and variety of the construction works creates hazardous and unsafe conditions for construction workers. Moreover, the poor safety performance of the construction industry emphasizes the importance of the safety management on construction sites. In the scope of this study, about 40.000 occupational accidents in all industries investigated. These records are taken from the Social Insurance Institution (SII) General Directory archives in Ankara and 4347 of them injuries occurred on construction sites. In addition to 4347 files, 892 court expert reports, which are submitted to criminal and labor courts are examined thoroughly. Not only for the construction industry, but also in other industries, classical safety checklists based on the Boolean expressions such as safe/unsafe, safety conditions satisfied/unsatisfied etc. However, the nature of the construction work, uncertainties inherent to the each condition and on site inspections generally linguistic expressions rather than metrics are used. For these cases fuzzy set analysis is an efficient tool to work with problems where no sharp boundaries (or problem definitions) are possible. In assessing safety level of a system what we have are nonquantitative estimates from experts. Therefore in this study, an approach for personal hazard assessment on construction sites with a fuzzy rulebased safety analysis is recommended. Accident Likelihood, Accident Severity, Current Safety Level and Hazard Level fuzzy set definitions depicted and fuzzy set graphics and IF-THEN rules are presented in the scope of the study. Keywords: Construction Safety, Fatal Injury, Fuzzy Sets, Accident Severity, Hazard Assessment * Corresponding author at: Technical University of Istanbul, Civil Engineering Faculty, 80626 Maslak/Istanbul. Fax: +90 212 285 65 87. E-mail address: egurcanli@ins.itu.edu.tr 832

1. INTRODUCTION Especially for the construction sites, there is often inadequate data or imprecise information available when making safety assessments desired to be made. Because of this negative situation, conventional approach es may not be able to model safety for whole construction process effectively and efficiently. The mentioned imprecision may be dealt with using fuzzy rules in fuzzy inference system, where the conditional part and/or the conclusions contain linguistic var iables. (1) By this approach experts need not know the precise point at which risk factor exist or the level of safety defects. Conventional approaches often fall short in their ability to permit the incorporation of subjective and/or vague terms as they rely heavily on supporting statistical information that may not be available. Not only for the construction industry, but also in other industries, classical safety checklists based on the Boolean expressions such as safe/unsafe, safety conditions satisfied/unsatisfied etc. However, the nature of the construction work, uncertainties inherent to the each condition and in the site inspections generally linguistic expressions rather than metrics are used. For these cases fuzzy set analysis is an efficient tool to work with problems where no sharp boundaries (or problem definitions) are possible. (2) While Boolean approach asks is the scaffolding safe?, the fuzzy logic approach asks how safe is the scaffolding?. Additionally it may be said that, for those countries where objective probabilistic data for risk or hazard assessment is extremely rare or insufficient, the utilization of subjective judgemental data based on expert s experiences is inevitable. 2. METHODOLOGY The beginning of this study involves various knowledge acquisition techniques to generate a body of information that could be used while developing fuzzy linguistic variable and their associated membership functions to qualify risk levels. The fatal injury data from the researches performed in the ITU Civil Engineering Faculty Construction Management Department for different types of construction sites are used in this study. In the scope of the study, about 40.000 occupational accidents in all industries investigated and 4347 of them are injur ies occurred on construction sites. In addition to 4347 files, 892 court expert reports, which are submitted to criminal and labor courts are examined thoroughly. The likelihood of each type of accident for different kinds of construction works are derived from the data and presented in Table 1. For example the likelihood of fall from height is highly frequent for building sites while for highway works, the traffic accidents on site observed highly frequent. Gathered data analysis indicates the weight of accident type (or cause of injury) for each kind of construction work. After that, the severity of each type of accident is determined. The third antecedent is Current Safety Level, which indicates the level of safety conditions satisfied for each type of accident. Among all the safety conditions to be satisfied, some are considered more important than the others. Therefore, improvement on these more important requirements has stronger influence on safety level. In this study, the pair wise comparison method is utilized. For each type of accident, these linguistic 833

