Examining Challenges in Complying with the Principles of Sustainability for the Design of Urban Bridges in Ethiopia

Abstract

Existential issues obstruct the practice of incorporating sustainability concepts, which is the holistic consideration of urban bridge design factors. Bridge infrastructure is considered a connecting structure for separated highways and railways. The case for ensuring the safe mobility of people and goods across obstacles from one urban corner to another is viewed as an essential component of transportation infrastructure. The design and provision of urban bridges to attain sustainability are associated with tremendous challenges because of a lack of awareness and existential issues and obstacles. The problem in the practice of urban bridge design in Ethiopia is indicated as being “traditional” in delivery, with a lack of accommodation for many essential components of sustainable design. Therefore, a change in thinking is needed to address sustainability. The question of how designers could make design practice sustainable is complicated by multiple challenges. In this research, we used a survey questionnaire to collect the opinions of design professionals. Principal component analysis was employed to explore the major gaps in sustainable urban bridge design practice. A lack of sustainable design impact; sustainability awareness; design codes, practices, and standards that consider sustainability criteria; working guiding protocols and frameworks; and support for sustainability practice were identified as major challenges. Addressing the design problem requires a mechanism to consider the challenges through the defined participation of the designer, client, and public during rule setting, monitoring, and evaluation. Sustainability rating tools must also be deployed to evaluate and quantify the performance of urban bridges.

1. Introduction

Sustainability objectives for roadway infrastructure development as suggested in [1] include resource efficiency to minimize the use of virgin or natural resources; environmental quality to minimize the deterioration of air and acoustic quality of the local environment by implementing measures such as congestion pricing, vegetation planning, and noise abatement measures; and ecological protection to protect existing water bodies, land, and ecological systems.

The primary components of road transport infrastructure are roads and highways, bridges, tunnels, operations and traffic management centers, border-crossing facilities, and truck terminals, among others [2,3]. Umer et al. (2016) suggested that due to urbanization, there is an increasing demand for services on the currently aging, insufficient, and vulnerable roadway infrastructure. The explicit goal of effective and efficient transportation infrastructure is to boost economic activity and development [4,5,6]. International competitiveness and economic growth can be aided by well-developed transportation infrastructure [7].

Bridges are part of transportation systems, which are the key infrastructure in connecting people, goods, and services [8,9]. Bridges are also valuable assets that connect and simplify movement in various places despite several constraints. Bridges represent a significant element of major roadway development and therefore demand significant expenditure. Engineering processes in bridge projects are always performed in a traditional way, such as using standard electronic drafting to create shop drawings, performing manual quantity takeoffs and cost estimation, selecting construction methods based on project manager experience, manual document control, and traditional bridge management during the operation and maintenance phase [10].

The provision of bridges in cities and towns must be approached holistically [11,12]. The roles of designers, contractors, owners, and communities in the realization of a sustainable bridge are essential. A primary objective in the field of design studies is to create urban bridges with the intention of achieving sustainability [13,14]. Bridge design account the design process from conceptual to final design work. Ensuring an appropriate concept moves the design in the right direction. The design process associated with attaining sustainability is framed by factors, parameters, and indicators. The responsible development, operation, and management of sound infrastructure based on resource efficiency, technological adaptation, minimal environmental effects, optimal economics, and social equality for both the present and future generations are characterized as sustainability [15,16,17]. Sustainable infrastructure can be achieved through sustainable design practice. As an alternative to traditional design, sustainable design promotes a less consumerist perspective and upholds social responsibility, environmental stewardship, economic viability, and global interdependence [18,19,20]. The process of designing infrastructure needs to fulfill the key major elements outlined in [21]: material selection, economic considerations, social considerations, environmental considerations, technical considerations, policy and regulations, design and project management, and design professionals and the design process. Infrastructure design is unique, owing to its capital intensity, the size of each structure, and interaction with the natural environment [22].

