General Article

International Journal of Sustainable Building Technology and Urban Development. 30 June 2024. 122-139
https://doi.org/10.22712/susb.20240011

ABSTRACT


MAIN

  • Introduction

  • Literature review

  •   Reconstruction of old-buildings

  •   Project complexity management

  •   The complexity of old-building reconstruction projects

  • Framework for complexity measurement

  •   The necessity of developing a framework for complexity measurement

  •   Fuzzy synthesis evaluation for complexity measurement

  •   Development of a framework for complexity measurement

  • Case study of Vietnamese old-building reconstruction project

  •   Establish a list of complexity variables

  •   Formulate the complexity weights of complexity variables/groups

  •   Calculate the degree of complexity of the old-building reconstruction project

  • Results and Discussions

  • Conclusions

Introduction

Urbanization and the consequent increase in population density have resulted in a growing need for high-rise buildings in urban cities [1]. These buildings provide an efficient solution for housing many people in these areas. However, these buildings may become old and worn out over time, requiring maintenance, repair, and even complete reconstruction [2]. The seriously deteriorating old apartment buildings are prone to dangerous incidents that threaten the health and safety of residents and neighboring households [3] and urban planning [4]. Additionally, the scarcity of land within urban areas and issues about land use rights necessitate demolition and subsequent reconstruction of old buildings [5]. Despite the necessity of old-building reconstruction projects, they are often fraught with challenges that can make them time-consuming and difficult to complete [6].

One factor that explains this issue is the complexity of these projects [7]. Reconstructing old buildings is a multifaceted undertaking involving various intricate procedures, including obtaining government consent, securing community consensus, following legal guidelines, compensating and clearing the site, demolishing the existing structure, rebuilding, and ensuring compliance with urban planning standards [8]. The intricacy is additionally exacerbated by the divergent interests of multiple stakeholders [9]. Investors endeavor to optimize their financial gains by limiting site pay and clearance expenses, whereas locals frequently advocate for more substantial compensation packages. Furthermore, the imperative of complying with urban planning standards, such as limitations on building heights, might present supplementary obstacles, as they must be harmonized with the profit-oriented goals of investors. In conclusion, the intricate procedures and the divergent interests among various stakeholders highlight the delicate nature of renovating old buildings, necessitating meticulous planning and proficient project management to ensure effective implementation [10].

The complexity is a critical aspect of the reconstruction of building projects [7, 11, 12]. Comprehending complexity allows project stakeholders to navigate the inherent complexities of old building reconstruction projects [13]. This understanding facilitates comprehensive planning, adaptive decision-making, and stakeholder collaboration and increases the probability of project success [14]. By embracing complexity theory, stakeholders gain access to the tools and frameworks to navigate the intricate nature of these projects successfully, make informed decisions, foster cooperation among stakeholders, and ultimately achieve desired outcomes that align with their priorities [10]. However, most previous studies emphasized the technical aspects of reconstruction buildings [15, 16, 17, 18]. Further investigation is warranted to explore the complexities associated with restoring old structures.

Although previous studies contributed to the general understanding of old building reconstruction projects, these efforts have yet to prove sufficient in furnishing a comprehensive knowledge of the complexities inherent in such projects [15, 17]. The stakeholders in the project recognize the presence of complexity, but they need a precise understanding of the degree or magnitude of the project’s complexity. Thus, they will need more information to propose proper complexity management strategies [19]. Consequently, conducting a quantitative inquiry into complexity measurement is essential.

This research aims to introduce a conceptual model for evaluating the complexity of old building reconstruction projects. Specifically, a measurement model utilizing the Fuzzy Synthesis Evaluation (FSE) technique has been developed to gauge the complexity of such projects. To demonstrate the efficacy of this framework, it was applied in the context of an old-building reconstruction project in a developing country (i.e., Vietnam).

Literature review

Reconstruction of old-buildings

As buildings age, two primary options typically arise renovation or reconstruction [12]. While previous studies have primarily focused on renovation strategies for aging buildings [12, 20, 21, 22], Nguyen et al. [8] identified a gap in the literature concerning the reconstruction of old buildings. Rennovation approaches have provided valuable knowledge to practitioners and have been widely studied [22]. However, there are instances where renovations cannot effectively address the challenges and complexities associated with old buildings. In such cases, demolishing and reconstructing the structure is the only viable option [8]. This study aims to bridge the existing gap in research by shifting the focus towards reconstructing old buildings. By exploring this aspect, the study seeks to contribute to understanding the reconstruction approach. The findings of this research will provide insights and guidance to practitioners and decision-makers who are faced with dealing with aging buildings that require reconstruction rather than renovation.

