Resilience and Functional Service Life of Modern Heritage Timber Buildings Amid Climate Change in Chile

Abstract

As climate change continues to manifest its effects globally, the built environment faces escalating challenges in maintaining functionality and resilience against extreme weather events. This study analyses the interaction between functional service life and building resilience amidst climate change impacts, focusing on contrasting regions within Chile: the north and extreme south. Through a series of 158 case studies, this research examines how buildings in these regions adapt and endure under changing climatic conditions. Employing a qualitative and quantitative approach, data collection involved on-site inspections, interviews with stakeholders, and analysis of historical records. The findings underscore the importance of localized solutions tailored to the specific climatic challenges faced by each region. Furthermore, the research highlights the significance of proactive measures such as robust design, materials selection, and maintenance protocols in enhancing building resilience. By synthesizing insights from diverse climatic contexts, this study contributes to a broader understanding of the complex dynamics shaping the functional service life and resilience of buildings in Chile. Finally, the findings offer some guidance for different stakeholders seeking to enhance the built environment against the escalating impacts of climate change.

1. Introduction

In recent decades, the resilience and service life of heritage and modern heritage buildings under climate change pressures have been increasingly studied, particularly in Europe and North America. Previous works [1,2] emphasize the vulnerability of built heritage to gradual climatic shifts such as increased temperature variability, humidity changes, and extreme weather events. While these studies have advanced methodologies for assessing risks and prioritizing conservation interventions, their focus predominantly lies in Northern Hemisphere contexts. Moreover, the application of predictive models, such as fuzzy inference systems (FIS), has shown promising results in handling the uncertainty inherent in heritage building performance forecasting [3,4,5]. However, there is a notable scarcity of empirical research in South American countries, especially addressing the resilience of modern heritage buildings—structures from the late 19th and early 20th centuries that embody cultural significance but are often overlooked in conservation frameworks [6]. In Chile, despite a growing body of research on climate adaptation [7,8], specific studies examining the functional service life and adaptive capacities of modern heritage timber constructions are virtually absent. This study seeks to bridge this critical research gap by employing a multi-scale analysis and a validated FIS model to assess and forecast the performance of modern heritage buildings in two climatically contrasting Chilean regions.

Moreover, climate change has emerged as a prominent research focus in recent decades [9], with increasing recognition of its impacts on heritage cities and constructions [10]. While major disasters exert significant consequences on heritage building performance, their occurrence is often less probable [11]. However, the effects of climate change and rising sea levels unfold gradually but can lead to severe performance impacts when they will be manifested. Effective risk management of cultural assets remains uncommon due to inadequate understanding of asset vulnerabilities, failure to accurately assess the cost of loss and damage, and the influence of external hazards [12,13].

The Intergovernmental Panel on Climate Change (IPCC) [14], established in 1988, plays the principal role in providing comprehensive assessments of climate change’s scientific, technical, and socio-economic dimensions. Its efforts encompass identifying causes, potential repercussions, response strategies, and issuing warnings along with scenario projections contingent upon human mitigation actions [15]. Preventive conservation programs are pivotal in assessing construction risks, aiming to enhance understanding of current conservation conditions (vulnerability) and threats (hazards) to mitigate further degradation and extend service life [16]. Cultural Heritage buildings face the following challenges: static-structural hazards, like floods, geotechnical issues, and seismic activity; environmental hazards including weather conditions, pollution, and the impacts of climate change; and anthropogenic factors like fires, vandalism, and population and even tourism effects [17,18].

Maintenance of heritage buildings intends to ensure their operational functionality over time, striking a balance between buildings’ performance (functional service life) and resource expenditure [19]. This entails prioritizing interventions in preventive maintenance actions to manage intrinsic vulnerabilities and external hazards [20]. Adaptation efforts requiring substantial investment are often staggered and occasional. Long-term vision is imperative in developing suitable strategies, encompassing disaster management and preservation planning [21].

In South American developing countries, infrastructure and heritage buildings are usually at risk for several reasons, but essentially due to the neglect of the local environment because limited funding is assigned to the structures’ protection over time [22,23]. Regarding the specific review conducted by Fatorić and Seekamp in 2017, North America, North Europe, and Asia emerge as the primary regions engaged in studies and research concerning the impacts of climate change on constructions. Conversely, in the Southern Hemisphere, particularly in South America, a notable knowledge gap persists in this domain [24]. Consequently, further research is imperative to advance the formulation of approaches for ranking various adaptation strategies and future mitigation policies [25].

This study innovatively examines the anticipated impacts of climate change on the operational efficiency of heritage buildings across diverse regions in Chile. It scrutinizes the trajectory of functional service life of these structures over time, particularly under climate change scenarios simulated through various models. A comprehensive analysis of 158 case studies located in both the north and the extreme south of Chile (specifically, the Arica and Parinacota and Magallanes regions) has been developed. Employing a fuzzy inference system (FIS) grounded in artificial intelligence, the study manages a spectrum of input and output variables associated with the functional performance of the buildings.

The novelty of this research lies in a multi-scale analysis that spans from individual heritage buildings, each with distinct characteristics, to the broader context of the city in which they are located, thereby encompassing the cultural landscape of Chile in its entirety. The findings gathered from this study offer valuable insights into the resilience of local heritage constructions in northern and extreme southern Chile. Furthermore, the methodology proposed could be tailored to facilitate the development of new protocols, adaptation measures, and mitigation strategies. These initiatives aim to guide future maintenance efforts and mitigate the degradation of buildings in other regions across the globe.

This study arises from the imperative need to formulate an integrated public policy for managing and safeguarding local heritage buildings in south Chile. This policy is grounded in the principles outlined by the General Law of Urban Planning and Construction (LGUC), the Community Regulatory Plan (CRP), and the Ministry of Housing and Urban Planning (MINVU) of the Chilean government. The findings of this research are of significant value to researchers and stakeholders responsible for preserving heritage buildings, as they contribute to minimizing the risk of structural failures and promoting the revitalization of urban spaces across Chile.

Considering this, the main aim of this research work is to assess the functional service life and resilience of modern heritage buildings in Chile under the impacts of climate change, using a multi-scale analysis approach.

2. Materials and Methods

2.1. Climatic and Geographical Characterisation of the Locations

In this study, two cities located in Chile (Arica in the north and Punta Arenas in the extreme south) have been analyzed. Their geographic locations are shown in Figure 1.

Arica is located on the northern border of Chile, just 18 km (11 miles) south of the border with Peru. The city was founded in 1584, is currently the capital of Arica and Parinacota region, and its population is over 185,288 inhabitants. Its elevation is 2 m above Pacific Ocean sea level, latitude 18°28′30″ S and longitude 70°19′00″ W. The climatic condition of Arica in terms of Köppen classification is a hot desert climate (BWh) [26,27]. Unlike many other cities with arid climates, Arica rarely experiences extreme temperatures throughout the year. Despite its lack of precipitation, humidity and clouds are normally high. Average rainfall is slightly over 0.2 mm in the months of January, February, March, May and June, while the remaining months present less rainfall [7].

Punta Arenas is located on the Brunswick peninsula and is over 1418.4 km from the north coast of the Antarctic continent. The city was founded in 1848, is currently the capital of the Magallanes region in the extreme south of Chile, and supports a population of over 148,391 inhabitants. Its elevation is over 34 m, latitude 53°09′46″ S and longitude 70°54′29″ W. Considering its climatological conditions according to the Köppen climate classification, the climate of Punta Arenas is subpolar oceanic (Cfc) [26]. This means that it has a moderately cold winter, without going below 0 degrees on average in any month, and a very mild and cool summer; and in addition, precipitation is distributed uniformly throughout the year [7,28].

In order to estimate some changes in annual rainfall and temperature in the cities of Arica and Punta Arenas for the near and distant future, data from the Centre for Climate and Resilience Research (CCRR) were used [8,29]. The available data—referring mainly to the difference between the annual average temperature, annual maximum average temperature, annual minimum average temperature and total annual rainfall—were used for the near future period (2020–2044) and distant future period (2045–2069), with weather for two extreme climate change scenarios using a Representative Concentration Pathway (RCP): RCP2.6 and RCP8.5 of the Fifth Assessment Report (AR5) IPCC [29]. (i) RCP2.6—the optimistic situation of climate change, with a forecasted CO₂ atmospheric concentration of 421 ppm in 2100 [30,31]; (ii) RCP8.5—the pessimistic situation of climate change, with a forecasted CO₂ atmospheric concentration of 936 ppm in 2100 [30,32]. Considering AR5, the RCP uses the same climate change scenarios as in AR6 in terms of geophysics and behavior of the planet’s climate system [33].

In comparison, in 2011 the concentration of CO₂ was 391 ppm which exceeds pre-industrial levels by around 40%. The differences in temperature are based on simulations on the fifth phase of the Coupled Model Intercomparison Project (CMIP5), which is intended to provide advanced knowledge of climatic variances and climate change conditions [32,34]. In the situation of the RCP2.6 scenario, 22 models were considered and, regarding the RCP8.5 scenario, a total of 36 models were used.

2.2. Case Studies Characterisation

The set of case studies are categorized as heritage buildings by the Heritage Building Conservation (HBC), CRP and considering the MINVU Chile [35]. The sample comprised 158 buildings, which were built between the last decades of the 19th century and the first decades of the 20th century. A set of 101 buildings is located in the city of Punta Arenas (extreme south of Chile) and the remaining 57 case studies are in the city of Arica (north Chile). A detailed characterization of the case studies is described in

Table 1. Characterization of the 57 case studies in Arica, north Chile.

Characteristics Analysed		Number of Case Studies	% of Case Studies
Region	Arica and Parinacota	57	100%
Location (city)	Arica	57	100%
Year of construction	>1901	36	63%
	<1900	2	4%
Use	Services	37	65%
	Housing	20	35%
Legal status	Private	44	77%
	Public	13	23%
Foundations	Concrete	32	56%
	Timber	24	42%
	Adobe	1	2%
Structure	Timber and adobe	28	49%
	Concrete	16	28%
	Masonry	6	11%
	Adobe	5	9%
	Masonry and steel	1	2%
	Steel	1	2%
Cover	Galvanised steel	48	84%
	Fibre cement	3	5%
	Clay tile	3	5%
	Concrete	2	4%
	Polycarbonate	1	2%
Facade	Rendered	57	100%

and

Table 2. Characterization of the 101 case studies in Punta Arenas, extreme south Chile.

Characteristics Analysed		Number of Case Studies	% of Case Studies
Region	Magallanes	101	100%
Location (city)	Punta Arenas	101	100%
Year of construction	<1900	101	100%
Use	Services	74	73%
	Housing	27	27%
Legal status	Private	84	83%
	Public	17	17%
Foundations	Concrete	101	100%
Structure	Masonry	76	75%
	Timber	22	22%
	Timber-framed masonry	3	3%
Cover	Galvanised steel	101	100%
Facade	Rendered	78	77%
	Galvanised steel	17	17%
	Timber	3	3%
	Rendered and galvanised steel	2	2%
	Brick	1	1%

and shown in Figure 2 and Figure 3.