The case of Ethiopian urban bridge provision is like that of other locations, but its delivery mechanism is particularly traditional, especially in terms of the long-term viability of bridge design approaches [23]. The problem in the practice of urban bridge design in Ethiopia is indicated as being “traditional” in delivery, with a lack of accommodation for many essential components of sustainable design. Therefore, a change in thinking is needed to address sustainability. Natural urban topography and morphology, on the other hand, necessitate sound design provisions. The problem is that cities in Ethiopia were founded based on a militaristic strategy and are sometimes located in mountains and gorges. There is a huge demand for bridges and tunnels in urban regions. The question of how designers could make the design practice sustainable is complicated by many challenges. Unless the issue is adequately defined and comprehensively handled, urban complexity will make the issue worse. The concept of a sustainable urban bridge as a core mechanism considers change factors and their relationships to address their current and future impacts. Urban infrastructure design must adapt to a new reality that involves retrofitting and reorganizing urban regions to handle exponential population expansion, resource limitations, and environmental concerns [24].

Urban bridges are vital infrastructure that are essential for fostering smooth connectivity between areas and urban mobility, in addition to facilitating accessibility, reducing barriers, and adding value if the spaces beneath are used [25,26,27]. A bridge must carry a service, such as highway or railway traffic, a footpath, or public utilities, over an obstacle. To achieve responsive and sound structure, the conceptual design process of bridge infrastructure in an urban environment requires a collaborative, thoughtful, and integrative approach with the aim of satisfying the principles of social, environmental, and economic sustainability.

The whole-system design practice [28] comprising the design process, design principles, and design methods as input to the system to address sustainability has not been easily practiced in Ethiopia. Incorporating sustainability parameters in the design of urban bridges could play a significant role in the efficient whole-system design practice. Although traditional design and building prioritize cost, performance, and quality, sustainable design and construction add to these criteria a reduction in resource depletion and environmental degradation, as well as the creation of a healthy built environment [29]. Employing sustainable design principles can significantly lower energy use, and these methods are increasingly crucial across the architecture, engineering, and construction (AEC) sector globally [30]. The goal of sustainable design is to “eliminate the negative environmental impacts completely through skillful, sensitive design” [31].

Through exploratory factor analysis [32,33], previous research outlined the existing principal challenges and factors associated with design practices for urban bridges to meet sustainability criteria. Perceptions, existing issues, and obstacles are the subjects of the present study through, which are investigated through component analysis. Principal component analysis [34] can reduce many compelling challenges associated with sustainable urban bridge design. The survey method [35] was used to collect stakeholders’ perspectives on the sustainability of urban bridge design in Ethiopia’s architectural, construction, and engineering industries. Therefore, the aim of this study was to identify the main existing issues, perceptions, and obstacles associated with complying with the concepts of sustainability in Ethiopian urban bridge design practice.

2. Method

We use quantitative methodology to identify the main compelling challenges of sustainable design practice through a subjective assessment using a Likert-type [36] scale of measurement with closed-end questions. This methodology quantifies the responses and tends to measure sufficient numerical data and responses [35,37]. The Likert measurement scale helps to obtain the opinions of respondents [38,39].

The subjective opinions of all respondents (n = 204) were obtained using a survey questionnaire [38] based on exposure and experiences of designers and researchers in the AEC industry. The number of samples was determined through a numerical computation of registered design professionals (N = 2532) with the Ethiopian Ministry of Urban Development and Construction, Construction Industry Development & Regulatory Bureau. The survey questionnaire was the data collection tool used to gather the opinions of the respondents. Questionnaires are instruments that help to obtain first-hand information on the field of the research respondents. The first part of the survey collected the respondents’ background information. The second part surveyed the respondents on their level of agreement with respect to the extent of the challenges associated with sustainability principles in urban bridge design.

The questions asked in order to address the objective of the study were framed according to a Likert scale. A five-point Likert scale measures respondents’ level of agreement [36]. The measurement scale is as follows: 1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, and 5 = strongly agree. Alternatively, very low = 1, low = 2, moderate = 3, high = 4, and very high = 5 for some of the survey questions.

Descriptive studies are conducted using cross tabulation to explain the relationship of different parameters towards the key informant. Exploratory factor analysis (EFA) was also chosen based on its suitability to identify factors or dimensions among variants. When it is unclear how many factors are required to explain the interrelationships among a set of characteristics, indicators, or items, exploratory factor analysis (EFA) can be employed [40,41,42]. As a result, researchers use factor analysis techniques to investigate the underlying dimensions of the construct of interest [43].

EFA is based on the idea that inside a set of observed variables, there exists a set of underlying factors, which fewer in number than the observed variables and can explain the interrelationships between them [44]. Because Pearson product–moment correlations are used in the first steps of factor analysis, many of the assumptions that apply to this parametric statistic also apply to factor analysis (e.g., large sample sizes, continuous distributions, and linear relationships among items) [43]. In terms of methodology, principal component analysis (PCA) is highly recommended when there are many factors to be reduced to fewer components.