variables combined with a fuzzy rule-based system and hazard level is found. At the last phase, hazard levels are summed up and the hazard level of the construction site is defined. Table 1. Fatal injuries, cause of accident vs. types of construction works Building Highway Railway Channel works Bridge Tunnel works Marine cons. Dams Demolition Energy Transmissi Other types Total Fall from height 880 8 1 19 15 0 2 13 8 22 60 1028 Contact with electricity 255 1 0 5 0 0 4 6 1 9 12 293 Accident by falling object 150 15 4 11 3 15 3 22 4 6 18 251 Heavy equipment accidents 33 90 0 13 4 2 6 19 1 7 31 206 Traffic accident on site 22 74 2 1 13 2 1 3 14 1 5 12 168 Building or Structure Collapse 105 1 0 2 4 0 0 1 41 0 13 167 Cave-ins 60 4 0 68 1 1 3 0 1 0 138 Other causes of accidents 24 9 0 7 6 0 2 7 1 6 25 87 Fire or Explosion 4 24 0 8 0 5 0 3 0 1 5 50 Material bouncing to face or other parts of the body 2 1 0 1 1 0 1 0 0 2 2 10 Total 1535 227 2 6 147 36 24 24 85 58 58 178 2398 Accident Likelihood, consequent severity, current safety level and the consequent part of the rule-based system hazard level, are presented as members of fuzzy sets in this paper, combined by matching them against rules in a rule base, evaluated with Mamdani-type inference system and then defuzzified to assess the hazard level of the job site. Safety analyst can assess the current safety level of the system directly giving points 1 to 10 for each safety element than translated to linguistic terms and these points are combined with linguistic terms to deal with accident 834

likelihood and severity. Moreover, the consequent part of the rule-base is also a fuzzy parameter and do not give a precise hazard level of the system, rathe r it gives a range and different alternatives for the system for the implementation of the safety practices. 3. Theoretical background of the Fuzzy logic approach Fuzzy logic could be confused with the traditional probability theory, however the former can measure the degree of membership in a set, the latter only measures the likelihood of an event to be in that set (3) The difference, as shown in Figure 1, is that, while in a Boolean set, an element can only be inside or outside the set, the element in Fuzzy Logic can be partially or totally inside or outside. The position of the element is described by the membership function () that has a value of one (=1), if the element belongs completely to the set; a value of zero (=0), if the element does not belong to the fuzzy set; and any value between zero (0<<1) and one, when the element belongs partially to the fuzzy set. Well defined boundaries Ordinar y set The dot is into the set Fuzzy set Fuzzy boundaries The dot is partially into the set The membership function of the dot is µ=0.5 Figure 1. Difference between an ordinary set and a fuzzy set. (4) The inputs to the fuzzy logic models are linguistic variables and its fuzzy sets. The outputs can be linguistic variables described by fuzzy sets (Mamdani model) or linear functions (Sugeno model). (5) The fuzzy sets for the inputs are related to the output fuzzy sets through if-then rules, which describe the heuristic knowledge about the behaviour of the system. The process of fuzzification of the inputs, evaluation of the rules and aggregation of all the required rules is known as fuzzy inference. The mathematical foundations of fuzzy logic an the fuzzy inference can be found in many resources (2, 5-8). The fuzzy system that will be described below is based on a Mamdani model (5) with the characteristics explained in Table 2. There are many techniques for obtaining the fuzzy relation R based on the IF A, THEN B or R = A B These are known as fuzzy implication operations and they are valid for all values of x X and y Y. The following form of the implication operator show the Mamdani implication technique for obtaining the membership function values of fuzzy relation R on the Cartesian product space XxY: 835

