Earthquake Simulation on Urban Areas: Improving Contingency Plans by Damage Assessment

Abstract

Crisis produced by earthquake events are often dramatic for their severity and their impact on population. Damages may extend from buildings to Critical Infrastructures. Predicting the functionality of the latter after an event is relevant for the design of contingency plans, as availability of primary services empowers the action of first responders in the aftermath management. This work deploys a complex earthquake simulator (CIPCast-ES) which allows to explore a realistic earthquake event occurring in the city of Florence (Italy) by predicting disruptions on buildings and Critical Infrastructure and by designing a reliable scenario, accounting for roads obstruction due to building collapse, to be used to design an efficient contingency plan.

1 Introduction

Earthquakes are relevant, endemic phenomena affecting a large portion of the globe. Italy is among the most seismic areas in the world, as being at the border between the African and the Indo-European tectonic plates [1]. This geological situation produced, in the course of the centuries, a large number of earthquakes (it has been estimated that over 15.000 earthquakes with moment magnitude $$M_w > 3$$ occurred in Italy in the last 1000 years [2]) which caused several victims, also due to the extreme vulnerability of the territory and of the urban settlements. The study described in the present paper was carried out to provide an estimate of the impact induced by the earthquakes on urban areas particularly with respect to buildings and road conditions after the event. Road obstruction, electricity outages and other primary resources could constitute severe limitations in the emergency phase and should be known in order to design reliable contingency plans. Our work attempts to provide a mean to support an informed planning for post-earthquake emergency management and improve urban development and transition toward “smartness” and security. The city of Florence was elected as a test-case due to the availability of extremely rich land data and the historical reports of several medium-intensity earthquakes occurred in the city area during the last century. The plan of the work is as follows: in the next section the model is shown with the input data used to perform seismic simulation. In the last section results are presented (for several quakes of different magnitudes occurring in the Florence city area) together with the illustration of the type of results that the CIPCast-ES system is able to produce, together with their use in the design of emergency plans.

2 Simulation Model

The earthquake simulation model referred to as CIPCast-ES, Critical Infrastructure Protection - Earthquake Simulator, described elsewhere [6–8], is a relevant component of the Decision Support System (DSS) CIPCast designed and realized within EU-FP7 Project CIPRNet and the Italian Project RoMA (funded by Italian Ministry of Research MIUR). The latest implementation of the CIPCast-ES is also related to the Italian National Program “Ricerca di Sistema Elettrico”? (“Research on Electric System”), carried-out in the framework of an agreement between the Italian Ministry of Economic Development and ENEA.

CIPCast-ES was developed to assess the earthquake-induced damage at single building level, and the relative expected consequences on the residents in term of casualties and population to be evacuated in the aftermath. CIPCast-ES works as a Decision Support System (DSS) on a deterministic base, simulating damage and impact scenarios for earthquake preliminary defined by the end-user in terms of location of the epicentre, magnitude, hypocentral depth. In this regard, CIPCast-ES can support preparedness planning and emergency management, allowing for testing alternative strategies and resource allocations. Furthermore, its results can be presented on a WebGIS interface, which was purposely developed to provide a geographical interface for complex results visualization. Basic information, maps and scenarios can be visualized and queried via web, by means of standard Internet browsers and, consequently, the main results can be easily accessible to the users and exploitable for further analyses.

The model is fed by the main earthquake data input, which can be either retrieved from the site of the National Earthquake Observatory [5] or defined by the user. The earthquake propagation model enables to simulate the dynamics of the shake waves along the terrain, which is represented in terms of a homogeneous bedrock, unless otherwise specified (in terms of amplification effects) upon seismic microzoning [13].