In contrast to the method described above, the maximum likelihood method [45] is used to calculate the parameters of each distribution when the distribution of parameters affects the outcome. The PCA approach uses latent data extraction to explain current parameters. The importance of decision and prediction procedures must also be emphasized when employing trained artificial neural networks (ANNs) [46,47] or other approaches akin to them in confirmatory factor (CF) research. The scope of the present study is restricted to an exploratory analysis using the PCA approach. The study report excludes confirmation factor analysis of the measurement model and determination of the goodness of fit.

The statistical analysis tool employed for quantitative analysis using exploratory factor analysis technique was a Statistical Package for Social Sciences (SPSS, IBM SPSS 23.0), as this tool is well-suited to reduce dimensions or factors [43]. The encoded data were analyzed using this tool due to its user-friendliness and suitability elicit subjective opinions of respondents. The results are discussed based on existing theories.

3. Results

3.1. Data Statistics

The demographic data of the respondents is illustrated in the cross tabulation of Figure 1 and Figure 2. The key pieces of information gathered were education level, areas of design experience, specialization based on education and/or experience, and years of design experience in bridge infrastructure. Education level across years of bridge infrastructure design experience and professional specialization with areas of design experience are the variables compared.

The sample distribution presented in Figure 1 shows the level of education and years of design experience. All 48 respondents had a bachelor’s degree, but none had experience in the design of bridges. Most of the respondents had fewer than 6 years of design experience in bridge infrastructure and had a bachelor’s degree (n = 35), and 40 professionals had a master’s degree. The number of respondents who had more than 6 years of design experience in bridge infrastructure was 27 in total, whose level of education included a master’s degree and Ph.D.

Figure 2 depicts the cross tabulation between respondents’ specialization and areas of design experience in general infrastructure. The number of professionals who specialized in bridge engineering with bridges and buildings as areas of experience was 18 and 5, respectively. Those who had bridge design experience generally specialized in structural engineering (n = 18) and bridge engineering (n = 20).

The research queries were organized into five main questions, and these questions were supported by several statements aimed at measuring each construct of sustainable practices in bridge design. Sustainable design is an alternative approach to traditional or conventional design that leads toward a less consumptive mindset and embraces global interdependence, environmental stewardship, social responsibility, and economic viability. It is technologically systematic and considers the impacts of design choices at local, regional, and global levels.

The most suitable ways of asking questions to the respondents were determined to be by obtained their level of agreement or disagreement depending on the type of question. Questions were posed to respondents using a Likert scale measurement of 1 to 5.

Table 1. Main statements for assessing the applicability of sustainability in urban bridge design.

Question/Statement	Scale
Level of agreement in perception/opinion of sustainability in bridge design	(1= strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, and 5 = strongly agree)
2. Level of issues affecting sustainable design practices	(Very low = 1, low = 2, moderate = 3, high = 4, and very high = 5)
3. Level of influence of stakeholders in sustainable urban bridge design	(Very low = 1, low = 2, moderate = 3, high = 4, and very high = 5)
4. Level of existing obstacles to practicing sustainable design of urban bridges	(Very low = 1, low = 2, moderate = 3, high = 4, and very high = 5)
5. Level of benefits available for practicing sustainable design of urban bridges	(Very low = 1, low = 2, moderate = 3, high = 4, and very high = 5)

summarizes the main questions and statements raised and their Likert scales.

The five primary assertions can be measured obtaining ratings for the statements, processing the data using statistical means, and coding the means using different codes within a range. The range from 1.00 to 1.79 was coded as ‘1′, that from 1.80 to 2.59 was coded as ‘2′, that from 2.60 to 3.39 was coded as ‘3′, that from 3.40 to 4.19 coded as ‘4′, and that from 4.20 to 5.00 was coded as ‘5′.

The data were tested for reliability and consistency of the questionnaire, and the result was evaluated using Cronbach’s alpha [48], which was found to be 0.906, surpassing the standard value of 0.5 [49]. Therefore, the 61 items included in the survey questionnaire were reliable for further statistical analysis. The data collected from 204 samples of respondents were also tested for normality to determine the data values were sampled from a normally distributed population. A test of normality, along with the generation of statistical graphs (computations), was conducted for hypothesis testing.