R@(x) = min(a(x), B(x)) (3.1) The equation above has been given various terms in the literature; it has been referred to as correlation-minimum and as Mamdani s implication (9). This formulation for the implication is also equivalent to the fuzzy cross product of fuzzy sets A and B. Table 2. Characteristics of the Mamdani Model Operation Operator Norm Formula Intersection (OR) MAX T-conorm * C (x) = max( A (x), B (x))= A (x)v B (x) Union (AND) MIN T-norm ** C (x) = min( A (x), B (x))= A (x) L B (x) Implication MIN T-norm max.(min( A (x), B (x))) Aggregation MAX T-conorm Defuzzification Center of N.A z µ c( z ) dz COA = z* = Methodology Mass of the µ c ( z ) dz surface C (x) = value of the resultant membership function. C (x) = value of the membership function where the input belongs to the fuzzy set A. z = abscissa value, ( C (z) is the ordinate). * T -conorm (also known as S-norm): A two-input function that describes a superset of fuzzy union (OR) operators, including maximum, algebratic sum and any of several parameterized T-conorms. ** T-norm: A two -input function that describes a superset of fuzzy intersection (AND) operators, including minimum, algebratic product and any of several parameterized T-norms It is the core of a fuzzy logic system in the sense that all other components are used to implement these rules in a reasonable and efficient manner. Specifically, the fuzzy knowledge/rule base comprises the following fuzzy IF THEN rules: IF x 1 is A 1 and and x n is A n, THEN y is B, (3.2) where A 1 and B are fuzzy sets in U i R and V R, respectively, and x=(x1,x2,,x n ) T U and y V are the input and output (linguistic) variables of the fuzzy system, respectively. In the hazard assessment system framework, it is necessary to represent human knowledge and experience in the form of the fuzzy IF THEN rules as given in equation (3.2). 4. Fuzzifier and Defuzzifier The fuzzy rule based rules are combined by fuzzy inference engine. The fuzzy inference engine performs a mapping from fuzzy set A in U to fuzzy set B in V. The fuzzifier is defined as a mapping from a real-valued point x * U R n to a fuzzy set A in U. The input is at x *, the crisp point, and the fuzzifier have to take into account this fact. Moreover, the fuzzifier should simplify computations dealt in the fuzzy inference engine. In our study, output of the fuzzy process needs to be a single scalar quantity, i.e. Hazard Level for our case, as opposed to a fuzzy set. 836

Defuzzification is the conversion of a fuzzy quantity to a precise quantity, just as fuz zification is the conversion of a precise quantity to a fuzzy quantity. The output of a fuzzy process can be the logical union of two or more fuzzy membership functions defined on the universe of discourse of the output variable. At least seven methods in the literature are commonly used for defuzzifying fuzzy output functions, among the many that have been suggested by researchers. 5. FUZZY SET DEFINITION FOR ACCIDENT LIKELIHOOD AND CONSEQUENT SEVERITY As mentioned before, the likelihood of each particula r cause of accident differs for different types of construction work. In Table 1, the distribution of fatal and nonfatal injuries investigated according to the different constructions sites and accident causes shown in a cross tabulated manner. In the approach presented in this paper, accident likelihood and fuzzy set definition of each cause of accident defined according to the construction work. For example accident likelihood for falls is Highly Frequent for building sites, while very low for tunnel works. Because if it is desired to implement a safety management system and necessary measures required for a construction site, for example for tunnel works, the importance of the scaffoldings of course cannot be compared with fire or explosion. For that kind of works, likelihood of injuries by falling object or explosion is higher than other causes of accidents. By taking into account the different characteristics of the different construction sites, hazard assessment and safety management can be effectively implemented. By the combination of subjective judgement and gathered data, the linguistic variables are employed to develop fuzzy membership functions for accident likelihood. When human experts are asked to evaluate a variable, they give us words. Fuzzy linguistic variables are extensions of numerical variables in the sense that they are able to represent the condition of an attribute at a given interval by taking fuzzy sets as their values. (10). The Accident Likelihood (AL) is one of the fundamental parameter used to assess the Hazard Level (HL) of a construction site. The other parameter is the Consequent Severity and these two parameters are represented by natural languages, which can be further described by the membership functions. In Table 3 and 4 the definition for Accident Likelihood and Consequent Severity are shown. The literature search indicates that for to seven levels of linguistic variables are commonly being used to represents risk factors in risk analysis (12-18) For each accident it is important to take into account the severity of the event. In the further pages, it will be seen that, if the severity of an accident is high, whether it is frequent or not, the safety management system should focus on that accident cause and implement necessary safety measures to eliminate the potential hazard. However, a question arises when defining the consequent severity for different types of accident; how can we define consequent severity, say for falls, for different leading causes of accidents? (It should be noted here by saying consequent severity, only impact of the accident on the employers are considered, environmental or financial impacts are not taken into account in this study) Here, again the combination of the subjective judgement of the exp erts (four 837