When considering the most common architectural characteristics of the several buildings studied in Arica, it is notable to highlight the following: (i) concrete foundations are used in 56% of the sample; (ii) a total of 49% of cases have a structure based on timber and adobe; (iii) galvanized steel is the most common material in building roofs (86%); (iv) all buildings (100% of the sample) have a rendered facade as cladding (Table 1). On the other hand, the case studies located in Punta Arenas presented construction characteristics as follows: (i) concrete is the foundation material in 100% of cases; (ii) a total of 76% of the cases use masonry in their structure; (iii) the covers of 100% of the sample were galvanized steel; finally, (iv) the most common façade material was rendering (78%) (Table 2).

2.3. Digital Fuzzy Logic Model Focused on Functional Service Life Prediction

In this study, the model used to forecast the functional service life of buildings relies on the fuzzy set theory, pioneered by Lotfi A. Zadeh [36]. Fuzzy systems have proven effective in enabling decision-making processes within various engineering systems [37,38], particularly when the phenomenon being studied involves a level of inherent uncertainty. This is particularly relevant when analyzing both the physical and functional service life of structures, as well as assessing external risks stemming from various internal and external factors [5].

Macias-Bernal et al. (2014) introduced an initial iteration of the model used in this study, known as the fuzzy building service life (FBSL) model [3]. This model estimates the comprehensive functional performance of buildings by incorporating a set of 17 input variables: (i) five variables (v₁–v₅) concerning intrinsic vulnerabilities (

Table 3. Fuzzy logic system input parameters [3].

Vulnerability and External Risk	Parameters	Ranges	Qualitative Valuation
Vulnerability parameters	Geological location (v1)	(1–4)	Constant value in Chile: 3.9 and 4.0 (*)
	Roof design (v2)	(1–8)	1—Fast evacuation of water 8—Complex and slow evacuation of water
	Environmental conditions (v3)	(1–8)	1—Building without complex constructions around it 8—Building located inside the complex constructions around it
	Constructive system (v4)	(1–8)	1—Uniform characteristics of constructive system 8—Heterogeneous characteristics of constructive system
	Conservation (v5)	(1–8)	1—Optimal state of conservation 8—Neglected state of conservation
Static-Structural parameters	Load state modification (r6)	(1–8)	1—Apparent modification 8—Disorderly modification
	Live loads (r7)	(1–8)	1—Live load lower than the original level 8—Live load higher than the original level
	Ventilation (r8)	(1–8)	1—Natural cross-ventilation in all areas 8—No natural cross-ventilation
	Facilities (r9)	(1–8)	1—All facilities are in use 8—Facilities cannot be used
	Fire (r10)	(1–8)	1—Low fire load in relation to the combustible structure 8—High fire load in relation to the combustible structure
	Inner environment (r11)	(1–8)	1—Maximum level of health. Cleanliness and hygiene of the building’s spaces 8—Low level of health. Cleanliness and hygiene of the building’s spaces
Atmospheric parameters	Precipitation (r12)	(1–8)	1—Area with low/medium/maximum annual rainfall 8—Area with maximum annual rainfall
	Temperature (r13)	(1–8)	1—Area with low temperature differences 8—Area with maximum temperature differences
Anthropic parameters	Population growth (r14)	(1–8)	1—Population growth greater than 15% 8—Population growth less than 5%
	Heritage value (r15)	(1–8)	1—Properties with great historical value 8—Properties with low historical value
	Furniture value (r16)	(1–8)	1—Social, cultural and liturgical appreciation (high value) 8—Social, cultural and liturgical appreciation (low value)
	Occupancy (r17)	(1–8)	1—High occupancy in the building 8—Low occupancy in the building

(*) Indicates constant values that vary in accordance with the geographical situation.

) and (ii) 12 external hazards (r₆–r₁₇), outlined in Table 3. The fuzzy inference system is executed using open-access software [39].

The methodology was upgraded considering the new version (FBSL_2.0) through an analysis of the ISO 31000:2011 risk management international standard [40]. This FIS undertook validation and correlation with another predictive model that assesses physical service life or degradation of building components. Findings indicated that as the degradation of building components increases, their functionality index decreases. To conduct this analysis, the degradation status of 647 claddings (203 natural stone claddings, 183 ceramic claddings, 84 rendered façades, and 177 painted surfaces) in the Lisbon, Almada, and Algarve regions of Portugal was assessed. A strong relationship between the two indexes considered was observed (with determination coefficients of 0.756 for natural stone claddings, 0.764 for ceramic claddings, 0.673 for rendered façades, and 0.833 for painted surfaces), revealing an inverse correlation within the sample [4].

The initial step in implementing the proposed model involves the fuzzification stage, wherein crisp values are converted into degrees of membership within fuzzy sets [41]. The model uses Gaussian-type membership functions, deemed most suitable for modelling the functional deterioration of heritage buildings. However, for the input variable v₁—geological location, trapezoidal membership functions are employed, considering four types of terrain. Li et al. (2002) affirm that Gaussian-type membership functions demonstrate high precision with minimal mean square error [42]. The 17 input variables undergo fuzzification into membership functions denoted as μ_A; where U represents the universe of discourse within which a fuzzy set can encompass any value defined in the range [0, 1]. Each element is assigned a membership degree in the fuzzy set A, ranging from 0 to 1, as determined by the membership function μ (Equation (1)).

$${\mu }_A:U\to \left[\mathrm{0,1}\right]$$

(1)

The next step is the knowledge-based and inference rules stage. In the model’s design phase, consultations were conducted with a panel of 15 experts specializing in heritage building management. This panel comprised individuals engaged in building rehabilitation, conservation professors, architects, archaeologists, civil engineers, and heritage building managers. Expert knowledge guided the development of various combinations, including input and output membership functions, a knowledge base, fuzzy rules, and hierarchical structures. Through collaborative decision-making, a total of 354 inference rules were established based on insights gathered from the expert survey [3,4]. The Delphi method was employed to process the responses provided by experts during the survey phase [43]. The knowledge base comprises a collection of fuzzy rules, incorporating linguistic labels that encapsulate expert insights into the controlled system. Mamdani’s fuzzy model, a widely accepted algorithm, is utilized in this study. The functional service life model (FBSL_2.0) was formulated as a modus ponens—Equation (2) [33].

$$Fuzzy inference rule \left(m\right):IF X is A and Y is B, \dots THEN Z is C$$

(2)

where A, B, and C represent linguistic values defined by fuzzy sets. Utilizing fuzzy sets allows for the encapsulation of knowledge to describe the overall performance of the system. The “IF” part of the rule defines the premise, which consists of combinations of input membership functions, while the “THEN” part of the rule specifies the consequence, denoted by output membership functions.

Defuzzification is used to derive crisp values representing the fuzzy data generated by the model (output). In this study, the Centre of Area (CoA) method, known for its standardized and effective defuzzification procedures, is employed [44]. Equation (3) presents the mathematical expression for defuzzification in the proposed model, enabling the estimation of the functionality index for the analyzed structures. The model yields a semi-qualitative index as output, describing the functional performance of each building analyzed, based on evaluations by specialists.

$${FBSL}_{2.0}={\displaystyle \frac{{\Sigma }_i{y}_i·{\mu }_B\left(y_i\right)}{{\Sigma }_i{\mu }_B\left(y_i\right)}}$$

(3)

2.4. Stakeholders and Community Survey

As part of the data collection methodology, a structured survey was conducted to integrate the perceptions and experiences of local residents and property owners of heritage timber buildings. The aim of this survey was to complement the technical assessments with social insights regarding the use, conservation, and perceived value of immovable cultural heritage (ICH) assets.

A total of 10 surveys were conducted between November 2020 and March 2021 in the cities of Arica, Punta Arenas, Valdivia, Osorno, Puerto Octay and Puerto Montt. The survey period coincided with the COVID-19 pandemic in Chile, which posed significant challenges to broader community engagement and restricted the number of participants. Despite these limitations, valuable qualitative data were collected from a targeted group of respondents, including building owners, residents, and individuals involved in community activities related to heritage sites.

The survey included multiple-choice and open-ended questions focused on four main topics: (i) knowledge of and emotional connection to heritage assets; (ii) maintenance practices and perceptions of state support; (iii) community participation in activities held at heritage sites; and (iv) perceived importance of recognizing and preserving architectural heritage. Results indicated a general awareness of the historical importance of local buildings, but also highlighted limited formal support mechanisms for maintenance and a need for stronger community engagement strategies.

The insights obtained from the surveys informed the calibration of key variables in the fuzzy inference system (FBSL_2.0), especially those linked to conservation condition (v₅), occupancy levels (r₁₇), and perceived heritage value (r₁₅). By incorporating direct stakeholder input into the model, the research enhances the contextual relevance and applicability of the resilience assessment for modern heritage timber buildings in Chile.

3. Results and Discussion

Section 1 includes the application of the proposed FIS model to the case studies analyzed in the cities of Arica and Punta Arenas (Chile), considering current climate conditions. Section 2 describes an application of the model to the total 158 case studies, under two scenarios (RCP2.6 and RCP8.5) of climate change forecasts regarding a near future period (2020–2044). Finally, Section 3 contains an application of the FIS model to the sample, under two climate change situations (RCP2.6 and RCP8.5) concerning a distant future period (2045–2069).

3.1. FIS Model Application to Heritage Timber Buildings, Under Current Climatic Conditions

Regarding current climatic conditions, the fuzzy logic model (FBSL_2.0) established a set of five input parameters (v₁—geological location; r₁₁—environmental condition; r₁₂—rainfall; r₁₃—temperature; and r₁₄—growth population), which are constant in this application to homogenizing the simulation into the set of case studies. The FBSL_2.0 has been applied in a total of 158 case studies, located in the extreme south of Chile and in the north close to the border with Peru, which are located as follows: (i) 101 case studies in Punta Arenas; and (ii) 57 case studies in Arica. In terms of their specific administrative emplacement in Chile, 36.1% are in the Arica and Parinacota region and the remaining 63.9% are in the Magallanes region.

Previous studies established that the lowest possible rate of the system is 9.0 points, which has been validated in applications in Europe (Spain, Portugal) and in Chile (Metropolitan, Valparaíso, Araucanía, Los Ríos and Los Lagos regions). This FIS was also correlated and validated with another predictive model that analyses and evaluates the physical deterioration of building components [45].

The maximum value of the functional service life index varies depending on the specific characteristics of each local context. In this sense, the highest possible value for the output was established as 72.0 points for the Arica and Parinacota region (

Table 4. Ranges of functional service life conditions (output) concerning Arica and Parinacota region, Chile.

Range	Conditions	Description
[72–30]	A	The vulnerabilities and risks are regarded as so small that they do not require any kind of intervention. (The maximum value (72.0 points) was achieved ranking all the variables as 1.0 point (the best possible), with the exception of v1 = 1.0; r11 = 2.5; r12 = 1.0; r13 = 1.0; r14 = 3.0).
[30–20]	B	The vulnerabilities and risks are regarded as medium, where costs and benefits are considered and balanced.
[20–9]	C	The vulnerabilities and risks are regarded as significantly dangerous to the structures; building inspection and possible intervention in the short term is recommended.