Table 2. Descriptive study assessing the applicability of sustainability in urban bridge design.

Descriptive
			Statistic	Std. Error
Level of agreement in perception/opinion of sustainability in bridge design	Mean		3.8424	0.04218
	95% Confidence Interval for Mean	Lower Bound	3.7592
		Upper Bound	3.9255
	5% Trimmed Mean		3.8522
	Median		4.0000
	Variance		0.361
	Std. Deviation		0.60097
	Minimum		2.00
	Maximum		5.00
	Range		3.00
	Interquartile Range		0.00
	Skewness		−0.615	0.171
	Kurtosis		1.262	0.340
Level of issues affecting sustainable design practices	Mean		3.9261	0.05541
	95% Confidence Interval for Mean	Lower Bound	3.8169
		Upper Bound	4.0354
	5% Trimmed Mean		3.9735
	Median		4.0000
	Variance		0.623
	Std. Deviation		0.78945
	Minimum		2.00
	Maximum		5.00
	Range		3.00
	Interquartile Range		0.00
	Skewness		−0.539	0.171
	Kurtosis		0.096	0.340
Level of influence of stakeholders in sustainable urban bridge design	Mean		4.1182	0.06031
	95% Confidence Interval for Mean	Lower Bound	3.9993
		Upper Bound	4.2371
	5% Trimmed Mean		4.1869
	Median		4.0000
	Variance		0.738
	Std. Deviation		0.85932
	Minimum		2.00
	Maximum		5.00
	Range		3.00
	Interquartile Range		1.00
	Skewness		−0.845	0.171
	Kurtosis		0.192	0.340
Level of existing obstacles to practicing sustainable design of urban bridges	Mean		3.6798	0.05503
	95% Confidence Interval for Mean	Lower Bound	3.5713
		Upper Bound	3.7883
	5% Trimmed Mean		3.6935
	Median		4.0000
	Variance		0.615
	Std. Deviation		0.78408
	Minimum		2.00
	Maximum		5.00
	Range		3.00
	Interquartile Range		1.00
	Skewness		0.072	0.171
	Kurtosis		−0.571	0.340
Level of benefits available for practicing sustainable design of urban bridges	Mean		4.2266	0.05792
	95% Confidence Interval for Mean	Lower Bound	4.1124
		Upper Bound	4.3408
	5% Trimmed Mean		4.2956
	Median		4.0000
	Variance		0.681
	Std. Deviation		0.82527
	Minimum		1.00
	Maximum		5.00
	Range		4.00
	Interquartile Range		1.00
	Skewness		−1.084	0.171
	Kurtosis		1.436	0.340

describes the descriptive properties of the five main inquiries and, for the purpose of testing normality, the extent to which the skewness and kurtosis can be observed, which can be used to determine the level of normality by computing the ratio of statistical skewness or kurtosis over the standard error. When z values range from −3 to +3, normal distribution is assumed. However, this process does not always work if samples are too small or too large. The z-score value of the samples did not fall in the specified range, suggesting that the sample was normally distributed. However, the test of normality would be sound if other methods such as the Shapiro–Wilk test [50,51] were employed.

Therefore, the test for the hypothesis was conducted using the Shapiro–Wilk test of normality (

Table 3. Test of normality to assess the applicability of sustainability in urban bridge design.

Tests of Normality
	Kolmogorov–Smirnov a			Shapiro–Wilk
	Statistic	df	Sig.	Statistic	df	Sig.
Level of agreement in perception/opinion of sustainability in bridge design	0.382	203	0.000	0.740	203	0.000
Level of issues affecting sustainable design practices	0.296	203	0.000	0.834	203	0.000
Level of influence of stakeholders in sustainable urban bridge design	0.258	203	0.000	0.810	203	0.000
Level of existing obstacles to practicing sustainable design of urban bridges	0.236	203	0.000	0.852	203	0.000
Level of benefits available for practicing sustainable design of urban bridges	0.254	203	0.000	0.791	203	0.000

^a. Lilliefors Significance Correction.

).

Null Hypothesis (H0):

The sampled data values are normally distributed.

Alternative Hypothesis (H1):

The sampled data values are not normally distributed.