academicians which have also experience in labour and criminal courts as expert witness) and gathered data helps when defining the consequence severity and its fuzzy set definition. After a small discussion the experts decided the consequent severity of each accident unanimously. In Table 5, the consequent severity of each accident is shown. When depicting the membership degrees, Zadeh s notation is used. For example, falls has a membership degree in fuzzy catastrophic set 0.3 and 0.7 membership in fuzzy severe set. These fuzzy set definitions are utilized in the rule base system later. Table 3. Accident Likelihood (AL) for each particular cause of accident for different construction works Accident Definition Likelihood Very Low That type of accident is unlikely but possible during project time <1.0 Low Likely to happen once during project time 2.5 Reasonably Low Between low and average 5.0 Average Occasional accident 10.0 Frequent Repeated accident 20.0 Highly Frequent Accident almost unavoidable >30.0 *These values are the combination of subjective judgement and gathered data According to the different kinds of construction sites, likelihood of a particular cause of accident in %* Table. 4 Consequent Severity Definition Consequent Rank Severity Definition 1 Negligible No serious injury or damage to health 2,3 Minor Minor injury, not detrimental to individual employability or to the performance of present work. 4,5,6 Moderate Minor to major injury but occasional fatality. Detrimental to the performance of present work such as curtailment of activities or some days absence to recover fully, maximum one week. 7,8 Severe Fatalities occur frequently, major injury. Leading to permanent partial disablement or unfitness for work or detrimental to performance of work over extended period, such as long term absence. 9,10 Catastrophic Fatalities occur highly frequently, serious injury. Also includes the possibility of multiple fatalities in close succession due to the incident, e.g. Explosion A membership value (or degree of membership) between 0 and 1 as mentioned before. The most convenient and simplest membership functions are formed using straight lines. Triangular membership function and trapezoidal membership are the 838

simplest and easy to deal with. These kinds of membership functions commonly used to describe the parameters in safety assessment. (16) Fuzzy set definitions and membership diagrams are shown in Figure 3 and 4. Table 5. Consequent Severity of each cause of accident Accident Type Fall from height Contact with electricity Accident by falling object Heavy equipment accidents Traffic accident on site Building/Structure Collapse Cave-ins Fire or Explosion Material bouncing to parts of the body Other causes of accidents Consequent Severity = 0.3/catastrophic + 0.7/severe = 0.25/catastrophic + 0.75/severe = 0.1/severe + 0.9/moderate = 0.2/catastrophic + 0.8/severe = 0.05/cat astrophic + 0.65/severe + 0.3/minor = 0.5/catastrophic + 0.5/severe = 0.5/catastrophic + 0.5/severe = 0.6/catastrophic + 0.4/severe = 0.80/mino r + 0.2 /negligible = 0.2/moderate + 0.7/minor + 0.1/severe 1.2 v. low low r. low average frequent highly frequent 1 0.8 0.6 0.4 0.2 0 0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 Figure 3. Fuzzy Accident Likelihood set definition 1.2 Negligible Minor Moderate Severe 1 0.8 0.6 0.4 0.2 0 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 Figure 4. Fuzzy Consequent Severity set definition. 839