For this study, the CIPCast-ES deterministic approach was used to estimate, for the area of interest, the expected ground motion and related consequences, for a selected seismic event (e.g., the maximum historical event from a pertinent seismogenetic source, or the maximum earthquake compatible with the known tectonic framework). CIPCast-ES allows to evaluate the deterministic hazard in terms of macroseismic intensity I providing a qualitative description of the seismicity in relation to the damage observed on the built environment [13]. In this respect it can be easily communicated and understood by the end-user and easily handled for managing post-disaster emergencies [14]. The model allows the use of different Ground Motion Prediction Equations (GMPE) to describe the damping of the ground motion as a function of the distance from the fault rupture [13]. CIPCast-ES currently implements three GMPE, selected from a review available for the Italian territory [19, 20]. For the sake of this study, the GMPE defined in [3], i.e. Eq. 1, was selected and used for the simulation for the reasons described in [13]:

$\begin{aligned} I = 2.085 + 1.428\cdot M_w - 1.042 \ln \sqrt{ R^2 + h^2 + (2.042 e^{(M_w-5)} -0.209)^2} + S \end{aligned}$

(1)

where

is the moment magnitude of the earthquake, R (km) is the epicentral distance, h (km) is the hypocentral depth, S is a site topographic factor (not considered here). Equation 1 is validated for hypocentral distance less or equal to 50 km [3]. Macroseimic intensity I was given referring to Modified Mercalli scale [15]. The possibility to account for the seismic microzoning, included within CIPCast-ES, was used for the case study of Florence Municipality.

The damage that might affect a building stock, when subjected to a seismic event, can be predicted via different approaches: CIPCast-ES identifies the vulnerability functions (i.e., a function connecting the expected level of damage, expressed in some standard scale, with respect to the parameter measuring the local seismic intensity) for the structural elements [17].

For the sake of this study three detailed data-bases in GIS format were exploited, namely: Registry of Buildings (RB); Map of the Amplification Factor of the seismic wave (MAF), Road Network Map (RNM). In particular, the RB database provided information about building material, state of preservation, height, age of construction. CIPCast-ES elaborates a “Damage Scenario”?, correlating the intensity of the event with the vulnerability of the different elements in the affected area. Thus, the model allocates the level of damage to each building, according to the European Macroseismic Scale EMS-98 [9].

2.1 Road Vulnerability Assessment Due to Ground Failure

The CIPCast-ES methodology used to assess the road vulnerability to ground failure is based on the model of reference [4]. According to that work, a functionality level of a road after an earthquake can be estimated by evaluating the possible invasion of the debris of collapsed buildings on the road itself with the subsequent reduction of its available width. The method correlates the building geometry and shape with the resulting debris volume and shape.

To apply such a methodology, CIPCast-ES makes use of the following data layers provided by the Florence municipality, by the Italian National Institute of Statistics(ISTAT) and by the Italian National Corp of Fire Fighters (CNVVF):

$\mathbf {T_i}$ : building material (i.e., reinforced concrete, masonry);
$\mathbf {H_i}$ : average height of the building;
$\mathbf {W_i}$ : width of the building;
$\mathbf {W_r}$ : width of the nearby road pavement;
$\mathbf {W_{br}}$ : distance between the building facade and the nearby road;
$\mathbf {k_v}$ : average building volume reduction after collapse;

A Gaussian distribution is used to estimate the variation of the debris width $$W_d$$

(Fig. 1) based on two parameters: the mean value $$E [W_d]$$

and the standard deviation $\sigma _{W_d}$ , which can be both calculated given the angle of collapse $\phi$ and the building volume reduction $$k_v$$

according to [12]:

$\begin{aligned} W_d = \sqrt{ W^2 + {2 \cdot k_v \cdot W \cdot H \over \tan \phi }} - W \end{aligned}$

(2)

../images/477940_1_En_6_Chapter/477940_1_En_6_Fig1_HTML.png — Fig. 1.
Estimation of debris width and road closure [4].

Based on the earthquake simulation described in [7], the CIPCast-ES platform produces a physical damage assessment for the buildings that is characterized by the following data:

damage level: for each building, a damage level according to the European Macroseismic Scale EMS-98 [9] (ranging from D1 to D5, plus the absence of damage D0);
$\mathbf {W_d}$ : the width of the debris heap resulting from the collapse of the building (with D5 damage level);
$\mathbf {W_{fr}}$ : the width of the road that remains clear after the debris fall.

In order to evaluate the road blockage due to collapsed buildings (Fig. 1), a functionality level FL, based on three thresholds $$FL_0$$

and

, was defined for each building i, assuming a necessary minimum width of 3.5 m for (ordinary, not tracked) emergency vehicles to go through:

, when $W_{d,i} \le W_{br}$ : the road is open;
, when $W_{br} \le W_{d,i} \le W_{br} + W_{r} - 3.5$ : the road is only open for emergency;
, when $W_{d,i} \ge W_{br} + W_r - 3.5$ : the road is closed.

