Sustainable Design of Smart Health Facilities in Seismically Prone Areas

Carlo Rainieri1,a, Giovanni Fabbrocino2,b 1StreGa Lab – DiBT Dept– University of Molise, Via Duca degli Abruzzi, 86039 Termoli - Italy

2StreGa Lab – DiBT Dept– University of Molise, Via Duca degli Abruzzi, 86039 Termoli - Italy

acarlo.rainieri@unimol.it, bgiovanni.fabbrocino@unimol.it

Keywords: Health facilities, Non-structural components, Operational Limit State, SHM sustainability.

Abstract. Safety of health facilities (hospitals) is only partially related to the performance of

primary structural members. Modern seismic codes provide strict requirements to both structural

and non-structural components, since the latter are also critical to ensure that the system remains

fully operational in the case of frequent earthquakes. Thus, performance and safety checks apply

also to electro-mechanical and medical equipment, elevators, tanks, power supply systems,

distribution systems, heating, ventilation and air-conditioning systems.

In the present paper attention is focused on the analysis of the factors which make health facilities

vulnerable and on the issues related to a rational and objective assessment of performance and

health state of structural and non-structural components. This is not a trivial task, since functions

and resilience of the system as a whole depend also on the ability of inspectors and managers to

integrate theoretical evaluations with field measurements and their physical meaning. In this

context, strategies and recommendations for a sustainable implementation of Smart Health

Facilities, which fulfil AtoE characteristics (Accuracy, Budget compliance, Computational burden,

Durability, Ease of use) on a long term basis, are discussed, taking into account the specific

requirements and characteristics of the different subsystems in a hospital.

Introduction

Many hospitals worldwide are located in areas exposed to medium or high seismic hazard. They

are often built according to out-of-date codes of practice and do not fulfil the typical requirement

for strategic structures of being fully operational after earthquakes. Thus, their seismic safety is the

object of increasing attention at the National and International level [1].

Health facilities are very complex systems, performing a large number of functions: health care,

office, laboratory and warehouse. They have a primary role in the seismic emergency management

but their complexity, occupancy level and the presence of specific equipment and installations make

them very vulnerable to earthquakes. Vulnerability assessment requires consideration of structural,

non-structural and administrative aspects [1,2]. A “safe hospital” is a facility whose services remain

accessible and functioning at maximum capacity and in the same infrastructure during and

immediately after the impact of a natural hazard [1]. As a consequence, the structure has to be able

to resist the force of natural disasters and equipment and furnishing should remain undamaged, vital

connections (water, electricity, medical gases, and so on) have to be in service and the personnel

has to be able to provide medical assistance even in emergency conditions.

Existing hospitals often experience service interruption after an earthquake because of functional

breakdown. Thus, in the post earthquake phase, structural safety checks are not the only critical

aspect; hospitals have also to remain in service without interruption, so that they can ensure the

following actions in compliance with operational limit state [1]:

• Protect the life of patients, visitors and hospital staff,

• Protect the investment in equipment and furnishing,

• Protect the performance of the health facility.

Key Engineering Materials Vols. 569-570 (2013) pp 278-285Online available since 2013/Jul/31 at www.scientific.net© (2013) Trans Tech Publications, Switzerlanddoi:10.4028/www.scientific.net/KEM.569-570.278

All rights reserved. No part of contents of this paper may be reproduced or transmitted in any form or by any means without the written permission of TTP,www.ttp.net. (ID: 192.133.28.4-24/10/13,17:38:03)

Prompt fault detection of equipment and installations and near real time identification and

localization of eventual structural damage after a ground motion are, therefore, the main tasks in the

development of “smart health facilities” (SHFs). The assessment of the performance of equipment

and installation also allows for the detection of indirect losses due to the loss of functions, that are

often more relevant than those associated to structural damage. Taking into account that the damage

of critical equipment and installations, such as tanks, lifeline services and so on, might cause

downtime in health facilities, different levels of acceptable damage can be defined based on the

related consequences on the user community and the frequency of occurrence of such a damage

level, in compliance with the concept of performance-based design. Thus, a thorough assessment of

health facilities requires an integrated performance evaluation based on continuous monitoring of

structural, non-structural and operational safety.