6. FUZZY SET DEFINITION FOR CURRENT SAFETY LEVEL In this stage of the study, a checklist prepared to assist those employers and employees who seek to comply with the rules and regulations of the Occupational Safety and Health Act (USA). The standards referred to are Federal Occupational Safety and Health Standards for the construction industry, 29 CFR (Code of Federal Regulations) Part 1926 and other selected General Industry Safety and Health Standards, Part 1910 (20) having applicability to construction work. But this checklist is different from the conventional ones. While the checklist prepared according to the Boolean approach asks is the scaffolding safe? for example, the approach presented in this paper asks how safe is the scaffolding?. Using this checklist an expert can evaluate an item related to site safety, a scale between 1 and 10. For example the question to answer is how are the extension platforms outside a wall properly guarded with side rails or equivalent 1926.500(c)(2) guards? Experts are given a point between 1-10. On the other hand, in the checklist, the safety measures to be taken grouped according to the leading causes of accidents occur in Turkish construction industry. Safety measures against falls, contact with electricity, heavy equipment accidents, and traffic accidents on site and so on grouped separately. On the other hand, all the safety measures for each accident cause weighted by experts. From the experience of the experts, it is essential to identify the most significant safety measures to be taken for each cause of accident to reduce the potential hazard on construction site. Among all the safety measures, some are considered more important than the others. Therefore, improvement on these more important safety measures has stronger influence reducing hazard regarding that type of accident. This identification process highly depends on the past experience and subjective judgements of the experts, moreover it is a complicated task. In this research, the pairwise comparison method is utilized. (21) Pairwise comparison starts with comparing the relative importance, or importance ratio, of two selected items. If n items are associated with en weights, w1, w2,.wn, the relative importance, aij, considering the ith item and the jth item is obtained as w i a ij = (6.1) wj The pairwise ratios satisfy; (A ni)w = 0 (6.2) where I is a nxn identify matrix. From this equation, it is apparent that n is an eigenvalue of A, and w is an eigenvector for eigenvalue n. In general case, it cannot be given the precise values of w 1 /w 2 but only estimates of them. The estimation errors result in inconsistency of the data in the pairwise ratio matrix. Saaty introduced a consistency index, CI, as a measure to evaluate the deviat ion from consistency of the pairwise ratios. (21) CI is calculated by CI= ( max n)/(n -1) (6.3) 840

where max is the maximum eigenvalue of A considering estimation errors. When values of the elements of a reciprocal matrix are generated randomly, the consistency index for this matrix is shown as RI. The ratio of CI to RI for the same order matrices is called the consistency ratio (CR). A pairwise ratio matrix with consistency ratio less that 0.10 is considered as a good one to calculate the weights of the items. When identifying the importance measures of safety measures to be taken for each accident cause (to eliminate potential hazard), experts are asked to specify how a particular need is more important than another one. The comparison values, a ij, are defined on a scale of 1 to 9, as recommended by Saaty and shown in Table 6. Table 6. Scale for comparison of safety measures Numerical Comparison of the ith safety measure and jth safety measure Values 1 Equally important 3 Slightly more important 5 Strongly more important 7 Very strongly more important 9 Extremely more important 2, 4, 6, 8 Intermediate values to reflect compromise Reciprocals Used to reflect dominance of the second alternative as compared with the first The consistency index, CI, is calculated less than 0.1 for all matrices. The corresponding eigenvectors for the eigenvalue l max were calculated for each matrix. The elements of these eigenvector than normalized for representing the weights for each safety measure. Using the mentioned checklists safety analyst evaluates the safety requirements, according to OSHA Standards. As mentioned before, the analyst do not check whether the requirement satisfied or not, however, he/she gives points between the scale of 1 to 10 and these points multiplied by the importance measures (relative weight) of each requirement. This point is also important, because it is a new approach for construction safety checklists, by considering the relative weights of each safety measure, and the system does not regard all the safety measures to be equal. When these kinds of facts are regarded, it is necessary to give weights to the basic safety measures for each leading cause of accidents. On the other hand, the checklist proposed needs to be developed; regarding safety management items those must be satisfied to establish an effective safety system in the site. As an example, the checklist for accident by falling object and finding the current safety level for prevention of these kind of accidents are depicted in Table 7. In the next section, with an illustrative example, how the system works will be explained. As a quantitative data, the total point showing the level of safety conditions for accident by falling objects does not mean anything for the first stage of the hazard assessment. This numerical data should be translated into a linguistic variable to enter the fuzzy-rule based system. By the aid of the experts, four level Current 841