) and it was established as 53.0 points for the Magallanes region (

Table 5. Ranges of functional service life conditions (output) concerning Magallanes region, extreme south of Chile.

Range	Conditions	Description
[53–30]	A	The vulnerabilities and risks are regarded as so small that they do not require any kind of intervention. (The maximum value (53.0 points) was achieved ranking all the variables as 1.0 point (the best possible), with the exception of v1 = 1.0; r11 = 5.0; r12 = 6.0; r13 = 5.0; r14 = 4.0).
[30–20]	B	The vulnerabilities and risks are regarded as medium, where costs and benefits are considered and balanced.
[20–9]	C	The vulnerabilities and risks are regarded as significantly dangerous to the structures; building inspection and possible intervention in the short term is recommended.

).

Table 6. Valuation of the functional of the FIS model for the 57 case studies located in Arica (north Chile).

Ids	Location	v1	v2	v3	v4	v5	r6	r7	r8	r9	r10	r11	r12	r13	r14	r15	r16	r17	FBSL2.0	Condition
ARI-27	Arica	4.0	7.5	7.0	7.0	8.0	5.0	7.0	8.0	8.0	8.0	2.5	1.0	1.0	3.0	1.0	8.0	8.0	19.38	C
ARI-19	Arica	4.0	8.0	8.0	8.0	8.0	8.0	7.0	8.0	8.0	5.0	2.5	1.0	1.0	3.0	1.0	8.0	8.0	19.40	C
ARI-28	Arica	4.0	5.4	4.5	3.2	6.2	4.5	4.0	5.2	4.5	3.5	2.5	1.0	1.0	3.0	1.0	6.0	5.5	22.84	B
ARI-52	Arica	4.0	5.7	7.2	2.8	8.0	2.4	6.0	8.0	8.0	4.7	2.5	1.0	1.0	3.0	1.0	8.0	8.0	23.56	B
ARI-50	Arica	4.0	5.6	7.0	3.0	7.2	3.5	5.0	7.0	7.0	4.7	2.5	1.0	1.0	3.0	1.0	7.0	7.0	24.40	B
ARI-43	Arica	4.0	5.5	5.5	4.0	6.8	4.2	3.4	5.5	5.0	3.0	2.5	1.0	1.0	3.0	1.0	6.0	6.0	24.90	B
ARI-35	Arica	4.0	5.6	5.4	2.5	5.6	3.5	4.0	5.3	4.2	2.5	2.5	1.0	1.0	3.0	1.0	5.5	5.0	26.30	B
ARI-39	Arica	4.0	5.4	6.5	3.0	7.2	5.0	5.3	5.6	5.5	4.0	2.5	1.0	1.0	3.0	1.0	6.0	5.0	26.62	B
ARI-38	Arica	4.0	5.4	7.2	2.0	7.4	5.0	5.4	7.0	6.6	5.0	2.5	1.0	1.0	3.0	1.0	6.5	6.0	26.64	B
ARI-29	Arica	4.0	5.2	6.7	3.4	6.0	4.5	4.5	5.0	4.5	3.7	2.5	1.0	1.0	3.0	1.0	5.5	4.5	26.74	B
ARI-44	Arica	4.0	5.5	5.2	3.3	6.8	4.0	5.5	5.5	5.5	2.4	2.5	1.0	1.0	3.0	1.0	6.0	4.0	26.97	B
ARI-36	Arica	4.0	5.5	7.0	2.8	6.4	5.2	5.8	5.5	4.5	4.8	2.5	1.0	1.0	3.0	1.0	5.0	4.0	28.17	B
ARI-22	Arica	4.0	5.0	7.0	3.2	6.2	5.0	5.5	5.0	4.5	4.5	2.5	1.0	1.0	3.0	1.0	5.5	4.0	28.78	B
ARI-37	Arica	4.0	5.7	7.0	2.2	6.7	4.7	5.0	5.5	4.5	4.8	2.5	1.0	1.0	3.0	1.0	5.0	4.5	28.79	B
ARI-45	Arica	4.0	5.5	6.0	3.3	5.6	4.0	4.5	5.0	4.0	3.2	2.5	1.0	1.0	3.0	1.0	5.0	5.0	29.13	B
ARI-33	Arica	4.0	5.2	6.7	3.3	6.3	4.0	4.0	5.0	4.0	3.8	2.5	1.0	1.0	3.0	1.0	5.0	3.5	29.42	B
ARI-51	Arica	4.0	7.2	4.5	2.7	4.0	3.6	4.0	4.5	4.0	3.0	2.5	1.0	1.0	3.0	1.0	4.5	4.0	30.81	A
ARI-40	Arica	4.0	5.6	5.4	3.5	5.0	4.0	5.5	5.3	3.5	3.0	2.5	1.0	1.0	3.0	1.0	5.4	4.0	31.07	A
ARI-47	Arica	4.0	5.6	7.2	3.0	5.6	4.0	5.0	5.0	4.0	4.5	2.5	1.0	1.0	3.0	1.0	5.0	4.0	31.21	A
ARI-30	Arica	4.0	5.0	7.0	2.4	6.6	4.0	4.5	6.0	5.4	4.8	2.5	1.0	1.0	3.0	1.0	6.5	5.0	31.89	A
ARI-49	Arica	4.0	5.3	6.6	3.0	5.5	3.7	4.0	5.4	5.0	4.0	2.5	1.0	1.0	3.0	1.0	6.0	6.0	33.52	A
ARI-24	Arica	4.0	5.4	6.6	3.2	5.7	4.0	5.3	5.4	4.0	4.0	2.5	1.0	1.0	3.0	1.0	4.5	3.5	33.53	A
ARI-31	Arica	4.0	5.4	7.0	3.2	6.0	4.0	4.0	5.5	5.0	4.0	2.5	1.0	1.0	3.0	1.0	5.0	4.5	34.01	A
ARI-16	Arica	4.0	7.2	5.5	4.5	5.7	3.5	4.0	5.0	4.5	3.2	2.5	1.0	1.0	3.0	1.0	4.5	3.5	35.36	A
ARI-26	Arica	4.0	5.0	6.5	3.0	5.8	4.5	5.0	4.5	4.5	4.2	2.5	1.0	1.0	3.0	1.0	5.0	4.5	35.42	A
ARI-23	Arica	4.0	5.0	7.0	2.5	5.8	4.6	5.0	5.0	4.2	4.5	2.5	1.0	1.0	3.0	1.0	4.8	3.5	35.97	A
ARI-41	Arica	4.0	5.3	6.6	1.6	4.8	3.0	3.5	5.0	4.0	4.0	2.5	1.0	1.0	3.0	1.0	5.5	5.5	36.77	A
ARI-55	Arica	4.0	6.2	5.6	2.5	7.5	2.5	2.4	3.0	6.0	3.0	2.5	1.0	1.0	3.0	1.0	4.0	4.5	37.89	A
ARI-11	Arica	4.0	6.0	6.0	5.0	5.0	4.0	3.5	3.5	3.5	3.5	2.5	1.0	1.0	3.0	1.0	3.4	2.5	38.13	A
ARI-07	Arica	4.0	7.0	6.5	5.0	5.0	2.0	3.5	4.0	3.5	3.5	2.5	1.0	1.0	3.0	1.0	3.5	3.4	39.49	A
ARI-56	Arica	4.0	7.5	4.6	6.4	5.4	4.5	4.0	4.0	4.0	3.3	2.5	1.0	1.0	3.0	1.0	3.6	1.2	39.61	A
ARI-13	Arica	4.0	6.0	6.5	3.0	5.6	4.0	3.5	5.5	4.0	3.6	2.5	1.0	1.0	3.0	1.0	5.0	2.0	39.65	A
ARI-53	Arica	4.0	6.0	6.2	3.4	5.4	4.0	5.3	4.0	3.0	3.2	2.5	1.0	1.0	3.0	1.0	2.6	2.4	39.69	A
ARI-34	Arica	4.0	5.5	7.0	2.9	5.3	3.0	3.0	4.5	4.0	4.0	2.5	1.0	1.0	3.0	1.0	4.0	3.0	39.70	A
ARI-10	Arica	4.0	7.3	3.5	2.2	4.5	2.8	3.0	4.0	3.5	3.0	2.5	1.0	1.0	3.0	1.0	3.0	3.0	39.78	A
ARI-09	Arica	4.0	6.2	6.5	2.6	5.2	3.0	3.5	3.2	3.0	4.0	2.5	1.0	1.0	3.0	1.0	3.0	3.0	39.95	A
ARI-54	Arica	4.0	6.4	4.0	2.6	5.0	3.6	3.4	3.5	3.0	3.0	2.5	1.0	1.0	3.0	1.0	3.5	2.0	39.97	A
ARI-42	Arica	4.0	5.4	6.6	3.0	5.5	3.6	4.0	5.5	5.0	4.0	2.5	1.0	1.0	3.0	1.0	5.0	5.0	39.99	A
ARI-57	Arica	4.0	6.5	5.2	4.0	4.6	4.0	4.0	3.0	2.5	3.0	2.5	1.0	1.0	3.0	1.0	3.5	2.0	40.12	A
ARI-58	Arica	4.0	6.5	4.0	2.0	4.5	4.0	4.4	3.0	3.0	2.7	2.5	1.0	1.0	3.0	1.0	3.5	2.0	40.61	A
ARI-05	Arica	4.0	6.5	7.0	3.5	5.4	3.2	3.5	4.0	3.0	4.0	2.5	1.0	1.0	3.0	1.0	3.0	2.0	40.77	A
ARI-21	Arica	4.0	5.0	3.5	3.0	6.0	4.8	5.5	4.0	3.5	2.5	2.5	1.0	1.0	3.0	1.0	4.0	2.5	40.87	A
ARI-06	Arica	4.0	6.5	7.0	4.0	4.5	3.0	3.5	3.5	3.0	4.0	2.5	1.0	1.0	3.0	1.0	3.0	2.0	40.92	A
ARI-04	Arica	4.0	7.0	7.0	4.0	5.0	4.0	4.0	3.5	2.0	4.0	2.5	1.0	1.0	3.0	1.0	1.0	1.0	41.46	A
ARI-12	Arica	4.0	5.4	2.7	5.0	5.5	4.0	4.0	5.5	4.3	3.0	2.5	1.0	1.0	3.0	1.0	5.0	2.0	41.55	A
ARI-32	Arica	4.0	5.2	7.0	3.3	5.5	3.8	3.0	3.5	3.0	5.0	2.5	1.0	1.0	3.0	1.0	2.5	2.3	41.82	A
ARI-03	Arica	4.0	2.5	5.6	3.0	5.5	4.0	3.0	4.0	3.5	4.2	2.5	1.0	1.0	3.0	1.0	4.0	4.0	41.99	A
ARI-46	Arica	4.0	5.4	5.2	3.0	5.2	3.5	5.0	4.0	3.5	3.2	2.5	1.0	1.0	3.0	1.0	3.5	3.0	42.10	A
ARI-18	Arica	4.0	3.6	6.5	3.2	5.4	3.5	3.0	5.0	4.3	4.5	2.5	1.0	1.0	3.0	1.0	5.0	4.0	42.51	A
ARI-20	Arica	4.0	5.5	6.5	3.5	5.5	4.0	3.5	4.5	3.5	4.5	2.5	1.0	1.0	3.0	1.0	4.5	2.5	42.57	A
ARI-01	Arica	4.0	5.0	5.5	2.4	5.0	3.0	3.5	4.0	3.0	4.0	2.5	1.0	1.0	3.0	1.0	3.0	3.5	42.61	A
ARI-15	Arica	4.0	6.5	7.0	3.0	5.0	4.5	4.0	2.5	1.5	3.7	2.5	1.0	1.0	3.0	1.0	2.0	1.0	42.87	A
ARI-17	Arica	4.0	3.2	6.5	3.6	5.7	3.5	3.0	5.2	4.3	4.5	2.5	1.0	1.0	3.0	1.0	5.0	4.0	42.94	A
ARI-02	Arica	4.0	4.5	5.0	3.0	5.2	3.5	4.0	4.0	3.0	3.7	2.5	1.0	1.0	3.0	1.0	3.0	3.5	43.59	A
ARI-61	Arica	4.0	6.5	2.0	2.4	3.5	2.6	3.0	3.5	3.4	2.7	2.5	1.0	1.0	3.0	1.0	3.0	2.4	44.70	A
ARI-14	Arica	4.0	5.0	2.4	2.4	5.0	3.0	3.5	3.0	2.5	2.8	2.5	1.0	1.0	3.0	1.0	2.5	2.4	44.84	A
ARI-08	Arica	4.0	4.0	6.8	4.2	6.2	5.0	5.4	4.0	4.0	4.2	2.5	1.0	1.0	3.0	1.0	3.5	1.5	44.85	A