Both the test of normality using Kolmogorov–Smirnov and Shapiro–Wilk tests resulted in the same significance (0.001), which is less than the p value of 0.05 [50,51]. This test suggests that there is a significant deviation of the sampled data distributions from a population that follows a normal distribution. Before rejecting the null hypothesis, it is always suggested to observe quantile–quantile (Q-Q) plots or the frequency distribution histograms of descriptive statistics. The following graphs show the Q-Q plots of the data sampled for the respective questions (Figure 3 and Figure 4). The plot for all questions tends to offset at the tail and heads of the data distribution compared to the straight line.

Based on the above justifications, the null hypothesis is rejected, and the alternative hypothesis is accepted; the data values of the samples used to assess the applicability of sustainability in the design of urban bridges are not normally distributed. This calls for a study of non-parametric testing of data, i.e., Spearman rank-order correlation [52].

Spearman rank-order correlation can be employed if the data have a characteristic of monotonicity (the value of one variable increases or decreases as the values of other variables increase or decrease, respectively); the distribution of data should also be linearly related. Figure 5 shows a scatter dot plot matrix of the five variables, indicating relationships among factors. The variables with good linearity and monotonicity represent issues and existing obstacles. This finding was also checked using Spearman’s rank-order correlation coefficient (

Table 4. Non-parametric correlation tests of sustainability practice.

			Perceptions	Affecting Issues	Influence of Stakeholders	Existing Obstacles	Available Benefits
Spearman’s rho	Perceptions	Correlation Coefficient	1.000	0.313 **	0.226 **	0.369 **	0.340 **
		Sig. (2-tailed)	.	0.000	0.001	0.000	0.000
		N	204	204	204	204	204
	Affecting Issues	Correlation Coefficient	0.313 **	1.000	0.313 **	0.534 **	0.275 **
		Sig. (2-tailed)	0.000	.	0.000	0.000	0.000
		N	204	204	204	204	204
	Influence of Stakeholders	Correlation Coefficient	0.226 **	0.313 **	1.000	0.202 **	0.328 **
		Sig. (2-tailed)	0.001	0.000	.	0.004	0.000
		N	204	204	204	204	204
	Existing Obstacles	Correlation Coefficient	0.369 **	0.534 **	0.202 **	1.000	0.319 **
		Sig. (2-tailed)	0.000	0.000	0.004	.	0.000
		N	204	204	204	204	204
	Available Benefits	Correlation Coefficient	0.340 **	0.275 **	0.328 **	0.319 **	1.000
		Sig. (2-tailed)	0.000	0.000	0.000	0.000	.
		N	204	204	204	204	204

**, correlation is significant at the 0.01 level (2-tailed).

).

The Spearman’s coefficient (r) and p value of each variable can be considered with respect to the decision as to whether the variables are significantly correlated or not. Association was measured using the range of r values suggested in [52] as absolute magnitude of the observed correlation coefficient interpretation: 0.00–0.10, negligible correlation; 0.10–0.39, weak correlation; 0.40–0.69 moderate correlation; 0.70–0.89, strong correlation; 0.90–1.00, very strong correlation. All Spearman r coefficients are positively correlated from weak to moderate at a 1% significance level. Table 4 clearly shows that the variables of existing obstacles and affecting issues exhibit a moderate correlation in the positive direction (r = 0.534, p = 0.001, N = 204), whereas the other variables have a weak degree of correlation, for example, the level of existing obstacles over the level of perception of sustainability (r = 0.369, p = 0.001, N = 204).

3.2. Descriptive Statistics

The ranking of the statements answered by the respondents for each variable is summarized in the charts below from the most impactful statement to the least impactful statement (

Table 5. Ranking of statements with respect to the practice of sustainability.