Safety Level ranks are defined and depicted in Table 8. The fuzzy set definition for Current Safety Level (CSL) is depicted in Figure 5. In the illustrative example above, the total point for the safety measures for accidents by falling object, 5.65, has two memberships. Using Zadeh s notation, the corresponding membership function for the point of 5.65 is given as: (5.65) = 0.35/inadequate + 0.65/ average. These linguistic variables for the point of 5.65 will be used for the fuzzyrule based system. Here, it is important to note that, it cannot be said a concise membership for the point of 5.65, the point is at the transition area and has not a fully membership for any set. On the other hand, the type of the construction work, the severity of an accident should be known to reach the hazard level for accident by falling object and for other types of accidents to derive the total hazard level for the inspected construction site. 1.2 1 Poor Inadequate Average Adequate 0.8 0.6 0.4 0.2 0 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 Figure 5. Fuzzy Current Safety Level Set Definition Table 7. Finding t he quantitative data for Current Safety Level 5. Accident by falling object wi 0.19 0.06 Safety conditions to be satisfied Level of safety conditions for each particular case Relative Point Do all employers wear protective helmets (hard hats) at all times where there is a possible danger of head injury from electrical shock and burns? 1926.100 1 2 3 4 5 6 7 8 9 10 1.13 To what extent are materials which are stored in tiers either stacked, racked, blocked, interlocked, or otherwise properly secured to prevent sliding, falling, or collapse? 1926.250(a)(1) 1 2 3 4 5 6 7 8 9 10 0.41 842

0.23 0.20 0.06 0.16 0.10 To what extent is the storage of materials properly done? Are materials stored more than six feet from any hoistway or inside floor opening and more than ten fee t from any exterior walls that do not extend above the top of the stored materials? 1926.250(b)(1) 1 2 3 4 5 6 7 8 9 10 1.16 To what extent are accessible areas within the swing radius of the rotating superstructure of the crane properly barricaded? 1926.550(a)(9) 1 2 3 4 5 6 7 8 9 10 1.57 Where conveyors pass over areas or aisles, to what extent have proper guards been provided to protect employees from falling materials? 1926.555(a)(5) 1 2 3 4 5 6 7 8 9 10 0.53 To what extent are all floor openings not used as material drops equipped with a properly secured cover that will support any load which may be imposed? 1926.850(i) 1 2 3 4 5 6 7 8 9 10 0.32 To what extent is any area where material is dropped outside the exterior walls of the structure effectively and properly protected? 1926.852(a) 1 2 3 4 5 6 7 8 9 10 0.52 TOTAL POINT 5.65 Table 8. Current Safety Level Definition Current Safety Level (Hazard prevention and abatement measures that are taken for each type of accident) Rank CSL Definition 1,2 Poor No measures are taken and hazardous conditions exist 3,4,5 Inadequat e Hazard prevention and abatement measures are not taken adequately and accident risk remains 6,7,8 Average Some sort of safety measures exist but exact safety conditions are not satisfied 9,10 Adequate All the measures are taken and high level of safety conditions are satisfied, hazardous conditions eliminated or mitigated to a certain degree 843