Reconstruction projects involving old buildings exhibit distinct traits and obstacles, rendering them a subject of considerable academic interest. To begin with, these projects encompass a series of different phases, encompassing the compensation of sites, the clearance of areas, the demolition of structures, and the subsequent reconstruction of residential buildings [5, 23]. This type of project aims to address the deteriorated condition of such buildings by replacing them with new and safer structures [24, 25]. This complex process requires careful evaluation of old buildings’ condition and structural integrity to ensure safety and longevity. Furthermore, old building reconstruction projects face specific regulatory considerations related to urban planning and land-use rights [5]. These projects often impact the residents living in the buildings that need to be demolished, requiring careful management and consideration of their needs and rights. Additionally, these projects may be subject to stricter guidelines and regulations than new construction projects due to their historical and social significance [26].

Moreover, the involvement of diverse stakeholders sets old building reconstruction projects apart. Local communities, historic preservation organizations, government agencies, and architectural experts are among the stakeholders with varying priorities and interests [26]. Balancing these competing demands presents a complex challenge requiring effective stakeholder management and collaboration. Studying old building reconstruction projects is worthwhile as it offers insights into addressing social and urban planning issues and managing the needs and expectations of various stakeholders [27]. These projects’ unique complexities and considerations provide valuable lessons for sustainable urban development and effective project management in similar contexts [12].

Implementing old-building reconstruction projects is becoming increasingly important in many countries, especially urban cities [12]. While existing old buildings can quickly solve the pressing need for housing in urban areas, they often require significant reconstruction to meet modern safety and living standards. Botta [28] witnessed that industrialized Western countries had a more substantial number of existing buildings requiring maintenance and reconstruction than buildings that needed to be constructed. Kim et al. [29] acknowledged the old-building reconstruction project aims to eliminate dilapidated apartment buildings and build new ones in their place.

Many countries have endeavored to implement the reconstruction of old buildings. The Chinese government implemented the “Urban Renewal Project,” a nationwide program to upgrade and improve housing stock in urban areas [30]. The project has resulted in the extensive demolition of existing buildings. The total area of demolished urban buildings during the 11th National Five-Year Plan period (2006-2010) reached 3 billion m2, with the ratio of demolished buildings to newly constructed buildings approaching one-quarter, or 23%. During the 12th Five-Year Plan period, approximately 130 million m2 of residential buildings were destroyed in 2011 alone. By 2013, the total floor area of housing demolished had increased dramatically to a staggering 400 million m2 [27].

Furthermore, there has been a growing need for urban regeneration in South Korea to enhance the condition of aging apartment complexes that have deteriorated since the 1990s. In South Korea, it is permissible for apartment buildings older than 20 years exhibiting safety concerns to undergo redevelopment or reconstruction [29]. Consequently, there has been a notable surge in the restoration of old buildings in South Korea [31].

One primary advantage of implementing old-building reconstruction projects is enhancing safety conditions for individuals within these structures [32]. Insufficient maintenance of old buildings over an extended period can present substantial hazards to the well-being and security of the individuals living within them. The properties in question may exhibit structural deficiencies, antiquated electrical and plumbing infrastructures, and other perils that may lead to incidents or bodily harm [33]. Reconstruction initiatives have the potential to mitigate these risks by replacing hazardous structures with new buildings that incorporate contemporary, secure designs and materials [27]. Reconstruction initiatives using old buildings can contribute to the promotion of sustainable development in metropolitan areas. Developers have the potential to mitigate the environmental impact of buildings and foster sustainable development by integrating sustainability elements, such as energy-efficient heating and cooling systems and renewable energy sources [34].

Project complexity management

The analysis of project failure, marked by cost overruns and schedule delays, has been a longstanding topic of study [10]. The underestimation of the increasing complexity of projects is identified as a significant contributing factor to project failure [14]. The construction business is a notable illustration as it contends with the increasing intricacy of projects. The sector is currently under escalating pressures to improve project performance, primarily motivated by the growing need for timely completion, cost-effectiveness, and high-quality results. The industry has problems from both internal reasons, such as organizational changes, and external elements, such as market pressures [13]. The proliferation of uncertainties within initiatives introduces intricacy and augments the probability of surpassing financial limitations and timeline projections [35]. This highlights the need for a comprehensive understanding of the factors contributing to project complexity and associated risks [36]. By addressing and effectively managing project complexity, stakeholders can mitigate the potential for cost overruns and time delays, improving project outcomes [37].