Perceptions of Sustainability on Bridge Design Practice	N	Mean	Rank
Bridge design process should include sustainability considerations	204	4.26	1
Important for planners, architects, and engineers to be conscious that some of the designs they execute have an impact on the environment	204	4.23	2
Important for planners, architects, and engineers to be conscious that some of the designs they execute have an impact on the economy	204	4.16	3
Important for planners, architects, and engineers to be conscious that some of the designs they execute have an impact on society	204	4.14	4
Use of sustainable design principles, processes, and methods will help to preserve natural resources	204	4.12	5
Important for planners, architects, and engineers to be conscious that some of the designs they execute have an impact on the institution’s performance	204	4.00	6
Important for planners, architects, and engineers to be conscious that some of the designs they execute have an impact on technological usage	204	4.00	7
Guidelines or frameworks for sustainable design should be easily found in Ethiopia	204	3.78	8
Sustainability considerations are mainly for satisfying mandatory requirements	204	3.35	9
The use of sustainable design principles would reduce construction cost and time	204	3.35	10
I am aware that sustainability is getting more recognition among my colleagues and co-workers	204	3.31	11
I believe that using sustainable design principles will increase construction cost and time	204	3.10	12
Even if there is an increase in construction cost and time, I have noticed that my colleagues intend to incorporate sustainability in bridge design practice	204	3.04	13
Even if there is an increase in construction cost and time, I have noticed that my clients intended to apply sustainable design principles, processes, and methods in projects	204	2.90	14
Adopting sustainable design practice should be voluntary	204	2.84	15
Valid N (listwise)	204
Issues affecting sustainable design practice	N	Mean	Rank
Limited finance for sustainable design practice	204	3.98	1
Inadequate current design facilities and techniques	204	3.80	2
Lack of structured protocols or frameworks	204	3.80	3
Inadequate instructions about design input, process, and outputs	204	3.79	4
Lack of sustainable design codes and standards	204	3.79	5
Lack of access to current and relevant information	204	3.78	6
Demands lots of skill and time in conceptualizing, analyzing, and designing	204	3.76	7
Weakness of professionalism	204	3.72	8
Lack of awareness of sustainability	204	3.70	9
Conventionality of the current design practice	204	3.70	10
Problems in understanding sustainable design processes and criteria	204	3.70	11
Demands high effort	204	3.60	12
Problems in determining main attributes	204	3.56	13
Valid N (listwise)	204
Influence of stakeholders in sustainable urban bridge design	N	Mean	Rank
The client (private and government)	204	4.34	1
Bridge engineer	204	4.06	2
Architect/urban designer	204	4.05	3
Urban planner	204	4.03	4
Structural engineer	204	3.93	5
Environmental planner	204	3.89	6
The public	204	3.60	7
Valid N (listwise)	204
Existing obstacles to sustainable urban bridge design practice	N	Mean	Rank
Lack of support from officials and/or politicians	204	3.91	1
Lack of professionals who are equipped with the concept	204	3.81	2
Unwillingness to change the conventional way of designing	204	3.77	3
Difficulties in balancing environmental, institutional, technological, economic, and social issues	204	3.76	4
Lack of tools and data to evaluate sustainable design practice	204	3.76	5
Perception of extra cost being incurred	204	3.75	6
Lack of codes and standards	204	3.58	7
Low flexibility for alternatives or substitutes of design process	204	3.58	8
Perception of extra time being incurred	204	3.56	9
Limited availability and reliability of design tools	204	3.47	10
Uncertainty in the liability for final works	204	3.44	11
Lack of information on sustainable design concepts	204	3.43	12
Problems in evaluating information	204	3.39	13
Perception that sustainable design is related to environmental design	204	3.34	14
Possibly delayed delivery due to sustainability requirement	204	3.24	15
Aesthetically less pleasing	204	2.78	16
Valid N (listwise)	204
Prevailing benefits of practicing sustainable urban bridge design	N	Mean	Rank
Increase social benefits	204	4.16	1
Reduce the environmental impact of the construction of a design product	204	4.12	2
Utilize recent technologies and state-of-the-art design approach	204	4.00	3
Efficiently addresses the major resource consumption problem	204	3.99	4
Economic optimization	204	3.96	5
Possibility to increase professional collaboration	204	3.95	6
Mitigate climatic problems	204	3.93	7
Helps to holistically approach a design problem	204	3.93	8
Introduces a good institutional culture for delivering efficient projects	204	3.93	9
Facilitates sound issuance, approval, and monitoring of institutional capacity	204	3.86	10
Valid N (listwise)	204

).

The descriptive statistical analysis presented in Table 5 shows that there are factors that dominantly affect sustainable urban bridge design, whereas other factors do not have a notable effect. Based on the responses and the determination of the level of perception of respondents with respect to sustainability practice in urban bridge design, the top-ranking statement rated as strongly agreeing was “Bridge design processes should include sustainability considerations”, whereas the statement, “Adopting sustainable design practices should be voluntary” was ranked last, with disagreement among the mean respondents. It is indeed a great supposition to consider sustainability in bridge design practices, and it should not be on a voluntary basis.