7. CONSTRUCTION OF A RULE-BASED SYSTEM FOR SAFETY ANALYSIS An important contribution of fuzzy system theory is that it provides a systematic procedure for transforming a knowledge maze into a non-linear mapping. (10) A fuzzy IF-THEN rule is an IF-THEN statement in which some words are characterized by continuous membership functions. For example, the following is a fuzzy IF-THEN rule one of a rule that used in the study: IF Accident Likelihood of fall from height is very low in tunnel works AND severity of consequence is severe AND current safety level is poor THEN h azard level is slightly hazardous. The very low, severe, poor or slightly hazardous are characterised by the membership functions. A fuzzy system is constructed from a collection of fuzzy IF-THEN rules. The first step when constructing a fuzzy logic system is to obtain a collection of fuzzy IF-THEN rules from human experts or based on the domain knowledge. The next step is to combine these rules into a single system. Different fuzzy systems use different principles for the combination (12). When constructing the rule-based system, the Accident Likelihood as the first antecedent, is depends on the construction work (or jobsite) and the type of the accident. For example the likelihood of falls different for building works and demolition facilities; of course, it should be focused on falls on building sites. If the safety measures required for falls are not taken in a building site, the hazard level for that site increases, while importance of these measures have little impact on the hazard level of a dam construction or tunnel work. In practical implication of the safety measures, the fuzziness of the antecedents eliminates the need for a precise match with the inputs. All the rules that have any truth in their premises will contribute to the fuzzy hazard level expression. Each rule is fired to a degree that is a function of the degree to which antecedent matches the input. This imprecise matching leads to an interpolation basis between possible input states and serves to minimise the number of rules for describing the input-output relation. In safety assessment, risk level or hazard level expressed commonly by degree to which it belongs to such linguistic variables as high (poor safety), substantial (fair safety), possible (average safety) and low (good safety) that are referred to as risk level expressions. In this study, the aim is to find the hazard level of a construction site and five linguistic variables very safe, safe, slightly hazardous, hazardous and extremely hazardous are used to describe the performance and the risky atmosphere of the site. The output set also is defined using fuzzy Hazard Level (HL) sets as in the same ways the fuzzy inputs are defined. The output value (HL) can be obtained by calculating the center of gravity of the output membership function. For each rule, the membership function measures for the three input variables, AL, CS and CSL, are obtained first. The smaller value is selected as the measure to evaluate the matching of the rule as stated in Table 2. The resulting members hip function of fuzzy reasoning considering only one rule is the minimum of the membership function at the THEN part of the rule and the rule matching measure. The resulting membership function, (z), considering all relevant rules is achieved by obtaining the maximum value of these result membership functions 844

for these rules. The value of the output variable, z*, is the center of gravity of the output membership function (z), calculated by the equation given in Table 2. 1.2 Ex. Hazardous Hazardous S. Hazardous 1.0 Safe Very Safe 0.8 0.6 0.4 0.2 0.0 0 1 2 3 4 5 6 7 8 9 10 Figure 6. Fuzzy Hazard Level Set Definition 8. AN ILLUSTRATIVE EXAMPLE To explain comprehensively, the example below will be helpful. By the aid of this example, it can be understood that how the system works. An inspected building site and the current safety level for each type of the accidents observed and calculated using the relative weights of the safety measures required. If we proceed according to cause of accidents what we have are given below. The membership values for accident likelihood and consequent severity derived from Table 1 (fatal injuries taken into account) and Table 5 respectively. Here to illustrate the method, only accidents by falling objects are presented for an example. 9.8 percent of the fatal injuries on building sites caused by contacted with electricity and the accident likelihood is reasonably low and average with a membership degree of 0.04 and 0.96 respectively. On the other hand, the consequent severity of accidents by falling object is given in Table 5 as = 0.1/severe + 0.9/moderate. The level of the safety measures is evaluated as 5.65 using the relative weights during the on site inspection. This value corresponds the linguistic variables such that = 0.35/inadequate + 0.65/ average. For this building site, the rule base that are used to assess the hazard level for these kind of accidents given below. Rule 67: If the al is r.low and cs is severe and csl is inadequate than hl is hazardous Rule 68: If the al is r.low and cs is severe and csl is average than hl is s.hazardous Rule 62: If the al is r.low and cs is moderate and csl is inadequate than hl is hazardous Rule 63: If the al is r.low and cs is moderate and csl is average than hl is s.hazardous Rule 92: If the al is average and cs is severe and csl is inadequate than hl is extremely hazardous Rule 93: If the al is average and cs is severe and csl is average than hl is hazardous Rule 87: If the al is average and cs is moderate and csl is inadequate than hl is extremely hazardous Rule 88: If the al is average and cs is moderate and csl is average than hl is hazardous 845