When earthquake is the main natural hazard in the geographic area of interest, a reliable seismic

vulnerability assessment plays a primary role in the definition of the expected performance of the

structure. It is currently carried out according to various methods [3,4], which can be referred to as

qualitative or quantitative. The former are usually used to analyze large building stocks and to

prioritize interventions in hospitals while the latter are used for individual buildings requiring more

detailed assessment and analyses. Among the qualitative methods, score assignment methods and,

in particular, rapid visual screening (RVS) procedures are often adopted. However, such methods

suffer the subjectivity of the expert judgement. This drawback is overcome by the implementation

of effective monitoring strategies where relevant parameters related to the system response and

environmental factors are continuously recorded and processed in order to get relevant information

about the health status of the system both in operational conditions and in the case of extreme

events such as earthquakes. Collection of measured data and information and their automated

processing lead to the formulation of a more objective judgement about the overall health

conditions and performance of the facility, including equipment and non-structural components.

Thus, the advantage with the implementation of SHFs is in the setting of a platform able to assist

the management of the hospital in the prompt and effective maintenance of structure and equipment

under operational conditions, and in decision making and emergency management in the case of

seismic events, thus extending the lifespan of the facility. Taking into account that a high

percentage of public spending [5] is for specialized health personnel and sophisticated and costly

equipment, it is critical that hospitals continue to work even in the case of an earthquake. This goal

can be more effectively accomplished taking into account that functions and resilience of the system

as a whole depend also on the ability of inspectors and managers to integrate theoretical evaluations

with field measurements and their effective physical interpretation [6,7]. The combination of

effective monitoring strategies with control and early warning systems can further enhance the

global safety of health facilities against hazardous events [8].

The continuous monitoring of structural and non-structural components requires the definition of

a sustainable monitoring strategy. In the present paper five criteria for a sustainable implementation

of SHFs in seismically prone areas are investigated. The ultimate objective is the definition of

design recommendations able to take into account and integrate in a single platform the monitoring

requirements of the different subsystems in the hospital and the need for synthetic, intelligible

information and scenarios supporting decision making by the management.

Identification of safety issues in health facilities: the preliminary step towards SHFs

Seismic protection of health facilities can take advantage of the recent advances in civionics [9]

and in the development of smart structures and systems in order to provide information about their

health state in an automated way. The designed SHF has to be able to provide a relevant

contribution to risk reduction, supporting the definition of effective management and maintenance

strategies, which can reduce vulnerability and enhance the overall performance of the facility.

Taking into account that disaster risk is the combination of a hazard with vulnerability and that,

while hazard can be of natural origin, vulnerability is always the result of human activities

(planning, construction and development), a comprehensive assessment of the risk of a health

Key Engineering Materials Vols. 569-570 279

facility starts from the identification of the hazard in the geographic area where it is located.

However, the identification of the factors making health facilities vulnerable [2] is even more

critical, since they rule the design of the SHF. They can be summarized as follows:

• Complexity, related to the large number of functions accomplished in hospitals, ranging

from health care to office and administration, laboratory, warehouse and so on;

• High level of occupancy 24 hours a day and presence of medical equipment, potentially

dangerous gases and life support equipment requiring continuous power supply;

• High level of dependence on public services and infrastructures (power supply, water,

clinical gases, oxygen, fuel, communications), and critical supplies (medicines, splints,

bandages, and so on);

• Presence of heavy medical equipment (X-ray machines, backup generators, autoclaves and

other pieces of specialized equipment) which can be damaged as a result of intense ground

motions;

• Presence of hazardous materials, which can cause indirect losses or, at least, contamination

if they spill or leak.

Such factors lead to the distinction among structural and non-structural safety issues and issues

based on functional capacity. Structural safety involves monitoring of structural components and

materials and their response to hazards. The objective of the SHF is the assessment of the

performance of the structure under operational conditions, the identification of incipient damage

and eventual degradation phenomena, and the assessment of the impact of earthquakes or other

hazards on structural integrity and functional capacity. Vibration based techniques able to identify

and locate structural damage [10] can be profitably adopted to this aim. The failure of non-structural

elements mainly endangers people and the contents of a building. The monitoring strategy has to

focus the attention on the stability of non-structural elements (supports, anchors...) and check

whether equipment can function during and after an earthquake. In particular, the continuous

observation and analysis of the performance of critical systems reduces downtimes for checks of

equipment and networks after the impact of an earthquake, since eventual failures are automatically

detected by the SHF. This leads also to an optimization of emergency management procedures,

since economic and human resources can be entirely devoted to the maintenance of damaged

systems only. The information coming from processing of the data collected by different sensors

deployed throughout the health facility plays a fundamental role not only in the definition of

appropriate and effective maintenance actions, but also in the organization and optimization of the

response of the personnel after an earthquake. Disaster preparedness of the staff can take advantage

of the monitoring results to organize assistance in the early earthquake aftershock based on still

working equipment and networks. Thus, an effective reduction of the overall vulnerability of health

facilities requires the development of integrated monitoring and management strategies, affecting

structural, non-structural and administrative components, able to make the health facility “smart”.