Kim and Nguyen [10] identified a notable absence of a precise and universally accepted definition of project complexity and complexity within complex project environments. Despite the undeniable impact of project complexity on crucial decisions in project management, understanding and assessing complexity often rely on intuitive or past experiential knowledge. Bosch-Rekveldt et al. [36] concurred with this perspective, asserting that complexity refers to examining complex systems, which inherently lack a standardized definition due to their complex nature. Complexity is the difficulty or intricacy of a particular situation or system [10]. Complexity can generally be understood as the number and diversity of components and the interconnectedness and interdependence that make up a system [38]. The more complex a system, the more difficult it is to understand, manage, and control, as there are more factors to consider and more potential for unforeseen interactions and outcomes [39]. Complexity is typical in many real-world situations, including organizational structures and technological, economic, and social systems [40]. Understanding and managing complexity is an essential challenge for many fields, including management, engineering, economics, and social science [36].

The complexity of old-building reconstruction projects

The complexities and difficulties associated with reconstruction projects for old buildings are multifaceted, encompassing a wide range of stakeholders who contribute their distinct interests, goals, and potential conflicts of interest to project implementation [41]. The parties commonly involved in these initiatives include governmental entities, investors, nearby communities, contractors, consulting firms, urban planners, social advocates, and inhabitants [29]. In the course of this investigation into the intricacies of reconstructing old buildings, this section will delve into the multifaceted nature of these undertakings, the conflicts that arise due to conflicting interests, the subtleties of urban planning, the difficulties associated with compensating for site constraints, and the prolonged timelines that frequently challenge the patience of all parties involved [34].

Reconstruction projects involving old buildings exhibit a varied and evolving stakeholder environment [30]. The government, frequently the instigator of such endeavors, demonstrates a strong interest in ensuring that these initiatives adhere to legal statutes, uphold historical conservation efforts, and contribute to urban development [8]. Investors participate in the market with the primary objective of attaining financial gains and brand credibility. Local communities own a significant stake in preserving their ecological surroundings and cultural legacy [31]. Contractors mostly direct their attention towards the technical facets of the construction process. Consulting firms are engaged to provide their specialized knowledge and skills to ensure that a project adheres to industry best practices and fulfills all necessary regulatory obligations. Urban planners are responsible for the preservation of both the visual appeal and operational efficiency of a city. Socialists espouse promoting societal welfare on a larger scale, prioritizing the community’s well-being and the preservation of the environment. Individuals with a strong emotional attachment to their properties greatly value their well-being and the financial compensation they receive for their homes.

The complicated interplay among stakeholders involved in renovating old buildings often leads to a notable characteristic of intricacy: conflicts of interest. These disputes occur on two main dimensions, highlighting the complex issues investors, citizens, and urban planners encounter.

A primary source of friction emerges between investors, whose motivations are frequently driven by financial interests, and inhabitants, whose attachment to their properties and communities drives their aspiration for more compensation. Motivated by a profit-driven agenda, investors consistently aim to cut costs by reducing site compensation and clearing expenses. On the other hand, the residents attach great importance to the value of their properties and seek higher compensation, intensifying tensions in their agreements with investors. The misalignment of objectives amongst stakeholders frequently results in acrimonious disputes and extended project schedules as they need help reconciling their differing agendas. Within the framework of the initial disagreement, the profit-oriented disposition of investors engenders a dichotomy between their interests and those of the inhabitants who reside in these antiquated structures. As financial stakeholders, investors are naturally inclined to minimize expenditures whenever feasible, prioritizing the optimization of site compensation and clearance costs. The primary goal is evident: to optimize profit margins through the minimization of financial spending. On the contrary, those residing in these communities, who possess strong attachments to their surroundings and frequently have familial connections to these assets, argue for reparation corresponding to their emotional and financial commitments.

Another significant friction aspect arises when investors come into contact with urban planners, who are tasked with upholding the integrity of city planning standards, such as limitations on building heights and adherence to architectural norms. Urban planning policies are formulated to safeguard the historical and aesthetic attributes inherent in urban settings. Investors are driven by maximizing their financial gains but are also confronted with the many challenges imposed by regulatory limitations. The execution phase of rebuilding projects becomes more complex due to the need to strike a careful balance between budgetary goals and compliance with city regulations. The complexities of these rules, including limitations on building heights and architectural design, provide significant challenges for investors seeking to optimize financial gains. Reconciling financial objectives with adherence to city requirements is a delicate endeavor, amplifying project implementation’s intricacy. Urban planners are responsible for safeguarding urban environments’ historical and aesthetic authenticity. They implement policies that prioritize the preservation of the city’s unique identity. The interaction between investors’ financial objectives and the legislative limitations imposed by urban planners gives rise to a multifaceted dynamic, highlighting the complexities inherent in renovating older buildings.