Table 9. Rule base for the accident by falling object Rule 1st antecedents membership value 2nd antecedents membership value 3rd antecedents membership value Fuzzy AND operator (Min.) 67 0.04 0.1 0.35 0.04 Hazardous 68 0.04 0.1 0.65 0.04 S.Hazardous 62 0.04 0.9 0.35 0.04 Hazardous 63 0.04 0.9 0.65 0.04 S.Hazardous 92 0.96 0.1 0.35 0.1 E.Hazardous 93 0.96 0.1 0.65 0.1 Hazardous 87 0.96 0.9 0.35 0.35 E.Hazardous 88 0.96 0.9 0.65 0.65 Hazardous This case is different from the former ones, because there are 8 rules to be fired and they must be combined. For the consequent part, HL, for each linguistic variable the maximum value must be taken. For hazardous membership degree of 0.65, for slightly hazardous 0.04 and for extremely hazardous 0.35 should be taken. Defuzzified value z*=4 will be the centre of area of the combination of these three sets, as shown in Figure 8 graphically. This value corresponds to the linguistic variable hazardous with a membership degree of 1. By the same way for each type of accidents, the defuzzified z* values are found. These values are summarized in Table 10. The hazard level for all causes of accidents gives the ins ufficiencies of the safety management regarding the employee safety and the points out the safety measures that should be concentrated on. It should also be noted that, the results do not give precise values for all causes of accidents. For example the hazard level for the heavy equipment accidents on site is 20% safe and %80 slightly hazardous. This kind of information give us, in this case, that to an extent, safety measures are satisfactory but all the hazardous conditions are not eliminated. The conventional checklists cannot give this kind of information. In this model, the transition between two states such as safe and unsafe are fuzzy and continuous so that a site conditions can be expressed in fuzzy terms. Table 10. Defuzzified values for each cause of accident and corresponding hazard levels Cause of accident Defuzzified Corresponding Hazard Level z* value Fall from height 1.53 100% extremely hazardous Contact with electricity 3.5 100% hazardous Accident by falling object 4 100% hazardous Heavy equipment accidents 7.2 20% safe, 80% s.hazardous Traffic accident on site 4.9 10% hazardous, 90%s.hazardous Building/Structure Collapse 4.3 %30 s.hazardous, %70 hazardous Cave-ins 2.75 25% e.hazardous, %75 hazardous Fire or Explosion 8.1 100% safe Material bouncing 8.5 100% safe Other causes of accidents 5.2 100% slightly hazardous 846

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 Ex. Hazardous Hazardous =0.65 S. Hazardous =0.04 z*=4 0 1 2 3 4 5 6 7 8 9 10 Figure 8. Graphical representation of finding the output variable z* 9. CONCLUSION Classical safety checklists based on the Boolean expressions may fall short to meet the uncertainties inherent to the nature of the construction work. Especially for safety experts, linguistic expressions used rather than metrics. Fuzzy set analysis is an efficient tool to work with these kind of situations. On the other hand, the checklist proposed in this study needs to be developed, regarding safety management items those must be satisfied to establish an effective safety system in the site, such as; safety and health programs/plans, safety and health responsibilities, employee involvement, fitness for duty, emergency response plans, first-aid/medical requirements, contractor/subcontractor relationship and so on. Moreover, in this study, the financial or environmental effects of the occupational accidents are not taken into account while constructing a fuzzy rule base. The study is only focused on daily, routine safety measures, rather than the safety management principles. Similar checklists and methodology can be implement safety analysis for all kinds of safety requirement specifications. However the authors argue that, this study is a preliminary and innovative approach for safety evaluation on construction sites. 10. REFERENCES Zimmerman H.J.,1991, Fuzzy set theory and its application. Norwell, Ma: Kluwer, Ross, T.J., 1998, Fuzzy logic with engineering applications, McGraw -Hill, New York, Yen, J., and Langari R., 1999, Fuzzy Logic: Intelligence, Control and Information, Prentice Hall, Gentile M., et al, 2003, Development of an inherent safety index based on fuzzy logic. AIChE Journal Vol.49. No.4 Yen, J., and Langari R., 1999, Fuzzy Logic: Intelligence, Control and Information, Prentice Hall, Duboise, D., and Prade, H., 1998, Possibility Theory: An approach to computerized processsing of uncertainty, Kluwer Academic Pub. Schmucker, KJ., 1984, Fuzzy sets, natural language computations, and risk analysis. Rockville, MD., Computer Science Press. 847

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