It should be noted that the simulations carried out by this approach were performed under the assumption of a worst case scenario, i.e. when a generic building collapses, it spreads its debris ONLY in the direction of the road (corresponding to the facade overlooking the road itself).

The information on the usability state of buildings is extremely relevant on its own: one may estimate the number of people injured or trapped in the rubbles and the number of people needing to be displaced and to whom a shelter should be provided. This helps the emergency and the rescue team to plan their intervention and immediate post-event strategies. In other words the knowledge of the state of all buildings allows to infer the damages on the population and the possible losses in the Emergency Preparedness Resources (EPR). Providing the communications are granted during the emergency, the decision maker in charge to coordinate the operations (i.e. the Mayor or the Prefect depending on the size of the event) will be provided with a list of needs and available means to deal with.

Dealing with the emergency requires moving people from inoperable houses to the gathering points and first aid centers or hospitals, goods (water, food, drugs, dresses etc.) from their storage sites to the centers of first lodging, the rescue teams from their houses to the operational and directional centers while possibly receiving further help from the external areas not affected by the earthquake. To these purposes, the road viability plays a central role. A relevant information thus consists in assessing which areas in the city remain connected, which parts are still reachable from outside and which resources are directly available in the connected areas. From the direct roads disruption and from the identification of their blockage by building disruptions, we can infer the viability of each road. In order to assess the reachability of the different areas, some further analysis is required (see [10] for the definition of the parameters related to the nodes reachability in a network).

In the present work, road networks are modeled in a primal representation [11] i.e. the nodes are the road junctions and the links are the roads themselves. A node can thus be defined “reachable” from an other when there is set of “contiguous” roads that connect them (i.e., when a car or an ordinary vehicle can move from one point to an other). In this calculation, far from crises or contingencies, one-ways were appropriately considered; however, in emergency situations, roads directionality could be reasonably omitted as, under those situations, authorities will usually remove circulation constraints to improve reachability of all city’s areas.

When a group of junctions and roads is mutually reachable, but not reachable from the others, we will say that the former group represents an “island” and we will name “islanding” the spontaneous formation of such regions upon roads unavailability produced by the event.

Based on the operability of each single road, which in turn results from the stability model of buildings, an appropriate CIPCast-ES module helps identifying the resulting islands (if any) after the event and analyzing their extent and their internal situation (in term of casualties, available resources etc.). A special attention is devoted to the identification of directional centers (emergency, shelter etc.) contained in the islands, as they would be unavailable for the global emergency while remaining available to support local emergency activities.

The analysis of the islands allows also to predict which part of the city may require the support of excavators or other vehicles to remove debris and restore reachability. The percentage of inoperable buildings represents a useful indicator of damage (loss of lodging), whereas the percentage of houses in the connected component may provide a complementary index to evaluate the extension of first aid actions.

2.2 Input Data

Several simulated earthquakes were designed for the urban area of the city of Florence (Italy). This area was chosen for different reasons:

the city of Florence is surrounded by territories (in the north-east and north-west parts of Tuscany) which are seismically active which, up to the last century, generated several earthquakes of sizeable magnitudes. This study analyses the damage scenarios generated by two historical events with epicentre location one in the Florentine area (Impruneta) and the other in the Mugello area, respectively: (i) 18 May 1895, estimated magnitude (epicentre located only few km outside the municipal boundaries: 43.7N - 11.267E); 29 June 1919, estimated magnitude (epicentre: 43.95N - 11.483E).
the city of Florence has an accurate and updated (2015) building database (RB), allowing an appropriate estimate of the building vulnerability at the level of single buildings
due to its seismic propensity and the presence of an invaluable cultural heritage, the urban territory of the city was subjected to a seismic zoning which allowed to produce an accurate map of the amplification factor (MAF)
there were several (moderate) earthquakes during 1900 whose damages were accurately reported; this allowed us to perform a realistic data assimilation which allowed to appropriately fix model parameters.

