Category Advances in. Engineering Structures,. Mechanics &. Construction

Environmental Surfaces

The realization that environmental surfaces can play a major role in the spread of resistive strains, has led to studies that find inadequacies in current methods of disinfection (Pentella et al. 2000). Many comparative studies have been reported. These studies used standard chemicals as well as new antimicrobial products for surface treatment (Lisay et al. 2000; Tessarin et al. 2000; Rutula et al. 2000). In addition to hospitals, increasing evidence of VRE is being found in long term care facilities (Lai et al. 2000) and in home care settings (Manangan et al. 2000; Greene et al. 2000). Evidence of airborne organisms, inadequate air quality, inadequate hand washing and environmental contamination in Neonatal Intensive Care units (Kassis et al. 2000; Burke et al. 2000) and pediatric care facilities (Goldman et al. 2000) have each been reported.

Evidence of Supplementary Protection

With the current industry increase in cost-cutting measures and staffing constraints, some standards are not being practiced as fully as one would hope (Harris et al. 2000). In an attempt to combat this, various approaches have been taken to provide supporting measures to assist the healthcare worker and to provide cleaner surroundings. Indeed, new hand care preparations (Gould 2000) are being developed to assist the healthcare worker. Some of these contain the well-known antimicrobial agent, triclosan (Hoffmann et al. 2000) that has been used in hand soaps for many years. In an attempt to provide continuous antimicrobial action on surfaces, a novel application incorporating triclosan into the surface material used on a hospital over-bed table was reported by Van Enk and Lam (2000). This study showed that such surface treatment was capable of reducing many species of viable or living organisms on the surface within 60 minutes, as compared to more than four hours for the untreated control surface. This was an interesting application of an antimicrobial agent that was integrated into the construction material.

Additional Factors

In a survey article by Weinstein (1998), nosocomial infections in the US were estimated to have cost $4.5 billion in the year 1995. This article indicates a growing concern based on the increase of antibiotic-resistant microorganisms, such as (MRSA) and (VRE). The shift from inpatient to outpatient care leaves the more vulnerable patients within the hospitals, especially the immunocompromised patients. This increases the opportunities and likelihood of nosocomial infections. These factors, when combined with the renovation of aging hospitals, which increases the risk of airborne fungal and other infections (PHTS 2000), suggest that a better approach is needed to cleaning and maintaining healthcare facilities in the future.

Resistive Strain Organisms

There is an increasing awareness by infection control personnel that touch surfaces can play a major role in the spread of microorganisms. Of particular concern is the potential spread of antibiotic – resistant organisms such as vancomycin-resistant enterococci (VRE), which is known to be transmitted from surfaces (Temple et al. 2000). This resistant organism is on the increase in hospitals throughout the United States. A study of upholstered chairs found that VRE are capable of prolonged survival on fabric seat cushions. In many hospitals, cloth-styled covering is preferred by management to increase patient comfort while in the hospital or long term care facility. However, these create potential reservoirs for the nosocomial transmission of VRE (Noskin et al. 2000). In another study, Mermel et al. (2000) found that at a number of cultured sites within patients’ rooms, the highest rates of VRE were found on patient telephone handsets. Additional VRE organisms were found on bed rails, over-

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bed tables and sink faucets. This was further supported by studies conducted by Rutala et al. (2000) and by Diekema et al. (2004). Another resistive strain namely, methicillin-resistant staphylococcus aureus (MRS A), was found on computer keyboards and computer mice used in the medicine wards and other acute care areas (Valena et al. 2000). MRSA was also found on hospital bed handsets (Young, J. M. et al. 2005) including call buttons and TV remote control devices. A further study of hospitals in Pennsylvania (Volavka, M. 2005) found that more than 11,600 patients got infections while in hospitals over a one year period. These infections led to 1500 (13%) deaths and over $2 billion in hospital charges.