Design of a smart health facility according to AtoE criteria

An effective SHF requires the installation of an appropriate number of sensors, of different types

and performance, and, above all, an efficient and fully automated data processing system. The latter

acquires sensor output, processes data and eventually provides an alarm. Thus, programmable

measurement devices for distributed data acquisition and parallel computation, and data reduction

and storage play a critical role in the implementation of SHFs, as a consequence of the fairly large

number of installed sensors. Moreover, all installations must have a minimum impact on functions

in the hospital. For its sustainable design and implementation, a SHF should have the following

characteristics: Accuracy, Budget compliance, Computational burden, Durability, Ease of use. They

have consequences both on the choice of components, technologies and procedures and on the

design of the overall architecture, as discussed next in this paper.

Accuracy. Raw data definitely provide limited information about the health of a structure.

However, the extraction of relevant information from raw measurements is possible only through a

proper choice of the sensors and the measurement chain, which must be able to resolve the response

280 Damage Assessment of Structures X

of the monitored subsystems. The different nature of structural and non-structural components

requires different strategies not only for data processing but also for data acquisition. Since there is

no sensor able to fit the needs of every application, sensor choice has to take into account the nature

and type of the monitored component and the objectives of monitoring. Sensor selection depends on

the physical quantity of interest, and this varies depending on the nature of the component and the

expected vulnerability issues. In some cases, such as for structural health assessment, sensors must

be able to properly resolve the system response both in operational conditions and in the case of an

earthquake in order to detect either degradation phenomena or seismic damage. If a global

assessment based on a number of accelerometers deployed on the structure and vibration based

damage detection algorithms can provide relevant information about the health state of the

structure, different sensors and data processing strategies are required for non-structural elements.

For instance, connections and anchorages of tanks and large medical devices (CAT scanners, X-ray

machines) can be more effectively monitored by strain gauges, settlements of distribution systems

by FBG sensors, losses in tanks and distribution systems by pressure measurements, while medical

equipment sensitive to vibrations require acceleration measurements. A summary of typical safety

issues in health facilities and of the most appropriate sensors for different monitoring objectives is

reported in Table 1.

Budget compliance. The adoption of modular and wireless architectures for data acquisition and

transmission is the key for the development of budget compliant SHFs. In fact, they allow for

consistent savings associated to a relevant reduction in the use of cables; moreover, they ensure

scalability of data acquisition systems: in fact, additional sensors and measurement nodes can be

progressively added according to budget availability and rational prioritization. This requires the

design of a versatile system, with distributed computational capabilities and a master-slave

organization of servers to take into account the specific needs of the different subsystems forming

the health facility. As the number of sensors increases, the adoption of modular architectures and of

wireless sensing units leads also to a minimization of the impact of the monitoring system on the

functions in the hospital.

Table 1. Sensor classes for different monitoring objectives and safety issues

Item Monitoring objective Sensors

Structural

safety

Overall structural performance and

health assessment

High sensitivity, seismic accelerometers

Structural detailing (connections,

joints...)

Strain gauges, displacement transducers,

fiber optic sensors

Foundations (vulnerability to floods,

differential settlement, liquefaction)

Fiber optic sensors,

Non-

structural

safety

Connections and anchorages Strain gauges, displacement transducers

Large medical devices (CAT scanners,

X-ray machines), medical equipment

sensitive to vibrations

Accelerometers, displacement transducers

Settlements of distribution systems Fiber optic sensors

Losses in tanks and distribution systems Pressure sensors

Antennas and lightning rods Anemometers, corrosion sensors,

accelerometers

HVAC, pipes, connection, valves Humidity sensors, fiber optic sensors,

temperature sensors, pressure sensors,

accelerometers

Safety

based on

functional

capacity

Fire protection systems Pressure sensors

Alarm activation/deactivation Accelerometers, displacement transducers

Elevators Seismic switches

Valve shut off Seismic switches

Hazard Seismic hazard Accelerometers, velocimeters, seismometers

Key Engineering Materials Vols. 569-570 281

Computational burden. For a near real-time response of the system, data must be collected,

stored, assessed for validity and processed within a very short time. With the rapid increase in the

number and type of installed sensors, modular and wireless architectures are definitely the most

effective. In fact, they allow the definition of clusters of sensors characterized by different data

acquisition and processing settings. The only drawback is the need for strategies ensuring

simultaneous sampling when it is critical, such as, for instance, in modal based damage detection.