and

Table 7. Valuation of the functional of the FIS model for the 101 case studies located in Punta Arenas (extreme south Chile).

Ids	Location	v1	v2	v3	v4	v5	r6	r7	r8	r9	r10	r11	r12	r13	r14	r15	r16	r17	FBSL2.0	Condition
PUN-31	Pta. Arenas	4.0	8.0	8.0	8.0	8.0	8.0	8.0	8.0	8.0	8.0	5.0	6.0	5.0	4.0	5.0	8.0	8.0	15.69	C
PUN-100	Pta. Arenas	4.0	4.5	4.0	1.5	8.0	3.0	5.0	8.0	8.0	3.5	5.0	6.0	5.0	4.0	1.0	8.0	8.0	18.05	C
PUN-70	Pta. Arenas	4.0	3.2	6.5	2.3	8.0	4.0	6.0	7.5	7.5	4.0	5.0	6.0	5.0	4.0	1.0	7.0	8.0	18.90	C
PUN-13	Pta. Arenas	4.0	4.2	6.5	3.8	5.6	2.0	2.5	4.5	3.5	6.5	5.0	6.0	5.0	4.0	1.0	5.0	5.5	19.23	C
PUN-102	Pta. Arenas	4.0	5.0	7.2	3.0	5.8	3.0	3.5	5.4	4.0	7.0	5.0	6.0	5.0	4.0	1.0	5.0	5.2	19.38	C
PUN-55	Pta. Arenas	4.0	4.0	5.0	2.2	6.4	4.0	4.0	5.2	4.8	5.0	5.0	6.0	5.0	4.0	1.0	6.0	6.2	19.64	C
PUN-110	Pta. Arenas	4.0	5.2	4.3	2.8	5.5	2.5	3.6	5.0	4.0	3.8	5.0	6.0	5.0	4.0	1.0	5.5	5.5	20.20	B
PUN-66	Pta. Arenas	4.0	2.4	4.2	2.8	6.3	4.0	4.0	5.5	4.0	5.4	5.0	6.0	5.0	4.0	1.0	6.5	6.0	20.52	B
PUN-54	Pta. Arenas	4.0	3.5	4.5	3.2	5.4	4.2	5.0	4.0	3.0	6.0	5.0	6.0	5.0	4.0	1.0	2.5	3.0	20.95	B
PUN-99	Pta. Arenas	4.0	2.0	6.5	3.3	5.3	4.0	5.4	5.3	4.0	5.0	5.0	6.0	5.0	4.0	1.0	5.5	3.5	20.99	B
PUN-103	Pta. Arenas	4.0	2.5	5.2	2.8	5.6	3.5	2.5	5.0	3.8	4.4	5.0	6.0	5.0	4.0	1.0	5.0	5.5	21.08	B
PUN-42	Pta. Arenas	4.0	3.0	5.2	2.0	6.0	4.0	5.0	5.2	4.4	4.5	5.0	6.0	5.0	4.0	1.0	5.5	4.0	21.40	B
PUN-78	Pta. Arenas	4.0	4.0	5.2	2.4	5.0	3.0	3.6	4.0	4.0	5.2	5.0	6.0	5.0	4.0	1.0	6.0	5.0	21.87	B
PUN-18	Pta. Arenas	4.0	7.0	5.0	2.6	5.3	3.5	4.0	5.5	4.0	3.8	5.0	6.0	5.0	4.0	1.0	4.5	3.0	22.01	B
PUN-14	Pta. Arenas	4.0	2.2	7.4	2.2	6.6	5.2	5.4	4.0	4.0	5.0	5.0	6.0	5.0	4.0	1.0	4.5	5.0	22.05	B
PUN-15	Pta. Arenas	4.0	2.2	4.5	2.6	5.2	3.5	3.5	5.4	4.0	6.0	5.0	6.0	5.0	4.0	1.0	6.0	5.5	22.07	B
PUN-60	Pta. Arenas	4.0	3.0	4.8	2.2	6.7	3.0	4.0	5.2	4.2	4.4	5.0	6.0	5.0	4.0	1.0	5.8	5.6	22.29	B
PUN-72	Pta. Arenas	4.0	3.0	7.2	2.2	6.8	4.0	5.0	5.0	4.4	5.0	5.0	6.0	5.0	4.0	1.0	5.0	5.0	22.33	B
PUN-86	Pta. Arenas	4.0	4.4	4.8	2.0	5.8	3.0	4.0	5.2	4.0	5.0	5.0	6.0	5.0	4.0	1.0	5.0	5.0	22.34	B
PUN-75	Pta. Arenas	4.0	3.0	4.4	3.2	5.7	4.0	5.2	5.0	4.0	5.6	5.0	6.0	5.0	4.0	1.0	5.0	4.5	22.43	B
PUN-73	Pta. Arenas	4.0	2.2	7.4	2.2	6.4	5.0	5.5	3.5	3.5	5.0	5.0	6.0	5.0	4.0	1.0	4.5	4.0	22.47	B
PUN-57	Pta. Arenas	4.0	3.0	5.0	3.0	6.0	3.0	3.5	4.8	4.0	4.5	5.0	6.0	5.0	4.0	1.0	6.0	5.5	22.53	B
PUN-94	Pta. Arenas	4.0	3.0	7.0	2.4	6.5	4.0	5.5	5.5	4.0	4.5	5.0	6.0	5.0	4.0	1.0	4.0	3.5	22.53	B
PUN-64	Pta. Arenas	4.0	4.0	5.0	3.0	5.0	3.0	4.0	4.0	3.5	6.2	5.0	6.0	5.0	4.0	1.0	4.0	4.0	22.74	B
PUN-41	Pta. Arenas	4.0	4.0	6.0	3.5	5.5	2.5	3.5	5.0	4.0	4.5	5.0	6.0	5.0	4.0	1.0	5.0	5.0	22.77	B
PUN-44	Pta. Arenas	4.0	3.8	4.6	4.5	4.4	3.5	4.0	5.0	4.0	5.0	5.0	6.0	5.0	4.0	1.0	5.5	5.5	22.89	B
PUN-62	Pta. Arenas	4.0	4.0	6.0	2.2	4.6	3.5	3.5	5.0	4.0	4.2	5.0	6.0	5.0	4.0	1.0	5.5	5.0	22.97	B
PUN-115	Pta. Arenas	4.0	3.2	7.0	2.4	5.0	3.0	3.0	4.5	3.0	5.0	5.0	6.0	5.0	4.0	1.0	5.5	5.5	23.09	B
PUN-77	Pta. Arenas	4.0	3.0	7.2	2.2	5.4	4.2	5.5	5.0	4.0	5.0	5.0	6.0	5.0	4.0	1.0	3.5	2.5	23.34	B
PUN-45	Pta. Arenas	4.0	2.8	5.0	2.2	6.0	3.5	3.5	5.0	4.0	4.2	5.0	6.0	5.0	4.0	1.0	5.0	5.0	23.41	B
PUN-105	Pta. Arenas	4.0	3.0	5.0	2.2	5.2	4.0	5.0	5.0	2.5	5.0	5.0	6.0	5.0	4.0	1.0	5.0	3.2	23.45	B
PUN-91	Pta. Arenas	4.0	4.3	4.6	2.6	6.3	4.5	4.0	5.0	4.0	4.8	5.0	6.0	5.0	4.0	1.0	3.2	2.2	23.48	B
PUN-12	Pta. Arenas	4.0	6.0	5.2	3.2	5.0	3.2	3.0	4.0	3.0	5.5	5.0	6.0	5.0	4.0	1.0	5.5	3.5	23.67	B
PUN-90	Pta. Arenas	4.0	3.2	7.4	2.3	4.8	4.0	4.2	4.5	3.5	5.0	5.0	6.0	5.0	4.0	1.0	4.0	2.8	23.67	B
PUN-47	Pta. Arenas	4.0	3.2	4.0	3.2	4.8	3.5	5.0	3.5	3.0	6.0	5.0	6.0	5.0	4.0	1.0	5.0	3.0	23.70	B
PUN-82	Pta. Arenas	4.0	3.4	7.5	2.3	5.8	4.5	5.3	5.0	3.5	5.2	5.0	6.0	5.0	4.0	1.0	2.0	2.5	23.90	B
PUN-101	Pta. Arenas	4.0	3.3	6.8	2.0	5.5	3.5	5.2	5.0	4.0	4.3	5.0	6.0	5.0	4.0	1.0	4.0	3.5	24.05	B
PUN-03	Pta. Arenas	4.0	6.0	6.2	1.5	4.0	2.0	3.5	3.5	3.0	4.0	5.0	6.0	5.0	4.0	1.0	3.5	4.5	24.13	B
PUN-35	Pta. Arenas	4.0	4.5	5.0	2.0	4.0	3.2	4.5	4.0	2.5	6.0	5.0	6.0	5.0	4.0	1.0	4.5	3.5	24.19	B
PUN-69	Pta. Arenas	4.0	3.0	7.0	3.0	5.8	4.0	5.0	5.0	3.5	5.0	5.0	6.0	5.0	4.0	1.0	4.0	2.5	24.37	B
PUN-34	Pta. Arenas	4.0	3.0	7.0	2.8	5.0	3.5	3.0	5.0	4.0	4.8	5.0	6.0	5.0	4.0	1.0	3.0	3.0	24.62	B
PUN-92	Pta. Arenas	4.0	3.0	7.2	2.6	4.6	3.6	5.2	5.0	4.0	5.0	5.0	6.0	5.0	4.0	1.0	4.0	4.0	24.62	B
PUN-98	Pta. Arenas	4.0	5.0	7.2	2.3	5.4	4.2	5.0	4.5	2.8	5.0	5.0	6.0	5.0	4.0	1.0	3.5	2.5	25.11	B
PUN-83	Pta. Arenas	4.0	3.0	7.2	2.3	6.8	3.6	5.0	4.0	3.5	5.0	5.0	6.0	5.0	4.0	1.0	4.0	2.5	25.14	B
PUN-114	Pta. Arenas	4.0	3.2	5.6	1.8	4.8	3.5	5.5	5.0	2.0	4.5	5.0	6.0	5.0	4.0	1.0	3.5	2.5	25.36	B
PUN-113	Pta. Arenas	4.0	3.4	3.6	2.3	6.5	3.4	5.0	5.5	4.5	2.5	5.0	6.0	5.0	4.0	1.0	3.5	3.5	25.57	B
PUN-89	Pta. Arenas	4.0	3.5	7.4	2.5	5.3	4.0	4.5	4.5	3.5	5.0	5.0	6.0	5.0	4.0	1.0	4.0	3.0	25.74	B
PUN-95	Pta. Arenas	4.0	2.5	4.5	2.3	5.5	4.0	4.5	4.5	4.2	4.2	5.0	6.0	5.0	4.0	1.0	5.8	2.5	25.97	B
PUN-68	Pta. Arenas	4.0	3.5	4.6	2.8	4.2	3.5	5.0	5.0	3.0	5.0	5.0	6.0	5.0	4.0	1.0	4.8	3.0	26.05	B