Nosocomial Infections

In the twenty-year period between 1975 and 1995, nosocomial or hospital acquired infections increased by 36%. A review of studies reported in two specific conferences on the topic, revealed the magnitude of the problem (APIC 2000; CDC 2000). The papers presented at these conferences provide evidence of increasing infection rates, a shift in infection type and additional locations where these infections are being spread. In addition to being found in most hospitals, nosocomial infections are common among patients admitted to acute rehabilitation units. Rates have been reported as high as 17% and were found to significantly contribute to patient morbidity (Mylotte 2000). Similar concern was voiced in a study involving a VA psychiatric facility (Risa et al. 2000). Nosocomial invasive aspergillosis is associated with high morbidity and mortality in patients with blood related illnesses (Raad et al. 2000). Various fungal infections found in immunocompromised patients were traced to problems during construction renovations of healthcare facilities (Drusin et al. 2000; Donelan et al. 2000; Rebmann et al. 2000). In coastal regions, that are prone to hurricanes, high humidity and moisture levels, patients are subjected to increased risk of nosocomial infections (Ober et al. 2000). Zhan et al. (2003) surveyed 20% of US hospitals and found that hospital acquired infections increase the average patient’s stay by almost 10 days, increases the cost per patient by more than $38,000 US and increase their risk of death by more than 4%. This study resulted in the Joint Commission on Accreditation of Hospital Organizations (JCAHO 2004) to revised the standards for 2004. Zoutman et al. (2003) reported a similar study of Canadian hospitals. In 2003 the Province of Ontario was suddenly exposed to a severe epidemic of SARS. Over the six months from the infection’s arrival until the last patient was discharged from hospital, 375 cases were recorded (OMH. 2004). At the height of the outbreak thousands of people, including health care workers, mostly in the Toronto area were quarantined for 10-day periods at home and given specific advice on preventing family members from infection. This was one of the extreme examples of a hospital acquired infection.

ANTIMICROBIAL TREATED CONSTRUCTION MATERIALS AND AIR FILTERS. REDUCE FACILITY BIOBURDEN AND IMPROVE AIR QUALITY IN. A HEALTHCARE ENVIRONMENT

G. M. McNeice1, O. Z. Tyler2 and D. W. Blackhurst3

1Clemson University, 2OZ Enterprises, 3Greenville Hospital System3

Background

The continued evidence of the presence and spread of pathogenic organisms on environmental surfaces in acute, ambulatory, short-term and long-term healthcare facilities and in the home, suggests that additional cleaning and maintenance protocols are needed. The gold standards of “good hand washing” and daily disinfecting of critical surfaces do not appear to be able to combat the increasing rate of hospital-acquired infections.

Optimization for Bridge Network Maintenance Planning

Bridge network maintenance planning has to deal with multiple bridges in a highway network under limited annual maintenance budgets. Thus, MCDM approaches can be used to help bridge owners, authorizers and/or maintenance managers to make rational decisions on maintenance actions applied to each of individual bridges in the highway network. In this study, the multiple attribute utility theory (MAUT) is adopted. As a matter of fact, MAUT focuses on the development of the multiple attribute utility functions to model and represent the decision maker’s preferential structures. The multiple attribute utility functions combine all of the marginal utility functions associated with individual attribute of each alternative. The marginal utility functions for each attribute can be built up by either direct interrogation with decision makers or by indirect methods, as well as by using the analytic
hierarchy process (Saaty, 1980) that has been mainly used in USA. The decomposition forms of the multiple attribute utility functions may be (1) additive, (2) multiplicative and (3) multi-linear forms (Keeney and Raiffa, 1993). The additive form requires mutual preferential independence, that is, every subset of criteria is preferentially independent from the remaining criteria. A subset of criteria is considered to be preferentially independent form the remaining criteria if and only if the decision maker’s preferences on the alternatives differ only with respect to the criteria, and are independent on the remaining other criteria. It must be noted that very complex decomposition forms are of not interest from a practical point of view (Vincke, 1992). MAUT also employs an interactive and iterative procedure involving policy analyst and decision makers to specify the weight and marginal utility function corresponding to each criterion. Finally, the total utility of each alternative can be used as an objective function in traditional mathematical programming in order to make final decisions (Doumpos and Zopounidis, 2002).