One of the main issues affecting sustainability identified in the practice of urban bridge design is “limited finance for sustainable design practice”, with a high level of agreement among average respondents. The other factor, “Problems in determining the main attributes” ranked last but was heavily weighted. The contribution of finance to design is noted with respect to determining possible and main attributes or criteria.

Stakeholder participation was also considered in the survey study, and the responses showed that the design stakeholders are highly needed in the execution of sustainable urban bridge design. The client (private or government), bridge engineer, architect/urban designer, urban planner, structural engineer, environmental planner, and the public are needed for the practice of sustainability in urban bridge design. However, the top-ranked stakeholder with the most influence is the client (private or government), and the lowest-ranked stakeholder with a low level of influence is the public. This shows how the consideration of the client and the public can influence the applicability of sustainability in the design work.

The most significant barrier to the practice of sustainability was determined to be a “lack of support from officials”, with “politicians” as the first-ranked obstacle; the factor with the lowest neutral rate of agreement was “aesthetically less pleasing”. Regarding the benefits of practicing sustainability, we found that “increase social benefits” was ranked first, with a very high rate of mean agreement, whereas “facilitate the sound issuance, approval, and monitoring of institutional capacity” received a high rate of agreement.

3.3. Exploratory Factor Analysis

The variables of perception, issues affecting sustainability, and existing obstacles were explained by factor analysis. However, the variables of stakeholder influence and the prevailing perception load on a single component; thus, the factorial analysis was not conducted. Principal component analysis was employed as the extraction method, with orthogonal rotation using the method of Varimax with Kaiser normalization [53,54]. EFA is mainly used in research involving the division of a large number of observed variables into smaller and more manageable units called latent factors.

The steps followed are based on the suggestions provided in [43,55]:

Step 1. Conduct KMO and Bartlett’s test of sphericity; if the results range within a 5% significance level, with a KMO value of more than 0.500, proceed with EFA analysis.

Step 2. Compute the communality by the given extraction and rotation method; the value of extraction should be more than 0.500.

Step 3. Explain the total cumulative variance of the generated components; they should explain more than 50% of the total variance of the components, and the eigenvalue which should be more than one. We also suggest conducting parallel analysis using Monte Carlo PCA [56] simulations to determine which eigenvalues should be considered.

Step 4. Finally, check the components in the rotated matrix thoroughly for any overlapping of component factors.

Step 5. Check the validity and reliability of the component factors.

(a)

Perceptions of Sustainability in Urban Bridge Design Practice.

We investigated whether there was any smaller number of unobservable factors among the eight variables that measured perceptions of sustainability in urban bridge design for which data were available. A total of 15 initial items were identified to measure perceptions of sustainability. The intention was to assess whether there were any underlying dimensions. The seven excluded variables were unable to comply with the requirements identified in the EFA analysis.

The Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy is a statistic used to examine the appropriateness of factor analysis based on the sample of the study. Kaiser presented the range as follows: >0.9, marvelous; >0.8, meritorious; >0.7, middling; >0.6 mediocre; >0.5, miserable; and <0.5, unacceptable. Therefore, KMO = 0.811 (

Table 6. KMO and Bartlett’s test of sustainability perceptions.

KMO and Bartlett’s Test
Kaiser–Meyer–Olkin Measure of Sampling Adequacy		0.811
Bartlett’s Test of Sphericity	Approx. Chi-Square	992.002
	df	28
	Sig.	0.001

) is an acceptable sample size for EFA.

Bartlett’s test of sphericity tests the hypothesis of whether the population correlation matrix is an identity matrix [57,58]. The existence of an identity matrix puts the correctness of the factor analysis under suspicion. A population correlation matrix is not an identity matrix; p values less than 0.05 indicate that it is not an identity matrix. Table 6 shows a p value of 0.001, which signifies that the population correlation matrix is not an identity matrix, and EFA can be conducted.

EFA was conducted using the PCA method of extraction to accommodate the common and unique variances of the factors in a rotation method of Varimax, assuming orthogonal rotation would generate negligible correlations between factors [33,59,60]. Therefore, after many iterations of loading and excluding cross loadings and loadings of less than 0.5, a communality of factor extraction was observed, as shown in

Table 7. Factor communality of sustainability perception.