The scheme of the simulation experiment was as follows:

seismic data are inserted into the model: epicentre coordinates (Lat, Long), magnitude, hypocentral depth, GMPE (Eq. 1) and amplification factor;
the simulation model estimates the diffusion dynamic of the shake wave and determines the Macroseismic Intensity I in the different areas affected by the quake, also accounting for site amplification effects;
the model produces a Damage Map of the different infrastructures (buildings, roads, electrical network, water and gas pipelines based on vulnerability functions of the different assets [7, 16–18]);
building collapse is produced and the volume of debris able to clutter the streets was estimated

3 Results and Discussion

In this work we limited our investigation to building disruption and its role in producing roads obstruction due to debris. The analysis thus produced a map of road unavailability (due to debris obstruction) and the consequent redesign of the contingency road-map to be used to reach the buildings identified as public shelters foreseen in the Emergency Plan.

The implementation within CIPCast-ES of the Florence case-study allowed estimating of the expected physical damage both to buildings and to the urban road network. Simulations were carried out to reproduce the effects of two recent historical earthquakes in the Florece area (Impruneta 1895 with $$M_w=5.5$$

and Mugello 1919 with $$M_w=6.3$$

). In order to make predictions for other possible situations (i.e. different magnitude of the quakes) simulation of further (simulated) events of smaller and larger magnitudes were also performed. Thus, for each event, 4 different simulations were carried out with $$M_w= 4.4, 5.5, 6.3, 7.3$$

. Results of major quakes are pictorially reported in Figs. 2 and 3, while a summary of the obtained results reported in Table 1 and explained in the following.

Table 1.

Nr. of connected, isolated and interrupted roads after seismic event. Results obtained after different simulations for the two historical events considered (Impruneta 1895 and Mugello 1919). Asterisk* indicates magnitudes actually occurred

Epicentre	Impruneta				Mugello
Magnitude	4.4	5.5*	6.3	7.3	4.4	5.5	6.3*	7.3
Connected	100%	100%	97.0%	86.5%	100%	100%	100%	98.1%
Isolated	0%	0%	0.6%	3.7%	0%	0%	0%	0.5%
Interrupted	0%	0%	2.4%	9.8%	0%	0%	0%	1.4%

Table 2.

Nr. of still reachable EPRs after seismic event. Results obtained after different simulations for the two historical events considered (Impruneta 1895 and Mugello 1919). Asterisk* indicates magnitudes actually occurred

Epicentre	Impruneta				Mugello
Magnitude	4.4	5.5*	6.3	7.3	4.4	5.5	6.3*	7.3
Prefecture (1)	1	1	0	0	1	1	1	0
City Hall (1)	1	1	1	1	1	1	1	1
Fire Station (3)	3	3	3	2	3	3	3	3
Hospital (12)	12	12	12	10	12	12	12	12
Police Station (11)	11	11	9	9	11	11	11	10
Assistance Center (27)	27	27	26	26	27	27	27	26
Assistance Area (15)	15	15	15	15	15	15	15	15
Waiting Area (19)	19	19	19	18	19	19	19	19
Storage Area (3)	3	3	3	3	3	3	3	3

For each of the designed test-cases, we estimated the fraction of roads that were kept operable and connected to the largest operable area. This index provides a first quantitative estimate of the connectivity of the city. As reported in Table 1 the seismic events effectively occurred in the past (marked by asterisks) did not lead to loss of road viability (in Florence), while synthetic (stronger) seismic events are expected to lead to some 13.5% loss of connectivity. The total unreachable area results from the contribution of interrupted roads and isolated ones, that is some 3.7% of roads are not covered by debris and yet disconnected. It is worth stressing that islands are also isolated from the main roads incoming into the city. The simulations also provide a (minimal) list of roads that need to clear from the rubble in order to reconnect islands to the main operable area. This information can be used by the rescue team in order to optimise their efforts. Present simulations are deterministic, assuming an average level of debris to fall in the closest road thus representing the worst-case scenario (the real phenomenon is rather stochastic and the exact prediction of the resulting islands cannot be easily performed).

CIPCast-ES would also allow to estimate the level of damage of other infrastructures: damages to electrical wires and substations, telecommunication cables and water pipelines can be also predicted and the consequent level of service reduction estimated. In the present work, however, the focus has been given to buildings collapse only and to the consequent reduction of road functionality due to the current lack of data on Critical Infrastructure. CIPCast-ES has been tested on Critical Infrastructure damages in the area of the city of Roma where a large dataset on critical infrastructure is available.