In this study, each of individual bridges in a highway network may be treated as a subset of criteria with a marginal utility function associated with the probabilities that each of the above five maintenance alternatives (including “do nothing”) may be conducted at certain time (year). Since the mutual preferential independence requirement can be easily satisfied in this case, the additive form of the multiple attribute utility function is used to form a single-objective function for optimization. The objective function of multiple attribute utility may be also weighted by using RIF of individual bridges in the network, where RIF is defined as the sensitivity of the bridge network reliability to the change in the individual bridge system reliability (Liu and Frangopol, 2005). RIF in this paper reflects the sensitivity of the bridge network reliability in terms of the network connectivity to the change in the individual bridge system reliability due to maintenance actions, and must be developed as a function of the bridge system reliability profiles, network reliability, and network topology. RIF may also be expanded to include traffic capacity and impacts of bridge maintenance activities on economy, environment and society, when considering user’s satisfaction and critical bridge performance of a bridge network (Liu and Frangopol, 2005). Consequently, the optimization problem in bridge network maintenance planning can be formulated as follows:

Maximize ^ Dij x RIFt x Pij (1)

І

Subject to: £C. < Cbudget (2)

is a binary design variable, i. e. the value of Dj can be either 0 or 1; is the reliability importance factor for bridge i;

is the probability of the maintenance alternative j applied to bridge ; is the cost associated with the selected maintenance alternative for bridge i; is the annual maintenance budget at certain year;

The binary design variable, Dj represents the decision on selecting the maintenance alternative j applied to bridge i, that is, Dj = 0 means the maintenance alternative j will not be applied to bridge i, and Dj = 1 means the maintenance alternative j is selected to be applied to bridge i. In addition, it should be noted that the values of RIFi and Pj usually vary during the entire service lifetime of bridge i. This is because RIFi is normalized by considering all bridges in a highway network that experience the aging and deterioration with time (Liu and Frangopol, 2005). On the other hand, Pj is normalized by considering all of the five maintenance alternatives applied to bridge i in a certain year, and is dependent on the results from the DP procedure that is combined with Monte Carlo simulations (Liu and Frangopol, 2006). Moreover, Ci is related to the cost in Table 3, but the actual values of Ci should
be assigned in order to obtain an optimal bridge network maintenance planning. Finally, this combinatorial optimization problem can be easily solved by either traditional mathematical programming or the advanced heuristic search methods such as Genetic Algorithms (GAs).

Case Study

A numerical example involving five highway bridges is provided to demonstrate the application of the proposed DSS in bridge network maintenance planning. Table 4 presents the values of RIFj, Py and Ci for combinatorial optimization that is subject to a budget constraint of C budget = 10,000 The optimization results from a traditional mathematical programming are also summarized in Table 4.

Table 4: Example Values of RPFi, Py and Ci

Maintenance

Bridge

Bridge

Bridge

Bridge

Bridge

Cost

Alternatives

E-17-HE

E-17-HR

E-17-LE

E-16-MU

E-16-NM

(Py)

P)

(Py)

P)

(Py)

(C)

Minor Concrete Repair

0.40

0.30

0.15

0.20

0.15

4,500

Silane Treatment

0.20

0.20

0.40

0.10

0.35

48

Cathodic Protection

0.25

0.30

0.20

0.30

0.20

2,600

Rebuild

0.10

0.15

0.05

0.30

0.05

12,000

Do Nothing

0.05

0.05

0.20

0.10

0.25

0

Sum of Py

1.00

1.00

1.00

1.00

1.00

Reliability Importance

Sum of RIFj

Factor (RIF,)

0.38

0.27

0.19

0.11

0.05

1.0

Selected

minor

cathodic

silane

cathodic

silane

C budget

Maintenance

concrete

protection

treatment

protection

treatment

Alternative

repair

10,000

Sum of Cj

Cost (C)

4,500

2,600

48

2,600

48

9,796

Conclusions

This paper presented a decision support system (DSS) for bridge network maintenance planning using the multiple attribute utility theory (MAUT). The combinatorial optimization problem was developed with a single-objective function of the probabilities that the maintenance alternatives may be applied to each of the individual bridges in a highway network. The probabilities in the single-objective function had to be obtained from a Dynamic Programming (DP) procedure, considering individual bridge condition index, safety index and life-cycle maintenance cost. The single-objective function was also weighted by the Reliability Importance Factors (RIF), which had to be the functions of individual bridge system reliability profiles, bridge network reliability, and network topology. The constraint of the optimization problem was the limited annual maintenance budget. A numerical example was provided to demonstrate the application of the proposed DSS in bridge network maintenance planning.