Framework for complexity measurement

The necessity of developing a framework for complexity measurement

The reconstruction of old buildings is a complex and challenging undertaking. Thus, complexity measurement is an imperative practice that empowers project stakeholders with valuable insights crucial for effective project management. By quantifying complexity, stakeholders clearly understand the complexity associated with their project. Furthermore, complexity measurement assists stakeholders in pinpointing the specific factors that contribute significantly to project complexity. Consequently, stakeholders can strategically address complexity by designing targeted approaches to manage its effects. This ensures that resources are allocated effectively to mitigate complexity-related challenges and enhance project performance. In addition, the measurement of complexity plays a fundamental role in facilitating ongoing efforts towards development. Regularly assessing complexity levels allows stakeholders to monitor fluctuations and evaluate the effectiveness of initiatives to mitigate complexity.

Nevertheless, it is essential to note that there is no universally applicable metric for assessing the complexity of all old building reconstruction endeavors. Each project has unique attributes that require thorough analysis. The diversity of buildings necessitates a wide range of project complexities, posing difficulties in establishing uniform project management methodologies. Hence, a pressing need exists for a comprehensive framework to assess the intricacy of restoration endeavors for aged structures. A proposed framework for measuring complexity would offer a systematic approach to comprehending and effectively handling the intricate aspects inherent in such initiatives. The proposed framework will effectively analyze and evaluate the various elements that contribute to the intricacy of a project while also establishing a shared vocabulary for stakeholders to engage in discussions about complexity. This will enable project managers to formulate more efficient strategies for handling complexity and facilitate the establishment of a collective comprehension among all stakeholders regarding the intricacies and potential hazards associated with the project.

Fuzzy synthesis evaluation for complexity measurement

The assessment and measurement of project complexity are typically problematic due to the inherent ambiguity and uncertainty associated with its nature [40]. One potential method for assessing complexity is using fuzzy set theory [42]. Fuzzy set theory is a mathematical framework that enables the quantitative representation of uncertain concepts. Fuzzy set theory can be utilized within the domain of project complexity to effectively discern and measure the diverse complexities that manifest in a given project. This methodology can acknowledge and account for the inherent uncertainty and ambiguity in intricate initiatives, facilitating a more comprehensive comprehension of the project’s complexity [10]. Researchers can utilize fuzzy set theory to discover and quantify the diverse aspects of complexity associated with projects involving renovating old buildings. By employing this methodology, scholars can cultivate a more precise and all-encompassing comprehension of the intricacies associated with these endeavors, resulting in enhanced decision-making and more triumphant results [42].

Fuzzy synthesis evaluation (FSE) is a specialized approach within the realm of fuzzy set theory, which proves to be highly advantageous in tackling intricate problems encompassing numerous layers and different features [42]. This approach enables the consideration of several criteria and aspects that may possess ambiguity, uncertainty, or inaccurate data. The FSE method has demonstrated its efficacy as a multi-criteria synthetic assessment approach in practical scenarios encompassing numerous decision-makers and unclear information [43].

Various Multi-Criteria Decision Making (MCDM) techniques have been employed in prior research to assess complexity. The authors Vidal et al. [44] and Nguyen et al. [14] deployed fuzzy Analytic Hierarchy Process (AHP) methodology to determine the complexity of projects. Similarly, He et al. [45] utilized the fuzzy Analytic Network Process (ANP) to evaluate the difficulty of mega projects. In a recent study by Kim et al. [38], the authors employed fuzzy Decision-making Trial and Evaluation Laboratory (DANP) methodology to assess the level of complexity in international development projects. Utilizing these Multiple Criteria Decision Making (MCDM) methods enabled researchers to evaluate the level of complexity in projects. However, it should be noted that the data gathering and calculation associated with these approaches were intricate [38]. The comprehension of the pairwise comparisons in the AHP, ANP, and DANP questions may have challenged the respondents [46]. In addition, those working in the respective field may encounter challenges when executing the intricate computations necessary for implementing these methodologies [38]. Therefore, it is imperative to develop pragmatic and accessible methods for assessing complexity.

Marle and Vidal [47] acknowledged that project complexity refers to the intricate nature of a project that makes it challenging to fully comprehend, predict, and manage its overall behavior, even when provided with complete information about the project system. Given such an understanding of complexity, previous scholars (i.e., Xia and Chan [48], Gransberg et al. [49], and Kermanshachi et al. [50]) argued that using complex mathematic models to investigate project complexity may make the problems to be more complex and the findings may not always be accurate. Instead, they proposed using much simpler calculations relying on a linear assumption [10] or uncorrelated factors [38]. However, Kim and Nguyen [10] criticized these studies for failing to capture the uncertainty of complexity variables.