PUN-96	Pta. Arenas	4.0	4.2	6.5	2.7	3.8	3.4	3.0	4.0	2.0	5.0	5.0	6.0	5.0	4.0	1.0	3.0	3.0	26.13	B
PUN-71	Pta. Arenas	4.0	4.2	6.5	2.7	3.8	3.4	3.0	4.0	2.0	5.0	5.0	6.0	5.0	4.0	1.0	3.0	3.0	26.22	B
PUN-46	Pta. Arenas	4.0	3.2	6.8	2.0	6.0	3.5	4.6	4.7	3.0	4.3	5.0	6.0	5.0	4.0	1.0	3.3	3.0	26.55	B
PUN-17	Pta. Arenas	4.0	4.0	5.0	2.2	6.2	5.5	5.3	5.2	5.0	3.0	5.0	6.0	5.0	4.0	1.0	3.4	2.0	26.67	B
PUN-84	Pta. Arenas	4.0	2.5	6.4	1.8	6.6	3.5	4.0	5.2	3.0	4.2	5.0	6.0	5.0	4.0	1.0	3.5	2.5	26.67	B
PUN-85	Pta. Arenas	4.0	5.0	7.5	2.0	4.8	2.0	3.0	3.5	2.0	5.0	5.0	6.0	5.0	4.0	1.0	3.5	2.0	26.67	B
PUN-108	Pta. Arenas	4.0	3.2	5.0	3.0	6.5	4.2	5.0	5.2	3.5	4.0	5.0	6.0	5.0	4.0	1.0	4.0	2.5	26.85	B
PUN-63	Pta. Arenas	4.0	4.2	3.4	2.2	3.8	3.0	3.5	3.5	2.2	5.0	5.0	6.0	5.0	4.0	1.0	4.0	4.4	27.17	B
PUN-112	Pta. Arenas	4.0	3.0	5.0	3.0	4.0	2.5	3.5	3.8	3.8	6.2	5.0	6.0	5.0	4.0	1.0	6.0	5.5	27.18	B
PUN-67	Pta. Arenas	4.0	2.0	4.8	4.5	4.4	3.5	3.5	5.0	4.0	5.6	5.0	6.0	5.0	4.0	1.0	5.5	5.5	27.22	B
PUN-51	Pta. Arenas	4.0	4.5	7.0	2.6	5.4	3.2	3.5	4.5	4.0	4.5	5.0	6.0	5.0	4.0	1.0	3.4	3.0	27.3	B
PUN-19	Pta. Arenas	4.0	1.5	6.0	3.2	5.0	2.0	3.0	4.5	3.5	6.5	5.0	6.0	5.0	4.0	1.0	6.4	6.0	27.43	B
PUN-21	Pta. Arenas	4.0	2.4	5.0	1.5	5.2	3.0	3.0	4.5	3.5	4.0	5.0	6.0	5.0	4.0	1.0	6.0	4.5	27.46	B
PUN-79	Pta. Arenas	4.0	4.2	7.0	3.0	4.2	3.0	3.0	3.4	3.0	4.8	5.0	6.0	5.0	4.0	1.0	3.4	2.0	27.57	B
PUN-106	Pta. Arenas	4.0	3.5	6.0	2.7	5.3	3.5	5.0	4.5	3.5	4.4	5.0	6.0	5.0	4.0	1.0	3.0	2.0	27.89	B
PUN-33	Pta. Arenas	4.0	3.2	2.4	3.2	4.2	3.5	4.3	5.0	4.0	4.5	5.0	6.0	5.0	4.0	1.0	5.4	4.0	27.98	B
PUN-06	Pta. Arenas	4.0	3.0	5.0	3.0	5.0	2.0	3.0	4.5	4.0	6.0	5.0	6.0	5.0	4.0	1.0	6.0	6.0	28.00	B
PUN-88	Pta. Arenas	4.0	3.0	4.5	2.3	4.8	3.2	3.5	4.8	4.5	3.8	5.0	6.0	5.0	4.0	1.0	5.0	3.5	28.19	B
PUN-38	Pta. Arenas	4.0	3.0	4.3	1.6	5.4	2.5	3.5	4.0	4.0	4.0	5.0	6.0	5.0	4.0	1.0	6.0	6.0	28.28	B
PUN-61	Pta. Arenas	4.0	4.5	4.5	2.5	3.5	3.0	4.5	3.0	2.0	5.5	5.0	6.0	5.0	4.0	1.0	2.5	2.8	28.28	B
PUN-97	Pta. Arenas	4.0	3.2	4.5	2.4	4.6	3.5	4.5	3.5	3.0	3.8	5.0	6.0	5.0	4.0	1.0	3.0	2.5	28.28	B
PUN-02	Pta. Arenas	4.0	5.0	3.5	2.0	3.3	2.0	3.5	5.0	3.8	4.0	5.0	6.0	5.0	4.0	1.0	4.6	5.0	28.60	B
PUN-36	Pta. Arenas	4.0	4.0	5.0	5.0	4.0	2.5	3.5	3.0	2.5	5.0	5.0	6.0	5.0	4.0	1.0	4.0	2.5	28.68	B
PUN-109	Pta. Arenas	4.0	4.2	4.0	4.0	3.8	2.5	3.0	4.5	3.0	5.0	5.0	6.0	5.0	4.0	1.0	5.5	5.5	28.72	B
PUN-76	Pta. Arenas	4.0	4.5	5.0	2.8	4.0	3.5	4.4	3.0	2.0	4.0	5.0	6.0	5.0	4.0	1.0	2.0	2.0	28.74	B
PUN-20	Pta. Arenas	4.0	2.5	3.0	2.0	6.5	2.0	3.5	4.0	4.0	3.5	5.0	6.0	5.0	4.0	1.0	4.0	3.0	28.86	B
PUN-49	Pta. Arenas	4.0	4.2	6.5	2.2	5.4	1.5	3.0	4.0	2.5	5.0	5.0	6.0	5.0	4.0	1.0	3.0	2.0	28.98	B
PUN-26	Pta. Arenas	4.0	3.0	4.3	2.0	5.0	3.5	4.0	4.0	4.0	3.8	5.0	6.0	5.0	4.0	1.0	4.3	3.5	29.22	B
PUN-93	Pta. Arenas	4.0	4.2	6.5	2.2	5.3	1.5	3.0	4.0	2.5	5.0	5.0	6.0	5.0	4.0	1.0	3.0	2.0	29.49	B
PUN-24	Pta. Arenas	4.0	4.5	6.2	2.5	3.0	3.0	4.0	3.5	3.5	5.5	5.0	6.0	5.0	4.0	1.0	5.5	3.5	30.02	A
PUN-111	Pta. Arenas	4.0	3.0	5.0	2.6	4.0	3.0	3.0	5.0	3.5	4.2	5.0	6.0	5.0	4.0	1.0	3.4	3.5	30.18	A
PUN-116	Pta. Arenas	4.0	2.2	4.4	3.0	4.4	2.5	3.5	5.0	3.5	5.0	5.0	6.0	5.0	4.0	1.0	6.0	6.0	30.36	A
PUN-30	Pta. Arenas	4.0	3.2	4.6	2.4	4.2	2.5	3.5	5.0	4.0	6.0	5.0	6.0	5.0	4.0	1.0	5.5	5.5	30.52	A
PUN-52	Pta. Arenas	4.0	3.0	3.2	2.2	5.0	2.2	3.5	4.0	4.0	3.0	5.0	6.0	5.0	4.0	1.0	4.5	5.0	30.54	A
PUN-74	Pta. Arenas	4.0	3.3	4.5	2.2	5.0	3.0	3.0	4.8	3.2	3.6	5.0	6.0	5.0	4.0	1.0	3.0	2.5	30.78	A
PUN-40	Pta. Arenas	4.0	5.0	4.4	2.0	3.0	2.0	3.5	4.5	3.5	4.2	5.0	6.0	5.0	4.0	1.0	5.0	4.5	30.79	A
PUN-81	Pta. Arenas	4.0	3.0	3.5	2.2	4.5	2.0	3.5	5.0	4.0	5.5	5.0	6.0	5.0	4.0	1.0	5.0	4.5	31.55	A
PUN-104	Pta. Arenas	4.0	4.0	4.5	1.5	3.5	2.0	3.5	5.0	3.5	4.5	5.0	6.0	5.0	4.0	1.0	5.5	5.0	31.61	A
PUN-29	Pta. Arenas	4.0	3.2	4.5	2.0	3.5	5.0	4.0	4.0	2.2	4.0	5.0	6.0	5.0	4.0	1.0	3.5	3.0	31.62	A
PUN-39	Pta. Arenas	4.0	4.3	3.0	3.4	3.5	3.0	3.5	3.5	2.4	2.4	5.0	6.0	5.0	4.0	1.0	3.0	2.5	31.67	A
PUN-80	Pta. Arenas	4.0	2.0	4.5	3.0	3.2	3.0	3.5	3.5	3.5	5.0	5.0	6.0	5.0	4.0	1.0	5.0	3.5	31.80	A
PUN-25	Pta. Arenas	4.0	5.0	4.5	2.0	2.5	2.0	3.0	3.0	2.0	4.0	5.0	6.0	5.0	4.0	1.0	3.5	4.0	33.05	A
PUN-28	Pta. Arenas	4.0	2.5	3.8	3.0	3.5	2.0	3.5	5.0	4.0	4.0	5.0	6.0	5.0	4.0	1.0	5.0	5.0	33.18	A
PUN-32	Pta. Arenas	4.0	3.2	5.4	2.8	3.4	3.4	5.0	3.6	2.2	4.0	5.0	6.0	5.0	4.0	1.0	3.0	2.0	33.23	A
PUN-16	Pta. Arenas	4.0	4.2	3.5	2.4	2.5	3.5	3.0	3.5	1.5	3.5	5.0	6.0	5.0	4.0	1.0	3.5	3.0	34.24	A
PUN-65	Pta. Arenas	4.0	2.5	5.5	2.0	3.5	2.0	3.0	3.5	2.0	4.0	5.0	6.0	5.0	4.0	1.0	4.5	2.5	34.70	A
PUN-48	Pta. Arenas	4.0	5.0	3.5	2.2	4.6	3.5	3.5	3.6	2.6	3.2	5.0	6.0	5.0	4.0	1.0	3.4	2.4	34.80	A
PUN-07	Pta. Arenas	4.0	3.0	4.5	3.2	3.5	2.0	2.5	3.5	4.0	5.5	5.0	6.0	5.0	4.0	1.0	5.0	5.5	35.27	A
PUN-27	Pta. Arenas	4.0	2.5	5.0	1.8	3.5	2.0	3.0	3.5	2.5	4.5	5.0	6.0	5.0	4.0	1.0	3.0	2.5	36.22	A
PUN-08	Pta. Arenas	4.0	3.2	5.2	2.0	2.8	2.0	2.8	4.5	4.0	3.5	5.0	6.0	5.0	4.0	1.0	5.5	5.5	39.54	A
PUN-58	Pta. Arenas	4.0	3.2	3.4	2.8	2.2	3.4	5.0	3.0	2.0	3.5	5.0	6.0	5.0	4.0	1.0	3.5	2.5	44.44	A
PUN-107	Pta. Arenas	4.0	4.3	7.0	3.0	1.5	2.5	3.0	3.0	1.5	4.5	5.0	6.0	5.0	4.0	1.0	2.4	2.3	44.77	A