Multiple Criteria Decision-Making Process

Since almost all real-world decision problems must be addressed on the basis of multi-dimensional approaches, the Multiple Criteria Decision-Making (MCDM) process has been developed long time ago. Pareto first introduced the efficiency concept in 1896 (Doumpos and Zopounidis, 2002). A feasible solution is efficient if and only if there is no other feasible solution which dominates it (Ringuest, 1992). Von Neumann and Morgenstern (1944) developed the utility theory as one of the major methodologies in modern MCDM. The preference structures of a decision maker are represented by multiple attribute utility functions. Charnes and Cooper (1961) extended the traditional mathematical programming theory to the goal programming. In recent decades, more and more user – friendly software has been developed based on advances in information technology and computer science.

Basically, MCDM provides a set of criteria aggregation methodologies that focus on decision maker’s preference structures, system values and judgment policy. Since the “optimal” solutions in the traditional mathematical programming usually do not exist in MCDM due to the potential conflicting nature of the multiple objects, MCDM could find an appropriate “compromise” solution that satisfies all of the decision maker’s policy. MCDM general procedure consists of (1) identifying decision objectives, all feasible alternatives and participants; (2) developing evaluation criteria that measure the performance of each alternative on decision objectives; (3) modeling criteria aggregation; and (4) providing meaningful recommendations. MCDM approaches include multi-objective mathematical programming (MMP), multiple attribute utility theory (MAUT), outranking relation theory (ORT), interactive methods and preference disaggregation analysis (PDA) (Vincke, 1992; Pardalos et al., 1995).

Dynamic Programming for Individual Bridge Maintenance Planning

Bridges without maintenance may not reach a targeted service lifetime due to the aging and deterioration. Therefore, bridge maintenance actions must be applied to extend the remaining service lifetime of individual bridges. Individual bridge maintenance planning needs to answer to questions such as what sequence of maintenance actions and when these maintenance actions should take place in order to minimize the life-cycle maintenance cost throughout the entire targeted lifetime period. The life-cycle maintenance cost can be either construction cost that bridge owners have to pay for or user’s cost that includes the time delays and fuel consumption due to detour and/or congestion caused

Figure 1: Effects of “Minor Concrete Repair” on Mean Condition and Safety Indices over Time

Figure 2: Effects of “Silane Treatment” on Mean Condition and Safety Indices over Time

Figure 3: Effects of “Cathodic Protection” on Mean Condition and Safety Indices over Time

(b) Effect on Safety Index

Figure 4: Effects of “Rebuild” on Mean Condition and Safety Indices over Time

by the maintenance actions or combination of both construction and user’s cost. In reality, bridge maintenance planning has to consider the maintenance funding limitation as well.

In this study, the prediction of the remaining service lifetime of individual bridges is based on both bridge condition and safety indices. The bridge condition index increases as the bridge deteriorates with time, while the bridge safety index decreases with time. The maximum condition index is set to be 3.0, and the minimum safety index is assigned to be 0.91 (Denton, 2002). In other words, an individual bridge should always have a condition index less than 3.0 and a safety index greater than 0.91 during the entire service lifetime period. The difference between the predicted remaining service lifetime and the targeted service lifetime of a bridge must be covered by applying maintenance alternatives. Any combination of the above five bridge maintenance alternatives that can extend the
bridge service lifetime to the targeted level may be regarded as a feasible maintenance plan. These feasible maintenance plans may require performing different combinations of the above five maintenance actions at different application times, resulting in different life-cycle maintenance cost. The life-cycle maintenance cost for each feasible maintenance plan are converted to the net present values (NPV), using the discount rates ranging from 2% to 8%. Thus, an optimal bridge maintenance plan is the feasible plan that has a minimum life-cycle maintenance cost in terms of NPV. A dynamic programming (DP) procedure has been developed to identify the optimal bridge maintenance plans for individual bridges (Liu and Frangopol, 2006). Monte Carlo simulations are integrated within the DP procedure for sensitivity studies, considering the probability distributions of all random variables and parameters. As a result, the probabilities that each of the above five maintenance alternatives (including “Do Nothing”) may be conducted at certain time (year) can be obtained for individual bridges. The details of the DP procedure combined with Monte Carlo simulations are presented in Liu and Frangopol (2006).