Fortunately, the utilization of FSE offers a practical solution in this context. The ease and comprehensibility of the inquiries facilitate participants in effectively evaluating the intricacy of each variable [43]. In addition, the calculation process is uncomplicated and may be efficiently executed using widely accessible Microsoft software, such as Excel [42]. Considering the intricate nature of restoration endeavors for old buildings, utilizing the FSE technique may be an appropriate approach for evaluating the level of intricacy entailed. One further benefit of FSE is its ability to offer a systematic and unbiased decision-making framework, particularly when information may be partial or ambiguous [43]. This is especially advantageous in the context of reconstruction projects involving older buildings, whereby there may exist a scarcity of information or conflicting agendas. Moreover, utilizing FSE can effectively contribute to the comprehensive consideration of all stakeholders engaged in the project [42]. Using FSE can contribute to a comprehensive project evaluation by considering various viewpoints and criteria.

The FSE method consists of a series of six steps, which are outlined as follows:

Step 1: A basic set of variables or groups, denoted as S = {s1, s2, s3, ..., si, ..., sm}, is established, where i represents the number of variables or groups.

Step 2: A set of grading alternatives, A = {a1, a2, a3, ..., aj}, is determined, with a 5-point Likert scale used for measurement (where a1 denotes “very low complexity,” a2 denotes “low complexity,” a3 denotes “fairly complexity,” a4 denotes “complexity,” and a5 denotes “very complexity”);

Step 3: The weights for each variable or group are determined using the following formulation:

(1)
Wi=MiMi,Wi=1

Where: Mi = the sum of the mean ratings; Mi = mean score value of the corresponding variable or group; Wi = weighting.

Step 4: The evaluation matrix for each variable or group is constructed using fuzzy logic and represented as R = [rij]mxn; where rij denotes the degree of aj regarding the variable sm.

Step 5: The final result of the evaluation matrix is computed using the following equation:

(2)
D=WiRi

Where: ‘∙’ = fuzzy composite operation; D = final evaluation matrix.

Step 6: Calculate the index of each group using the following equation:

(3)
I=j=15D×L

Where: L = 1÷5 (linguistic variable)

Development of a framework for complexity measurement

This study employed the three-stage framework for assessing the complexity proposed by Nguyen et al. [38]. Specifically, Nguyen et al. [38] proposed a framework to investigate the complexity of international development projects. This validated framework was then revised to fit the purpose and the methods used in this study and depicted in Figure 1.

https://static.apub.kr/journalsite/sites/durabi/2024-015-02/N0300150201/images/Figure_susb_15_02_01_F1.jpg
Figure 1.

Framework for complexity measurement.

During the initial phase, an exhaustive compilation of intricate factors is generated through meticulously examining existing scholarly works and incorporating insights from a panel of experts. During the second stage, the Fuzzy Synthesis Evaluation (FSE) method is employed to determine the complexity weights assigned to each variable, taking into account the subjective opinions of the experts. This approach enables the incorporation of uncertainties and imprecisions into the assessment procedure. During the third step, the quantification of the project’s difficulty level involving the reconstruction of old buildings is determined by evaluating the allotted weights for each variable. The framework provides a systematic methodology for assessing and quantifying the intricacy of these undertakings.

Case study of Vietnamese old-building reconstruction project

The purpose of this section is to demonstrate the practical application of the integrated model that was described in the preceding paragraphs. This will be accomplished by providing a case study conducted in Ho Chi Minh City (HCMC), Vietnam. Vietnam has emerged as a rapidly expanding economy within the Southeast Asian region, with a commendable average annual GDP growth rate of 6.8% during the past ten years [43]. Economic growth is accompanied by a rising need for contemporary infrastructure and housing, predominantly in metropolitan regions. In Vietnam, over one million households reside in over 2,500 antiquated apartment buildings constructed before 1994. These buildings collectively occupy a total floor area exceeding three million square meters, with a notable concentration in urban centers. Nevertheless, many of these entities, specifically 600, were recognized as being in considerable deterioration [51].

To tackle these concerns, renovating and reconstructing aging residential complexes is imperative. The Vietnamese government has established lofty objectives for restoring old structures, enhancing the well-being of urban dwellers, and promoting sustainable development. HCMC, the largest city in Vietnam, has faced numerous challenges in implementing old-building reconstruction projects. The city has set a goal to address 50% of the 237 old and damaged apartment buildings by 2020, which is part of the city’s action plan. However, only 32 of the old and damaged buildings have been addressed, insufficiently providing adequate housing for the population [51]. This slow pace of redevelopment of old buildings is a significant challenge that needs to be addressed to ensure the well-being of the residents and achieve the city’s goals for urban development.

The subject case pertains to an urban location in Ho Chi Minh City and encompasses a total area of 1913 square meters, comprising 176 residential apartments. Of these, 52 units were designated explicitly for resettlement purposes, while the remaining 124 were intended for commercial housing. The building was initially established with 52 state-owned rental apartments, which tenants occupied. In 2012, the property was transferred to the developer to construct a new building, and the tenants have compensated accordingly to find temporary alternative accommodations. In 2022, the new building was completed. The developer allocated 52 of the newly built apartments for resettlement for the former residents of the former building, while the remaining 124 flats were earmarked for commercial use.