../images/477940_1_En_6_Chapter/477940_1_En_6_Fig2_HTML.png — Fig. 2.
Simulation result (1919 event, M = 7.3): EPRs and road functionality. The location of different EPR (symbols in the inset) can be identified and compared with the emergence of islands. The Prefecture is located in the disconnected area and it is thus flagged with a red spot. (Color figure online)

../images/477940_1_En_6_Chapter/477940_1_En_6_Fig3_HTML.png — Fig. 3.
Simulation result (1895 event, M = 7.3): EPRs and road functionality. As in Fig. 2 for the other event. This event produces a larger disconnected area containing several EPR (symbols with red spots inside). Also in this case, the Prefecture is located in one (the larger) disconnected area. (Color figure online)

As already mentioned, apart from the extension of the damage and reachable area, an other important information is related to the location of the EPR (Fig. 4). Table 2 reports the number of reachable EPR as a function of the seismic intensity. The number of each EPR is reported in parentheses in the first column. When, upon the quake, the number of reachable EPR is not equal to the total number of that EPR, the value is reported in bold character in the Table. The prediction of the map of operable EPR allows to design appropriate contingency plans according to the seismic intensity. While results show that for low-moderate intensities the EPR availability is still granted, in case of stronger events (Mw>6.3) their possible unavailability and/or unreachability should be appropriately considered. Beside the contingency planning these information may also support the urban development and maintenance plans. It is worth stressing that, the Prefecture and the City Hall, which are supposed to host the headquarters for emergency management, were left operable in the real events, but they were not reachable in the simulations. This result is significant and implies to redesign contingency plans. Strictly speaking those two buildings, with great historical and architecture value, should be allocated to other less critical activities, not hosting potential centres for emergency coordination. The buildings themselves are solid and they could support also strong seisms, however they are surrounded by other buildings that may isolate them. This may significantly undermine management capabilities upon severe events.

../images/477940_1_En_6_Chapter/477940_1_En_6_Fig4_HTML.png — Fig. 4.
Simulation results (1895 event, M = 7.3): EPRs and accessibility. Same as Fig. 3 with a larger view on the city. The event seems capable to produce a number of islands in the city center where the most of historical heritages is located. The outer parts of the city seem to be unaffected by road unavailability; this is a result which could be carefully exploited in the design of the emergency plan.

4 Conclusions

We presented novel results achieved by the CIPcast-ES simulator and a methodology to improve the management of seismic-induced crisis in urban areas. Most of the results refers to the city of Florence, imagining seismic events similar or close to the historical ones occurred in the last century; however the approach can be extended to any urban area, providing the structural data and the topology of the city are known. A deterministic model for building stability and the fall of debris on the road allows to infer operability of buildings and roads’ practicability. Predicting the damages of human edifices and infrastructures allows to foresee seisms impact and plan the resources for mitigation. The present approach allows also to predict the “islanding” phenomenon where areas are still operable on their own although being not reachable from the other areas of the city. The combination of the CIPCast-ES capabilities with an accurate road network analysis upon building disruptions has allowed to produce a realistic map of the level of connectivity of the city and the reachability level of the most relevant EPR in a city after a seismic event of a given magnitude. Data assimilation with recent historical events in the area of Florence has allowed to fix appropriate models parameters (in particular for the choice of the seismic waves propagation law), producing a model setting able to provide reliable results. As an example on how the simulation results could be appropriately exploited, the prediction of a large islanding in Florence for large quakes scenarios (Figs. 2, 3 and 4) with the subsequent disconnection of major EPRs such as City Hall and Prefecture from the rest of the city should be necessarily considered in the specific contingency plan, to avoid its difficult revision in the hours immediately following the event (as it has been the case in other recent seismic events). The availability of other city data such as the graphs of technical networks (electrical and water distribution, telecommunication network etc.) and the location of their major active elements (transformer, Basic Telecommunication Stations, water pumping stations and reservoirs) might be further analysed; the expected damage of such infrastructures and the presence of a behavioral model (i.e. a model connecting their physical integrity with the level of service they are able to deliver) would be a further information which could compose a much reacher crisis scenario which could drive the design of more accurate and effective contingency plans.

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