specify the information related to the set of 17 input variables and the functional service life (output variable) of the 158 heritage structures observed. The infrastructures inspected are stated as Heritage Building Construction (HBC) by the Communal Regulatory Plan, MINVU [35]. Analysis of the 57 case studies located in the city of Arica shows the following details: 3.5% (two case studies) achieved the end of their functional service life—Condition C; 24.6% of the buildings (14 cases) have reached the medium level (i.e., Condition B), which entails medium-level considerations regarding vulnerabilities and external hazards, where both costs and benefits are evaluated and harmonized. The remaining 71.9% of the sample (41 cases) are ranked in the upper functional service life condition, in which vulnerabilities and risks are considered as reduced, not requiring any type of intervention (Table 6 and Figure 4). Moreover, the mean value of the index FBSL_2.0 was 35.43, with a standard deviation of 7.1294. In this sense, the minimum value of FBSL_2.0 was 19.38 points and the maximum 44.85 points.

In relation to the sample located in Punta Arenas (Magallanes region), six case studies (5.9%) are in Condition C, the lowest possible classification, in which vulnerabilities and risks are regarded as significantly dangerous to the structures, and building inspections and possible interventions in the short term are recommended. A total of 71 of the cases studied (71.3% of the sample) were ranked in Condition B (medium level) and 23 cases (22.8%) in Condition A (upper level) (Table 7; Figure 5). Furthermore, the FBSL_2.0 minimum, mean and maximum values were 15.69 points, 26.62 points (standard deviation of 5.09) and 44.77 points, respectively.

3.2. Building Resilience Under Climate Change Conditions and for a Near Future Period (2020–2044)

In terms of building resilience under climate change conditions in a near future period (2020–2044), very small variations between the current situation and the predicted period were observed (

Table 8. Average annual temperature (°C) and average annual precipitation (mm) (2020–2044) for the locations under study.

Ids	Location	Average Annual Temperature (°C)			Average Annual Precipitation (mm)
		1985–2005	2020–2044		1985–2005	2020–2044
		av. (st.dev.)	RCP2.6 av. (st.dev.)	RCP8.5 av. (st.dev.)	av. (st.dev.)	RCP2.6 av. (st.dev.)	RCP8.5 av. (st.dev.)
ARI	Arica	19.1 (0.01)	20.3 (0.45)	20.5 (0.29)	2.0 (0.03)	1.84 (0.17)	1.88 (0.12)
PUN	Punta Arenas	6.2 (0.01)	6.8 (0.07)	6.9 (0.06)	421.6 (2.2)	425.5 (4.5)	427.3 (4.2)

). In the case of Arica, the 57 buildings analyzed did not present any significant variation in terms of their functional performance in the RCP2.6 and RCP8.5 scenarios. The results can be justified by the low rainfall in the city and its low effect on service life. In future, more analysis of this situation will be required in order to better understand this phenomenon [46].

Considering the cases located in the extreme south of Chile, the atmospheric variables were remodeled for the 101 cases studied. Regarding this, in a previous study, the atmospheric input variables were detailed correlated with real data obtained from average annual precipitation (mm) and average annual temperature (°C) in Chile [47,48]. Currently, the FIS model proposed is not able to allow for unpredictable extreme climatic events [33].

For the scenarios RCP8.5 (pessimistic) and RCP2.6 (optimistic) for the period 2020–2044, only atmospheric input variables (r₁₂ and r₁₃) were re-evaluated. The remaining inputs were assigned based on the previous in situ visual inspection, and they were kept unchanged in the analysis of the two climate change scenarios. Considering the scenario RCP2.6 for the period 2022–2044 (near future), the FIS model (FBSL_2.0) was unable to identify any variations in the functional service life predictions for the sample analyzed (Figure 6).

Regarding the pessimistic scenario RCP8.5 (2022–2044), the fuzzy model can perceive variations in the functional service life of heritage buildings, before and after climate change conditions. In this scenario, the variables valuation (r₁₂ and r₁₃) is based on the correlation previously established between climate change predictions [30,33] and the annual average temperature and precipitation predicted in each location analyzed. The association between the atmospheric parameters was established as follows:

• Arica: (i) the average annual temperature (°C) will be 20.5 °C, corresponding to 1.0 points in the input variable r₁₃ (temperature); (ii) the average annual precipitation (mm) will be 1.9 mm, corresponding to 1.0 points in the input variable r₁₂ (precipitation) (

  Table 9. Functional service life of the 57 buildings in the city of Arica, before and after climate change prediction (RCP8.5) for the period (2020–2044).

  Ids	Functional Service Life
  	1985–2005		2020–2044
  	r12 = 1.0/r13 = 1.0	Condition	r12 = 1.0/r13 = 1.0	Condition	Δ FBSL2.0
  ARI-27	19.38	C	19.38	C	0
  ARI-19	19.40	C	19.40	C	0
  ARI-28	22.84	B	22.84	B	0
  ARI-52	23.56	B	23.56	B	0
  ARI-50	24.40	B	24.40	B	0
  ARI-43	24.90	B	24.90	B	0
  ARI-35	26.30	B	26.30	B	0
  ARI-39	26.62	B	26.62	B	0
  ARI-38	26.64	B	26.64	B	0
  ARI-29	26.74	B	26.74	B	0
  ARI-44	26.97	B	26.97	B	0
  ARI-36	28.17	B	28.17	B	0
  ARI-22	28.78	B	28.78	B	0
  ARI-37	28.79	B	28.79	B	0
  ARI-45	29.13	B	29.13	B	0
  ARI-33	29.42	B	29.42	B	0
  ARI-51	30.81	A	30.81	A	0
  ARI-40	31.07	A	31.07	A	0
  ARI-47	31.21	A	31.21	A	0
  ARI-30	31.89	A	31.89	A	0
  ARI-49	33.52	A	33.52	A	0
  ARI-24	33.53	A	33.53	A	0
  ARI-31	34.01	A	34.01	A	0
  ARI-16	35.36	A	35.36	A	0
  ARI-26	35.42	A	35.42	A	0
  ARI-23	35.97	A	35.97	A	0
  ARI-41	36.77	A	36.77	A	0
  ARI-55	37.89	A	37.89	A	0
  ARI-11	38.13	A	38.13	A	0
  ARI-07	39.49	A	39.49	A	0
  ARI-56	39.61	A	39.61	A	0
  ARI-13	39.65	A	39.65	A	0
  ARI-53	39.69	A	39.69	A	0
  ARI-34	39.70	A	39.70	A	0
  ARI-10	39.78	A	39.78	A	0
  ARI-09	39.95	A	39.95	A	0
  ARI-54	39.97	A	39.97	A	0
  ARI-42	39.99	A	39.99	A	0
  ARI-57	40.12	A	40.12	A	0
  ARI-58	40.61	A	40.61	A	0
  ARI-05	40.77	A	40.77	A	0
  ARI-21	40.87	A	40.87	A	0
  ARI-06	40.92	A	40.92	A	0
  ARI-04	41.46	A	41.46	A	0
  ARI-12	41.55	A	41.55	A	0
  ARI-32	41.82	A	41.82	A	0
  ARI-03	41.99	A	41.99	A	0
  ARI-46	42.10	A	42.10	A	0
  ARI-18	42.51	A	42.51	A	0
  ARI-20	42.57	A	42.57	A	0
  ARI-01	42.61	A	42.61	A	0
  ARI-15	42.87	A	42.87	A	0
  ARI-17	42.94	A	42.94	A	0
  ARI-02	43.59	A	43.59	A	0
  ARI-61	44.70	A	44.70	A	0
  ARI-14	44.84	A	44.84	A	0
  ARI-08	44.85	A	44.85	A	0

  and Figure 6).
• Punta Arenas: (i) the average annual temperature (°C) will be 6.9 °C, corresponding to 7.1 points in the input variable r₁₃ (temperature); (ii) the average annual precipitation (mm) will be 427.3 mm, corresponding to 3.2 points in the input variable r₁₂ (precipitation) (

  Table 10. Functional service life of the 101 buildings in the city of Punta Arenas, before and after climate change prediction (RCP8.5) for the period (2020–2044).