The present case study scrutinizes the critical complexity variables of the mentioned project. Practitioners engaged in this project were contacted for participation in the study. Ultimately, seven participants consented to contribute, expressing their willingness to partake in anonymous interviews. The decision-makers (DMs) team comprised seven experts, including two government agency representatives, three experts from the developer group specializing in legal affairs, financial management, and project management respectively, one expert from the advisory group acting as the design manager, and one expert from the contractor group working as the construction manager. All participants had more than ten years of experience in the construction industry. The DMs were denoted as DM1, DM2, DM3, DM4, DM5, DM6, and DM7. The proposed integrated approach was executed step-by-step for the case study, as delineated in Section 3. To accomplish the primary objective and establish a meaningful decision-making model, the following steps were executed:

Establish a list of complexity variables

The initial stage of the study involved extracting and compiling a comprehensive list of potential complexity variables about the project. This was achieved by collating data from diverse sources such as literature reviews, previous project experiences, and interviews with the DM team. A thorough literature research was undertaken to identify complexity variables pertinent to reconstructing old residential buildings. Invitations were sent to seasoned industry specialists to enhance and authenticate the initial compilation. The participants in this study have a minimum of ten years of experience in the construction sector, with a particular focus on the reconstruction of old residential buildings. They were engaged in semi-structured interviews as part of the research process. The interviews were performed with great attention to detail to explore ideas comprehensively, thus reducing the likelihood of disregarding essential aspects.

According to Chapman [52], researchers are advised to leverage the strengths of current complexity taxonomies in their specific research area rather than developing new frameworks. This methodology can yield enhanced resource and time management and a more comprehensive comprehension of the phenomenon being studied. Researchers can improve their studies of the intricacy of a specific project or system by expanding upon preexisting taxonomies. This study utilized the technology-organization-environment (TOE) paradigm proposed by Bosch-Rekveldt et al. [36] to categorize the detected factors.

Throughout the interview process, the experts assessed the appropriateness of the selected complexity variables about the example project and the TOE classification. They were also encouraged to suggest any additional variables they deemed relevant to the project. Through a collaborative discussion on project performance, the researcher and the interviewees worked together to finalize the list of complexity variables. The interviews were concluded when saturation was achieved [43], indicating a consensus among the professionals and the absence of further novel findings. Finally, 12 complexity variables were identified and presented in Table 1.

Table 1.

Complexity variables of the case project

Group Complexity variables
Technological complexity (TC) Site compensation and clearance
Scope uncertainties
Ability of consultants
Work experience
Organizational complexity (OC) Project duration
Communication between stakeholders
Financial constraints
Financial benefit uncertainty
Environmental complexity (EC) Administrative procedure
Number of stakeholders
Urban planning
Project location

Formulate the complexity weights of complexity variables/groups

The interviewees were then asked to evaluate the complexity of the identified complexity variables according to the five-point Likert scale (i.e., very low complexity, low complexity, fairly complexity, complexity, very complexity). Collected data was then employed as input for the Fuzzy Synthesis Evaluation (FSE) method presented in Section 3.

Using the formula (1), the weightings of 12 complexity variables and three groups were calculated and illustrated in Table 2.

Table 2.

Weightings of complexity variables and groups

Complexity variables and groups The mean of each
variable
The weighting of each
variable
Mean of each
group
Weighting of each
group
Technological complexity 15.143 0.363
Site compensation and clearance 4.714 0.311
Scope uncertainties 4.286 0.283
Ability of consultants 1.714 0.113
Work experience 4.429 0.292
Organizational complexity14.7140.353
Project duration 4.429 0.301
Communication between stakeholders 4.714 0.320
Financial constraints 2.857 0.194
Financial benefit uncertainty 2.714 0.184
Environmental complexity 11.8570.284
Administrative procedure 3.429 0.289
Number of stakeholders 3.286 0.277
Urban planning 2.000 0.169
Project location 3.143 0.265
Total mean value of CBs41.714

The membership function (MF) for each complexity variable was determined. The computation of a complexity variable’s MF was based on respondents’ judgments using a scale measurement. To illustrate, the MF for “Site compensation and clearance” is presented as follows:

MFTC1=0.00verylowcomplexity+0.00lowcomplexity+0.00fairlycomplexity+0.29complexity+0.71verycomplexity

Likewise, the remaining complexity variables’ membership functions (MFs) were assessed. Upon determining the MFs for all complexity variables, the MF of each complexity group was calculated using formulation (3). For instance, to compute the MF of the “Technological complexity” group, the following process was undertaken:

D1=W1R1=(0.311,0.283,0.113,0.292)×0.000.000.000.290.710.000.000.000.710.290.000.000.570.430.000.000.000.000.570.43=(0.00,0.00,0.06,0.51,043)

Similarly, the weightings for all other complexity groups were determined. The subsequent step involved evaluating the index of each complexity group by employing formulation (4). For example, the complexity index for the “Technological complexity” group (TC) was calculated as follows:

ITC=j=15D×L=(0.00,0.00,0.06,0.51,0.43)×(1,2,3,4,5)=4.364

The summary of the index results for each complexity group is presented in Table 3.