Bridge Maintenance Alternatives and Associated Cost

The bridge maintenance alternatives presented in this paper include both preventive and essential maintenances with actual cost data, as well as “do nothing”. The effects of the five different maintenance alternatives, namely, “minor concrete repair”, “silane treatment”, “cathodic protection”, “rebuild” and “do nothing” on individual bridge condition and safety indices over time have been studied as shown in Tables 1 and 2 (Denton, 2002). For example, the “minor concrete repair” results in a decrease of bridge condition index (CI) between 2 and 3 with a triangular probability distribution. The mode of the triangular probability distribution is 2.5, indicating that the most likely decrease of the bridge condition index is 2.5 (see Table 1). Meanwhile, the “minor concrete repair” causes a delay in deterioration of bridge safety index (SI) when the bridge condition index is less than 1.0, in other words, there is no deterioration of the bridge safety index after the “minor concrete repair” maintenance action is applied, and the deterioration of the bridge safety index resumes after the bridge

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© 2006 Springer. Printed in the Netherlands.

condition index reaches 1.0. The “silane treatment” affects only the deterioration rates during the maintenance effective duration that has a triangular probability distribution between 7.5 and 12.5 years. The bridge condition and safety indices will not change in the first 12.5 years after the “cathodic protection” maintenance action is applied. The “rebuild” is the only essential maintenance actions in this study. If this action is applied, the bridge condition index will be set to zero and the bridge safety index will be assigned to the safety index of the rebuilt bridge. Meanwhile, the deterioration of the bridge condition index will start between 10 and 30 years after “rebuild” with a triangular probability distribution mode of 15 years. The deterioration of the bridge safety index will begin when the bridge condition index reaches 1.0. Table 3 presents the associated cost of the five alternatives considered. Figures 1 to 4 shows the effects of the preventive (“minor concrete repair”, “silane treatment” and “cathodic protection”) and essential (“rebuild”) maintenance alternatives on the mean values of bridge condition and safety indices over time.

Table 1: Effect of Bridge Maintenance Alternatives on Mean Condition Index (after Denton, 2002)

Bridge

Maintenance

Alternatives

Decrease in Condition Index

Delay in Deterioration (years)

Reduced

Deterioration

Rate (year-1)

Maintenance Effective Duration (years)

Minor Concrete Repair

T(2.0, 2.5, 3.0)

Silane Treatment

T(0.00, 0.01,0.03)

T(7.5, 10.0, 12.5)

Cathodic Protection

12.5

Rebuild

set to zero

T(10, 15, 30)

Do Nothing

Note: T (minimum value, mode, maximum value) represents the triangular probability distribution.

Table 2: Effect of Bridge Maintenance Alternatives on Mean Safety Index (after Denton, 2002)

Bridge

Maintenance

Alternatives

Increase in Safety Index

Delay in Deterioration (years)

Reduced Deterioration Rate (year-1)

Maintenance Effective Duration (years)

Minor Concrete Repair

CI<1.0

Silane Treatment

T(0, 0.007, 0.018)

T(7.5, 10.0, 12.5)

Cathodic Protection

12.5

Rebuild

set to SI

CI<1.0

Do Nothing

Note: T (minimum value, mode, maximum value) represents the triangular probability distribution.

Table 3: Cost for Bridge Maintenance Alternatives (after Denton, 2002)

Bridge Maintenance Alternatives

Cost

Minor Concrete Repair Silane Treatment Cathodic Protection Rebuild Do Nothing

T(16, 3605, 14437) T(0.3, 39,77)

T(19, 2604, 5189) T(247, 7410, 28898) 0.0

Note: T (minimum value, mode, maximum value) represents the triangular probability distribution.