  Ids	Functional Service Life
  	1985–2005		2020–2044
  	r12 = 3.2/r13 = 7.4	Condition	r12 = 3.2/r13 = 7.1	Condition	Δ FBSL2.0
  PUN-33	15.69	C	15.46	C	−0.23
  PUN-100	18.05	C	18.04	C	−0.01
  PUN-70	18.90	C	18.99	C	0.09
  PUN-13	19.23	C	19.37	C	0.14
  PUN-102	19.38	C	19.54	C	0.16
  PUN-55	19.64	C	19.85	C	0.21
  PUN-110	20.20	B	20.49	B	0.29
  PUN-66	20.52	B	20.82	B	0.30
  PUN-54	20.95	B	21.29	B	0.34
  PUN-99	20.99	B	21.36	B	0.37
  PUN-103	21.08	B	21.45	B	0.37
  PUN-42	21.40	B	21.76	B	0.36
  PUN-78	21.87	B	22.33	B	0.46
  PUN-18	22.01	B	22.48	B	0.47
  PUN-14	22.05	B	22.51	B	0.46
  PUN-15	22.07	B	22.53	B	0.46
  PUN-60	22.29	B	22.76	B	0.47
  PUN-72	22.33	B	22.79	B	0.46
  PUN-86	22.34	B	22.86	B	0.52
  PUN-75	22.43	B	22.97	B	0.54
  PUN-73	22.47	B	22.96	B	0.49
  PUN-57	22.53	B	23.02	B	0.49
  PUN-94	22.53	B	22.53	B	0.00
  PUN-64	22.74	B	23.22	B	0.48
  PUN-41	22.77	B	23.31	B	0.54
  PUN-44	22.89	B	23.41	B	0.52
  PUN-62	22.97	B	23.47	B	0.50
  PUN-115	23.09	B	23.68	B	0.59
  PUN-77	23.34	B	23.85	B	0.51
  PUN-45	23.41	B	23.95	B	0.54
  PUN-105	23.45	B	24.05	B	0.60
  PUN-91	23.48	B	24.06	B	0.58
  PUN-12	23.67	B	24.26	B	0.59
  PUN-90	23.67	B	24.29	B	0.62
  PUN-47	23.70	B	24.23	B	0.53
  PUN-82	23.90	B	24.54	B	0.64
  PUN-101	24.05	B	24.62	B	0.57
  PUN-03	24.13	B	24.74	B	0.61
  PUN-35	24.19	B	24.76	B	0.57
  PUN-69	24.37	B	24.96	B	0.59
  PUN-34	24.62	B	25.23	B	0.61
  PUN-92	24.62	B	25.31	B	0.69
  PUN-98	25.11	B	25.83	B	0.72
  PUN-83	25.14	B	25.76	B	0.62
  PUN-114	25.36	B	26.10	B	0.74
  PUN-113	25.57	B	25.67	B	0.10
  PUN-89	25.74	B	26.38	B	0.64
  PUN-95	25.97	B	26.62	B	0.65
  PUN-68	26.05	B	26.78	B	0.73
  PUN-96	26.13	B	26.85	B	0.72
  PUN-71	26.22	B	26.98	B	0.76
  PUN-46	26.55	B	27.35	B	0.80
  PUN-17	26.67	B	27.35	B	0.68
  PUN-84	26.67	B	27.46	B	0.79
  PUN-85	26.67	B	27.46	B	0.79
  PUN-108	26.85	B	27.59	B	0.74
  PUN-63	27.17	B	27.93	B	0.76
  PUN-112	27.18	B	27.92	B	0.74
  PUN-67	27.22	B	27.99	B	0.77
  PUN-51	27.3	B	28.07	B	0.77
  PUN-19	27.43	B	28.22	B	0.79
  PUN-21	27.46	B	28.25	B	0.79
  PUN-79	27.57	B	28.34	B	0.77
  PUN-106	27.89	B	28.69	B	0.80
  PUN-31	27.98	B	28.79	B	0.81
  PUN-06	28.00	B	28.83	B	0.83
  PUN-88	28.19	B	29.05	B	0.86
  PUN-38	28.28	B	28.70	B	0.42
  PUN-61	28.28	B	28.94	B	0.66
  PUN-97	28.28	B	23.86	B	-4.42
  PUN-02	28.60	B	29.44	B	0.84
  PUN-36	28.68	B	29.45	B	0.77
  PUN-109	28.72	B	29.56	B	0.84
  PUN-76	28.74	B	29.54	B	0.80
  PUN-20	28.86	B	29.71	B	0.85
  PUN-49	28.98	B	29.85	B	0.87
  PUN-26	29.22	B	30.10	A	0.88
  PUN-93	29.49	B	30.33	A	0.84
  PUN-24	30.02	A	30.91	A	0.89
  PUN-111	30.18	A	31.02	A	0.84
  PUN-116	30.36	A	31.24	A	0.88
  PUN-30	30.52	A	31.41	A	0.89
  PUN-52	30.54	A	31.43	A	0.89
  PUN-74	30.78	A	31.68	A	0.90
  PUN-40	30.79	A	31.67	A	0.88
  PUN-81	31.55	A	32.43	A	0.88
  PUN-104	31.61	A	32.50	A	0.89
  PUN-29	31.62	A	32.51	A	0.89
  PUN-39	31.67	A	32.57	A	0.90
  PUN-80	31.80	A	32.60	A	0.80
  PUN-25	33.05	A	33.92	A	0.87
  PUN-28	33.18	A	34.05	A	0.87
  PUN-32	33.23	A	34.09	A	0.86
  PUN-16	34.24	A	35.06	A	0.82
  PUN-65	34.70	A	35.51	A	0.81
  PUN-48	34.80	A	35.59	A	0.79
  PUN-07	35.27	A	36.05	A	0.78
  PUN-27	36.22	A	37.13	A	0.91
  PUN-08	39.54	A	39.37	A	-0.17
  PUN-58	44.44	A	44.83	A	0.39
  PUN-107	44.77	A	45.67	A	0.90

  and Figure 6).

After a detailed analysis of the case studies in Arica, no changes were detected considering the functional performance of the set of buildings under analysis. The limitation in sensitivity observed within the model hinders its ability to discern variations when only two variables, specifically rainfall (r₁₂) and temperature (r₁₃), undergo minor adjustments (Table 9).

Concerning the sample in Punta Arenas, in the RCP8.5 scenario (2020–2044), several differences could be detected. On the one hand, the maximum variation of the functional service life was achieved in PUN-27, which increased by 0.91 points. On the other hand, the minimum variation is obtained for building PUN-97, which saw a functional level decrease of 4.42 points. An average value of 0.57 points (with a standard deviation of 0.56) (Table 10) was obtained for the 101 buildings analyzed.

3.3. Building Resilience Under Climate Change Conditions and for a Distant Future Period (2045–2069)

Analyzing the data forecasted and obtained from CCRR (2019) for the 2045–2069 period, the average annual temperature will see a minimum increase of 0.7 °C from the current weather data (1985–2005) in the optimistic predictions (RCP2.6) and a maximum increase of 2.5 °C in the pessimistic predictions (RCP8.5) (

Table 11. Average annual temperature (°C) and average annual precipitation (mm) concerning the locations under study (2045–2069).

Ids	Location	Average Annual Temperature (°C)			Average Annual Precipitation (mm)
		1985–2005	2045–2069		1985–2005	2045–2069
		av. (st.dev.)	RCP2.6 av. (st.dev.)	RCP8.5 av. (st.dev.)	av. (st.dev.)	RCP2.6 av. (st.dev.)	RCP8.5 av. (st.dev.)
ARI	Arica	19.1 (0.01)	20.5 (0.43)	21.6 (0.17)	2.0 (0.03)	1.87 (0.15)	1.93 (0.14)
PUN	Punta Arenas	6.2 (0.01)	6.9 (0.10)	7.6 (0.08)	421.6 (2.2)	430.0 (4.2)	430.9 (5.0)

). Regarding precipitation, Arica shows a very slight minimization of the maximum of annual average precipitation from the current situation to the optimistic predictions (RCP2.6), going from 2.0 mm in the period 1985–2005 to 1.87 mm. Analyzing the pessimistic scenario (RCP8.5), for the period (2045–2069), Arica also presents a slight decrease (0.07 mm) in annual average precipitation, less than the current weather data registered. However, Punta Arenas presents a clear increase of precipitation (9.3 mm) when compared with the data registered in the period 1984–2005 and the RCP8.5 predictions for 2045–2069. The increase of temperature and the decrease of rainfall in the two locations under analysis for the period 2045–2069 is detailed in Table 11.

For the scenarios RCP2.6 and RCP8.5 for the period 2045–2069, only inputs r₁₂ and r₁₃ were re-evaluated. The remaining parameters were established based on the previous in situ visual inspection, and they were kept unchanged in the analysis of the two climate change scenarios. In terms of the analysis focused on the RCP2.6 situation for the period 2044–2069 (near future), the FIS model (FBSL_2.0) presented no significant changes in relation to the previous modelled scenario RCP8.5 (2020–2044). However, considering the pessimistic scenario RCP8.5 (2045–2069), the FIS system could perceive functional service life variations before and after climate change in the case studies in the city of Punta Arenas [33]. Considering the sample in Arica, they do not present any changes in the inputs’ valuation.

• Arica: (i) the average annual temperature (°C) will be 21.6 °C, corresponding to 1.0 points in the input variable r₁₃ (temperature); (ii) the average annual precipitation (mm) will be 1.9 mm, corresponding to 1.0 points in the input variable r₁₂ (precipitation) (Figure 6).
• Punta Arenas: (i) the average annual temperature (°C) will be 7.6 °C, corresponding to 6.7 points in the input variable r₁₃ (temperature); (ii) the average annual precipitation (mm) will be 430.9 mm, corresponding to 3.3 points in the input variable r₁₂ (precipitation) (

  Table 12. Functional service life of the 101 buildings in the city of Punta Arenas, before and after climate change prediction (RCP8.5) for the period (2045–2069).