Table 3.

Summary of results

Complexity Group Important level Coefficients Ranking
Technological complexity (TC) 4.364 0.360 1
Organizational complexity (OC) 4.223 0.348 2
Environmental complexity (EC) 3.532 0.291 3
Overall complexity index 4.078

Calculate the degree of complexity of the old-building reconstruction project

Finally, the complexity index was formulated based on the linear relationships among the complexity groups.

Iván [53] highlighted the complexity of complexity measurement. Thus, Dao [54] argued a simple approach should be employed to investigate complexity rather than using complex methods. Kim and Nguyen [10] also noticed that using complex mathematical formulations may make the studies more complex, and the findings may not necessarily be more accurate than simple ones. In that case, Xia and Chan [48] and Gransberg et al. [49] proposed a linear should be used to study the project’s complexity. Indeed, Xia and Chan [48] established a linear formulation for measuring the complexity of building projects in Chian. Gransberg et al. [49] assessed the complexity of international transportation projects by independently evaluating five dimensions: technical, cost, context, financing, and schedule, thereby creating complexity footprints. This approach is reinforced by the convenience and ease of interpretation of the results using a linear equation model, which has been utilized in previous research [42, 43].

In this study, the complexity index of each group was normalized, and the ultimate complexity index was obtained by combining the three normalized indexes. For example, the complexity coefficients (CC) for the “Technological complexity” group (TC) were calculated as follows:

CCTC=4.3644.364+4.223+3.532=0.360

Finally, the complexity index is presented below:

Complexity index = 0.360 x TC+ 0.348 x OC + 0.291 x EC

The complexity index results, presented in Table 3, indicate an overall complexity index of 4.078 (out of a maximum of 5.00), which suggests that the case project exhibits a considerable degree of complexity. Technological complexity was the most complex group, followed by Organizational and Environmental complexity.

Results and Discussions

The case study project demonstrates a significant level of technological complexity (TC), which can be attributed to four key variables: site compensation and clearing, uncertainties in project scope, the expertise of consultants, and work experience. The intricacy of site compensation and clearance arose due to the relocation process for the residents of the old building. This process, which commenced after the developer was granted the project, spanned three years. The relocation procedure was further complicated by the transition period between state compensation schemes, resulting in increased uncertainty and complexity. In addition, the project was significantly affected by uncertainty regarding its scope, which contributed to the technological complexity. Initially conceived as a structure including 20 floors, the project underwent a modification in its extent due to the influence of stakeholders, resulting in a final configuration encompassing 24 floors. These modifications have the potential to introduce difficulties in the planning and implementation of projects, hence augmenting the likelihood of errors and the need for additional effort.

The competence of consultants was an additional independent variable in the project. The consultants engaged in this instance needed to gain familiarity with the unique difficulties of reconstructing aged structures. Consequently, this lack of expertise contributed to the intricacy of the project and necessitated supplementary exertion to familiarize them with the subject matter. The absence of sufficient experience resulted in extended project timelines and escalated expenditures. Moreover, the developer’s and other stakeholders’ work experience significantly influenced the project’s technological complexity.

Given that this was their inaugural endeavor in reconstructing aged structures, they encountered a formidable learning curve. They were compelled to navigate through a multitude of unanticipated obstacles. The absence of prior experience can result in a deficiency of anticipation and heightened vulnerability, rendering the endeavor more intricate than its hypothetical alternative.

The sample project’s organizational complexity (OC) is delineated by four variables: project duration, stakeholder communication, financial constraints, and uncertainty regarding economic benefits. The project’s duration is crucial in OC, as it encompasses 12 years for the successful culmination of the reconstruction endeavor. The extended time frame of this project presents difficulties for the developer, stakeholders, and residents participating in it. In addition, meticulous strategic planning and effective project management are essential to maintain progress and financial feasibility. Effective communication among stakeholders is an additional element that influences organizational culture. The project encompasses multiple stakeholders, each with their respective priorities and interests. The presence of divergent priorities gives rise to challenges and inefficiencies in communication, impeding advancements and fostering discord.