Grouping the sensors deployed on different subsystems and components into clusters divide the

total computational burden among a number of distributed computational nodes. In a similar

architecture, relational databases play a critical role for data storage, data mining and data fusion. In

fact, in the presence of distributed computing nodes, the role of the centralized data server is to

aggregate, store and further process synthetic data and information in order to provide an

intelligible overview of the performance of the facility. For instance, automated modal

identification techniques [11,12] running on the local servers can perform an effective data

reduction and provide the synthetic information needed by vibration based damage identification

techniques for health assessment of structures running on the central server [10]. The collection of

synthetic data from local data processing procedures in the database allows for the subsequent

application of data mining and data fusion procedures to exploit the opportunities arising from the

combination, into the same monitoring system, of information coming from different sensors and

related to different physical variables (for instance, for removal of environmental effects [13]). The

need of combining different sensors and analysis tools for an effective assessment of health

facilities is also demonstrated by the influence of non-structural damage affecting critical equipment

and installations on the overall performance. Medical devices, tanks, adduction system, power

supply systems and backup generators, heat, ventilation and air conditioning (HVAC) systems have

a primary influence on the in-service conditions of health facilities. Even if their failure does not

usually put the stability of a building at risk, it can endanger people and contents, as a consequence

of the secondary effects (fire, explosions, leaks of chemical substances) caused by damage to non-

structural elements. They might cause interruption of services and, therefore, make a modern

hospital virtually useless. In order to protect investments in equipment and technological devices,

advanced diagnostic tools and monitoring of critical parts such as joints and connections are

fundamental. The large variety of devices and equipment requires the adoption of differentiated

monitoring strategies. For instance, if advanced systems for displacement monitoring can detect

settlements of distribution systems, monitoring of inertial or shaking effects is needed for a prompt

assessment of the functionality of mechanical equipment after an earthquake. Advanced techniques

for machine condition monitoring and fault diagnosis [14] can process the acquired data and

provide objective information about the functionality of equipment and installations, thus

supporting the definition of priorities in maintenance interventions.

Durability. Durability of a SHF is strictly related to the design and implementation of redundant

measurement chains and the adoption of a distributed sensing and computing architecture. This is

based on database working as a gatherer of data and information from the peripheral nodes to the

central server. The analysis of the characteristics of the monitored subsystem and the identification

of expected damage conditions drive the definition of sensor layout. However, the definition of

sensor layout for each monitored subsystem of the SHF should follow a redundancy criterion. In the

presence of a slightly in excess number of properly arranged sensors, eventual failures of individual

sensors do not affect the continuity of monitoring and the significance of the collected data and

information. If possible, redundant schemes should be adopted also for data transmission. Adoption

of local processing reduces the amount of data to be transmitted and this is particularly useful in the

post-earthquake phase. However, redundant vectors for data transmission make the SHF more

robust in the case of strong motions. The consequences of extreme events on data transmission

systems have to be taken into account in particular when early warning and disaster management

are primary objectives in the implementation of a SHF.

282 Damage Assessment of Structures X

Durability oriented strategies require specific efforts not only in the design of the measurement

layout but also in the overall organization of monitoring subsystems. The adopted architecture must

prevent downtime when individual components may need replacement because out of order or

obsolete. The setting of different databases for either raw data or synthetic information collection on

the servers is a possible strategy to simplify the replacement of components without changes in the

overall architecture of the monitoring system. In fact, if a component or monitoring subsystem is in

need of replacement or new subsystems are going to be added, there is no need to change the

overall architecture of the system. Exploiting the opportunities related to the use of databases as

data gatherer, only local settings are required to allow the new subsystem to interact with the local

database, and durability of the SHF is ensured with minor maintenance efforts.

Ease of use. A SHF, able to diagnose its own faults and damage, represents also a primary tool

for the reduction of the administrative and organizational vulnerability, acting on the preparedness

of personnel in the event of an earthquake and helping in the management and maintenance of

structural and non-structural elements over time. Thus, it must provide synthetic information about

the conditions of the different subsystems of the health facility and that should be intelligible to

managers who are often not experts neither technicians. As an example of the impact that

“intelligible information” provided by SHFs can have on the overall safety and management of the

facility, the availability of a scenario about the performance of structures and subsystems in a few

minutes after the earthquake can help the staff in the emergency management and in the

identification of the required interventions (for instance, replacement of components in distribution

systems) to maintain the hospital fully operational. In this framework, the combination of

monitoring plans with early warning strategies can provide additional level of seismic protections

(shut-down of critical equipment, reduction of the risk of indirect losses related to the failure of

tanks and distributions systems) at minor costs [8]. The continuous monitoring of the health state

and performance of hospitals, including equipment and installations, and the return of “intelligible

information” to the management can help in the formulation also of disaster mitigation plan and in

the prioritization of investments (both in operational conditions and after hazardous events) for

safety of people and goods. The importance rating of clinical and support services [2] can help in

the definition of priorities in the implementation of the monitoring system in the presence of budget

constraints.