  Ids	Functional Service Life
  	1985–2005		2045–2069
  	r12 = 3.2/r13 = 7.4	Condition	r12 = 3.3/r13 = 6.7	Condition	Δ FBSL2.0
  PUN-33	15.69	C	14.98	C	−0.71
  PUN-100	18.05	C	18.01	C	−0.04
  PUN-70	18.90	C	19.20	C	0.30
  PUN-13	19.23	C	19.66	C	0.43
  PUN-102	19.38	C	19.88	C	0.50
  PUN-55	19.64	C	20.25	B	0.61
  PUN-110	20.20	B	21.09	B	0.89
  PUN-66	20.52	B	21.43	B	0.91
  PUN-54	20.95	B	21.96	B	1.01
  PUN-99	20.99	B	22.15	B	1.16
  PUN-103	21.08	B	22.18	B	1.10
  PUN-42	21.40	B	22.45	B	1.05
  PUN-78	21.87	B	23.24	B	1.37
  PUN-18	22.01	B	23.39	B	1.38
  PUN-14	22.05	B	23.42	B	1.37
  PUN-15	22.07	B	23.42	B	1.35
  PUN-60	22.29	B	23.67	B	1.38
  PUN-72	22.33	B	23.68	B	1.35
  PUN-86	22.34	B	23.91	B	1.57
  PUN-75	22.43	B	24.03	B	1.60
  PUN-73	22.47	B	23.90	B	1.43
  PUN-57	22.53	B	23.97	B	1.44
  PUN-94	22.53	B	23.28	B	0.75
  PUN-64	22.74	B	24.12	B	1.38
  PUN-41	22.77	B	24.36	B	1.59
  PUN-44	22.89	B	24.39	B	1.50
  PUN-62	22.97	B	24.41	B	1.44
  PUN-115	23.09	B	24.84	B	1.75
  PUN-77	23.34	B	24.81	B	1.47
  PUN-45	23.41	B	24.98	B	1.57
  PUN-105	23.45	B	25.20	B	1.75
  PUN-91	23.48	B	25.17	B	1.69
  PUN-12	23.67	B	25.38	B	1.71
  PUN-90	23.67	B	25.49	B	1.82
  PUN-47	23.70	B	25.21	B	1.51
  PUN-82	23.90	B	24.91	B	1.01
  PUN-101	24.05	B	25.67	B	1.62
  PUN-03	24.13	B	25.89	B	1.76
  PUN-35	24.19	B	25.80	B	1.61
  PUN-69	24.37	B	26.03	B	1.66
  PUN-34	24.62	B	26.35	B	1.73
  PUN-92	24.62	B	26.63	B	2.01
  PUN-98	25.11	B	27.21	B	2.10
  PUN-83	25.14	B	26.88	B	1.74
  PUN-114	25.36	B	27.48	B	2.12
  PUN-113	25.57	B	27.04	B	1.47
  PUN-89	25.74	B	27.52	B	1.78
  PUN-95	25.97	B	27.79	B	1.82
  PUN-68	26.05	B	28.09	B	2.04
  PUN-96	26.13	B	28.15	B	2.02
  PUN-71	26.22	B	28.09	B	1.87
  PUN-46	26.55	B	28.43	B	1.88
  PUN-17	26.67	B	28.58	B	1.91
  PUN-84	26.67	B	28.55	B	1.88
  PUN-85	26.67	B	28.55	B	1.88
  PUN-108	26.85	B	28.78	B	1.93
  PUN-63	27.17	B	28.95	B	1.78
  PUN-112	27.18	B	29.06	B	1.88
  PUN-67	27.22	B	28.95	B	1.73
  PUN-51	27.30	B	28.99	B	1.69
  PUN-19	27.43	B	29.17	B	1.74
  PUN-21	27.46	B	29.21	B	1.75
  PUN-79	27.57	B	29.29	B	1.72
  PUN-106	27.89	B	29.65	B	1.76
  PUN-31	27.98	B	29.76	B	1.78
  PUN-06	28,00	B	29.82	B	1.82
  PUN-88	28.19	B	30.08	A	1.89
  PUN-38	28.28	B	30.38	A	2.10
  PUN-61	28.28	B	30.20	A	1.92
  PUN-97	28.28	B	24.99	A	-3.29
  PUN-02	28.60	B	30.45	A	1.85
  PUN-36	28.68	B	30.81	A	2.13
  PUN-109	28.72	B	30.58	A	1.86
  PUN-76	28.74	B	30.51	A	1.77
  PUN-20	28.86	B	30.74	A	1.88
  PUN-49	28.98	B	30.90	A	1.92
  PUN-26	29.22	B	31.16	A	1.94
  PUN-93	29.49	B	31.36	A	1.87
  PUN-24	30.02	B	31.99	A	1.97
  PUN-111	30.18	A	32.06	A	1.88
  PUN-116	30.36	A	32.33	A	1.97
  PUN-30	30.52	A	32.52	A	2.00
  PUN-52	30.54	A	32.53	A	1.99
  PUN-74	30.78	A	32.78	A	2.00
  PUN-40	30.79	A	32.76	A	1.97
  PUN-81	31.55	A	33.52	A	1.97
  PUN-104	31.61	A	33.60	A	1.99
  PUN-29	31.62	A	33.62	A	2.00
  PUN-39	31.67	A	33.68	A	2.01
  PUN-80	31.80	A	33.59	A	1.79
  PUN-25	33.05	A	35.01	A	1.96
  PUN-28	33.18	A	35.13	A	1.95
  PUN-32	33.23	A	35.18	A	1.95
  PUN-16	34.24	A	36.11	A	1.87
  PUN-65	34.70	A	36.53	A	1.83
  PUN-48	34.80	A	36.61	A	1.81
  PUN-07	35.27	A	37.04	A	1.77
  PUN-27	36.22	A	38.09	A	1.87
  PUN-08	39.54	A	39.68	A	0.14
  PUN-58	44.44	A	45.08	A	0.64
  PUN-107	44.77	A	46.78	A	2.01

  and Figure 6).

Concerning the sample in Punta Arenas in the RCP8.5 scenario (2045–2069), several differences could be detected. Only three case studies lowered their functional performance. The maximum variation of the functional service life was achieved in PUN-36, which increased by 2.13 points, and the minimum variation was also obtained for building PUN-97, which saw its functional level decrease by 3.29 points. An average value of 1.55 points (with a standard deviation of 0.7) (Table 12) was obtained for the set of 101 buildings examined in the extreme south of Chile (Figure 6).

Considering the above details of the functional classification of the 158 case studies analyzed between north and south Chile (Figure 6), we can observe the following: (i) Arica maintains a stable distribution over time, with 72% of cases in good condition, 25% in intermediate condition, and only 3% in poor condition; (ii) Punta Arenas, on the other hand, shows a positive trend: the proportion of buildings in good condition increases from 23% in the recent past to 35% in the distant future, while the intermediate condition slightly decreases from 71% to 60%. The proportion in poor condition remains low (from 6% to 5%).

In this sense, and regarding a detailed analysis of the case studies in Punta Arenas after climate change prediction (RCP8.5) for the period (2045–2069) (Figure 6), five case studies (5.0%) were positioned in Condition C. This classification represents the lowest level, indicating significant vulnerabilities and risks posed to the structures, necessitating prompt building inspection and potential intervention in the near future. The characterization of these cases was as follows: (i) legal status: 100.0% private; (ii) foundations: 100.0% concrete; (iii) structure: 40.0% timber-framed, 40.0% masonry and 20.0% timber-framed masonry; (iv) cover: 100.0% zinc-aluminum; (v) façade: 60.0% stucco; 20.0% metal plate; 20.0% stucco and metal plate.

A set of 61 cases studies (60.4% of the sample) were ranked in Condition B (medium level). Their main characterization was as follows: (i) legal status: 85.2% private and 14.8% public; (ii) foundations: 100.0% concrete; (iii) structure: 77.0% masonry, 21.4% timber and 1.6% timber-framed masonry; (iv) cover: 100.0% zinc-aluminum; (v) façade: 80.3% stucco; 16.4% metal plate; 3.3% timber.

Finally, 35 cases (34.7%) were ranked in Condition A (upper level), and they were classified as follows: (i) legal status: 77.1% private and 22.9% public; (ii) foundations: 100.0% concrete; (iii) structure: 77.1% masonry, 20.1% timber and 2.8% timber-framed masonry; (iv) cover: 100.0% zinc-aluminum; (v) façade: 74.3% stucco; 17.3% metal plate, 2.8% metal plate and stucco, 2.8% timber and 2.8% masonry.

Technical and environmental factors are critical to understanding the resilience of heritage buildings; in addition, socioeconomic dimensions play an equally vital role in determining the feasibility and success of adaptation strategies. In the Chilean context, limited financial resources, fragmented policy frameworks, and varying levels of institutional capacity significantly influence the conservation outcomes of modern heritage buildings, particularly in peripheral and southern regions.

Government housing and heritage policies, such as those issued by the Ministry of Housing and Urban Planning (MINVU), provide some guidelines for the preservation of historic buildings. However, in practice, financial support for maintenance and adaptation is often limited or inconsistently distributed, especially outside major metropolitan centers. Survey results indicated that most property owners of heritage buildings do not receive direct state assistance for maintenance efforts, increasing the vulnerability of these structures to gradual deterioration under changing climatic conditions.

Local funding mechanisms for conservation are generally scarce and rely heavily on municipal budgets, which are often constrained and prioritized towards urgent infrastructure needs. Moreover, administrative fragmentation between cultural heritage agencies and urban development authorities can delay or hinder effective intervention planning. In this context, institutional capacity—both in terms of technical expertise and resource allocation—emerges as a critical determinant of building resilience.

The feasibility of implementing proactive adaptation strategies, such as preventive maintenance or material retrofitting, is therefore closely tied to the availability of funding programs, tax incentives, and community-based initiatives. Without addressing these socioeconomic barriers, resilience strategies risk remaining theoretical rather than actionable. Strengthening public policies that recognize the cultural and economic value of modern heritage, coupled with targeted financial support mechanisms, will be essential to ensure the long-term functional service life and resilience of these buildings amid accelerating climate change impacts.

In this sense, this kind of study can help in a ‘time–space’ conceptualization, intending to show how climate change impacts can influence the resilience of buildings and future degradation of heritage buildings in the context of South America, particularly in Chile.

4. Conclusions and Future Research Directions

This study assessed the impacts of climate change on the functional service life of 158 heritage buildings located in northern and southern Chile. Although the fuzzy inference system (FIS) model was originally developed for prioritizing maintenance interventions in religious buildings in Seville, it was adapted here to evaluate functional performance under changing climatic conditions in Chile [4]. While not initially designed to measure climate impacts, the model was successfully modified to support this purpose.

The results suggest that, in the near future (2020–2044), climate change will not significantly affect the functional performance of the studied heritage buildings. Only minor variations were detected in Punta Arenas under the most pessimistic RCP8.5 scenario, reflecting the gradual progression of climatic changes. However, projections for the distant future (2045–2069) under RCP8.5 indicate greater vulnerability, particularly in Punta Arenas.

In this scenario, 5% of the buildings were classified under Condition C, requiring urgent inspection and potential intervention. These cases primarily involved privately owned structures with concrete foundations and timber-framed or masonry compositions. A majority (60.4%) of buildings were ranked under Condition B, indicating moderate vulnerability, while 34.7% were ranked in Condition A, suggesting a relatively resilient state. These findings highlight significant variability in the resilience of heritage buildings in southern Chile.

Although climate change is often perceived as a threat to building performance, this study shows that heritage buildings in southern Chile may be relatively well adapted to anticipated conditions. The traditional construction techniques employed—particularly masonry structures with zinc-aluminum coverings—demonstrate a strong resilience to climatic stressors such as temperature increases and reduced precipitation. These adaptations contrast with more vulnerable techniques like brick or stone masonry commonly affected by seismic activity in Chile.

Overall, projected climatic changes in southern Chile, including rising temperatures and declining precipitation, may have a limited adverse impact on the functionality of these heritage buildings. In fact, reduced moisture exposure could even enhance their durability by minimizing water-related defects.

This research provides valuable insights for the development of preventive maintenance strategies and climate-adaptation guidelines. The adapted FIS model offers a practical tool for prioritizing interventions across different regions, supporting stakeholders—including property owners, policymakers, and heritage organizations—in preserving cultural heritage assets amidst evolving climate challenges.