Financial limitations are a notable factor in organizational change (OC) since the developer is confronted with restricted financial resources and encounters difficulties securing loans from financial organizations. The developer must demonstrate the project’s economic benefit to secure financing, which can be challenging given the long project duration and uncertainty surrounding financial benefits. This uncertainty can create additional challenges for the developer and make it more difficult to secure funding. Moreover, financial benefit uncertainty is a critical variable in OC. The developer prioritizes economic benefits but also needs to ensure compliance with regulations and protect the interests of the residents. The site compensation and clearance are an expensive part of the project, and there are restrictions on the number of floors that can be built. This uncertainty around financial benefits makes it more challenging for the developer to secure funding and manage the project effectively.

The complexity arises from four primary elements: administrative procedure, the number of players involved, urban planning considerations, and the specific site of the project. The administrative process in Vietnam is known for its complexity and time-consuming nature, resulting in potential delays and heightened expenses [38]. The reconstruction project of old buildings in Ho Chi Minh City was no exception. The developer had to acquire multiple permits and approvals from numerous governmental entities, frequently leading to bureaucratic obstacles and subsequent delays. This factor introduced additional intricacy to the project, augmenting its environmental delicacy. The inclusion of multiple stakeholders further heightened the environmental complexity of the project. The stakeholders encompassed several parties, namely the inhabitants of the preexisting structure, the developer, governmental entities, financial establishments, and construction firms. Each stakeholder possessed distinct interests and priorities, frequently resulting in conflicts with one another [40]. Establishing intricate interconnections necessitates efficient management to guarantee the achievement of the project.

The variable of urban planning further influenced the project’s environmental complexity. The renovation of the old buildings was required to comply with rigorous urban planning guidelines established by the governmental authorities [51]. The newly constructed tower’s vertical dimension was limited due to its placement within a densely populated metropolitan vicinity. This necessitated the developer to strike a compromise between the residents’ desires for increased space and the regulatory constraints imposed by the government. This factor introduced additional intricacy to the project, contributing to its heightened environmental complexity. The environmental complexity of the project was further compounded by its geographical placement. The project was situated in a highly populated region of Ho Chi Minh City, necessitating the developer’s careful consideration of the project’s implications for the neighboring neighborhood. The concerns mentioned above encompassed matters such as the accumulation of vehicular traffic, the emission of auditory disturbances, and the relocation of inhabitants. The developer was required to establish tight collaborations with governmental entities and local communities to ensure the project’s successful implementation with a focus on environmental sustainability.

Conclusions

The significance of rebuilding projects for old buildings has witnessed a notable rise in numerous metropolitan regions as a means to substitute deteriorating structures that no longer adhere to contemporary safety and living requirements. Nevertheless, executing these initiatives poses noteworthy obstacles that may result in setbacks and stakeholder discontent. This study proposes a conceptual framework for assessing the intricacy of restoration endeavors on old buildings. This framework utilizes a measurement model founded on the Fuzzy Synthesis Evaluation (FSE) technique.

To ascertain the framework’s efficacy, the study implemented it in a rebuilding project for an aged structure within an urban Vietnam region. The analysis results indicated a complexity index of 4.078, suggesting a significant level of complexity in the case study project. The project’s technology side was determined to be the most intricate, with organizational and environmental complexity following behind.

This study contributes significantly to the existing knowledge by presenting a conceptual model for evaluating the complexity of old building reconstruction projects. This study emphasizes the benefits of the FSE approach, particularly its straightforwardness in gauging project complexity. In contrast to intricate methods like fuzzy AHP and fuzzy DANP, which entail complex questionnaires and mathematical computations, the FSE questionnaire is user-friendly and easily computable. This contribution enhances understanding and decision-making in old building reconstruction projects. The proposed framework provides project stakeholders, including investors, owners, government agencies, and consultants, with a tool to evaluate the complexity of their projects, facilitating the identification of suitable complexity management strategies.

However, some limitations can be improved in future research. Firstly, applying the proposed framework was limited to only one old-building reconstruction project. While the framework’s effectiveness was demonstrated, its broader applicability could benefit from future research endeavors. Encouraging the deployment of this framework across different projects would provide a more comprehensive validation of its efficacy and adaptability. Furthermore, the complexity measurement of the case project relied on insights from a relatively small group of seven practitioners. While these individuals play integral roles within the project and their contributions hold significance, expanding the survey to encompass a more diverse pool of respondents in subsequent studies could significantly bolster the framework’s robustness. A larger dataset containing a broader spectrum of perspectives would enable a more thorough and holistic evaluation of the framework’s efficacy, allowing for a deeper understanding of its potential benefits and limitations.

Acknowledgements

The authors would like to appreciate Dr. Chu Viet Cuong and Mr. Nguyen Thanh Cuong for their helping during data collection.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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