Additional remarks. The scheme of a SHF fulfilling the above-discussed criteria is illustrated in

Fig. 1. It is clear how only the development of a smart system, which analyzes data related to

different subsystems (structure, equipment, installations and so on) and eventually provides

warnings in the case of damage or faults, can effectively take into account the critical nature and

interdependence of the various subsystems. The integration of different sensors and automated

analysis procedures allows for a condition-based maintenance of health facilities and an assessment

of both the short-term impact due to earthquakes and the long-term deterioration process due to

physical aging and routine operation. Anomalies can be detected by continuous processing of the

incoming data. In the case of earthquake risk analysis, continuous structural monitoring can be used

to collect a database of relevant data and information about the dynamic behaviour of the structure

over its lifespan. In the pre-seismic event phase, these data can be analyzed to evaluate the ability of

the structural subsystem to withstand seismic events on the basis of tremors, such as those due to

traffic or wind excitation, by updating the numerical model. At the same time, the calibrated model

can improve the ability of structural computations to make reliable estimations of seismic

performance, including the effect of quakes on equipment and installations according to simplified

formulation or estimated floor spectra [5]. An in-depth knowledge about the seismic characteristics

of the site (such as zone of the epicentre, seismicity, etc.) provides additional information about the

seismic input that is also relevant for reliability of analyses. In fact, a more detailed characterization

of the seismicity and the expected events on one hand, and of structural behaviour on the other

hand, allowing for more reliable predictions of the structural response, decrease the problem of a

false alarm. The continuous monitoring of the dynamic response leads also to a characterization of

the influence of environmental factors on the structural response. At the same time, the adoption of

Key Engineering Materials Vols. 569-570 283

local data processing procedures, such as the automated dynamic identification of the structure,

leads to a significant data reduction which is relevant not only for data transmission in critical

conditions but also to reduce the costs related to data storage. In fact, only some days of raw data

are stored on the local database and cyclically deleted. The estimated modal parameters, instead, are

permanently stored and sent to the central server for health assessment and visualization. This

results in significant savings in terms of storage volumes and bandwidth requirements for

communications.

The combination of monitoring plans with basic early warning and control strategies can further

enhance the overall safety of the health facility. The information coming from sensors can be used

for the implementation of control strategies able to improve the overall safety in the case of an

earthquake. This is the case, for instance, of lifts: the information coming from accelerometers

deployed on the structure and eventually from early warning systems can be used to activate

strategies for the immediate shutdown of elevators in the event of a potentially damaging

earthquake. Shutdown of critical systems and closure of valves in the case of damage to distribution

systems are other possible applications.

Fig. 1. Scheme of a SHF fulfilling the AtoE criteria

Conclusions

Safety of health facilities mainly depends on the resilience of the overall system rather than on

the performance of primary structural members. For this reason, the modern seismic codes provide

strict requirements to both structural and non-structural components to ensure that the hospital

remains fully operational in the case of frequent earthquakes. Standardized approaches, criteria and

indicators for a reliable assessment and management of existing facilities are needed. The present

study has investigated the opportunities provided by SHFs in the objective structural and non-

structural characterization of existing hospitals. Taking into account that the ability of inspectors

and managers to integrate theoretical evaluations with field measurements has an influence on the

284 Damage Assessment of Structures X

overall resilience of health facilities, systems and tools for the effective physical interpretation of

data and information coming from sensors deployed on the system play a primary role in enhancing

the seismic safety, reducing down time and optimizing interventions. Such objectives can be

achieved by the development of effective and integrated monitoring strategies for the different

components in the facility. Thus, five criteria for a sustainable design of SHFs have been proposed,

and recommendations and strategies for their proper implementation have been discussed taking

into account the specific characteristics and requirements of the different subsystems forming the

hospitals.

Acknowledgements

The present work is carried out in the framework of the research project “Dynamic Monitoring in

the management of seismic safety of health facilities” issued by the DiBT Department at University

of Molise. Collaboration with ReLUIS Consortium research groups active in the framework of Line

2.2 Special Systems of the ReLuis-DPC Executive Project 2010-2013 “RELUIS II”, rep. 823 is also

gratefully acknowledged.

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