Communalities
	Initial	Extraction
Even if there is an increase in construction cost and time, I have noticed that my colleagues intend to incorporate sustainability in bridge design practice.	1.000	0.801
Even if there is an increase in construction cost and time, I have noticed that my clients intend to apply sustainable design principles, processes, and methods in projects.	1.000	0.697
Important for planners, architects, and engineers to be conscious that some of the designs they execute have an impact on the environment.	1.000	0.784
Important for planners, architects, and engineers to be conscious that some of the designs they execute have an impact on the economy.	1.000	0.809
Important for planners, architects, and engineers to be conscious that some of the designs they execute have an impact on society.	1.000	0.786
Important for planners, architects, and engineers to be conscious that some of the designs they execute have an impact on the institution’s performance.	1.000	0.829
Important for planners, architects, and engineers to be conscious that some of the designs they execute have an impact on technological usage.	1.000	0.708
I am aware that sustainability is getting more recognition among my colleagues and coworkers.	1.000	0.400
Extraction method: principal component analysis.

. Eight communalities were considered in the analysis.

The amount of variance in the study’s chosen variable that is connected to a factor is known as its eigenvalue. Elements with an eigenvalue of more than one were included in the model according to the eigenvalue criterion. Scree Plot: The eigenvalues and component (factor) numbers were plotted in the scree plot in the order of extraction [61]. The optimal number of elements to be preserved in the final solution was determined using the plot’s form. The goal of the scree plot is to visually distinguish an elbow [62], which is the location where the eigenvalues start to form a linear falling trend. Criteria for percentage of variance provides the proportion of variance that can be assigned to any factor in relation to the sum of the variances of all other factors.

The idea of the cumulative percentage of variance serves as the foundation for this strategy, i.e., the number of factors that the model should consider when the cumulative percentage of variation reaches an acceptable level. It is generally advised that components contributing between 60% and 70% of the variation be kept in the model [53,63].

The extracted factors of communality were investigated relative to the total variance explained (

Table 8. Total variance explained by sustainability perception.

Total Variance Explained
Component	Initial Eigenvalues			Extraction Sums of Squared Loadings			Rotation Sums of Squared Loadings
	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %
1	3.968	49.600	49.600	3.968	49.600	49.600	3.933	49.168	49.168
2	1.845	23.067	72.667	1.845	23.067	72.667	1.880	23.499	72.667
3	0.822	10.276	82.942
4	0.413	5.159	88.101
5	0.329	4.114	92.216
6	0.244	3.049	95.264
7	0.223	2.793	98.057
8	0.155	1.943	100.000
Extraction method: principal component analysis.

), showing that two components (component 1 and component 2) out of eight variables had an initial eigenvalue of more than one (1.0), and these components explained more than 72% of the total variance of factors.

A rotation is required because the original factor model may be mathematically correct but may be difficult in terms of interpretation. The interpretation is extremely difficult if various factors have high loadings on the same variable. Rotation solves this kind of interpretation difficulty. The main objective of rotation is to produce a relatively simple structure in which there may be a high factor loading on one factor and a low factor loading on all other factors. The rotated matrix of components of the perception of sustainability was extracted based on the orthogonal rotation (Varimax method); the findings are shown in

Table 9. Rotated component matrix of sustainability perception.

Rotated Component Matrix a
	Component
	1	2
Important for Planners, architects, and engineers to be conscious that some of the designs they execute have an impact on the institution’s performance.	0.910
Important for planners, architects, and engineers to be conscious that some of the designs they execute have an impact on the economy.	0.898
Important for planners, architects, and engineers to be conscious that some of the designs they execute have an impact on society.	0.885
Important for planners, architects, and engineers to be conscious that some of the designs they execute have an impact on the environment.	0.883
Important for planners, architects, and engineers to be conscious that some of the designs they execute have an impact on technological usage.	0.841
Even if there is an increase in construction cost and time, I have noticed that my colleagues intend to incorporate sustainability in bridge design practice.		0.892
Even if there is an increase in construction cost and time, I have noticed that my clients intend to apply sustainable design principles, processes, and methods in projects.		0.824
I am aware that sustainability is getting more recognition among my colleagues and coworkers.		0.627
Extraction method: principal component analysis. Rotation method: Varimax with Kaiser normalization.

^a. Rotation converged in three iterations.

.

The rotated loadings of factors (Table 9) were assigned to the two components with a loading of 0.5 or more, in agreement with the standard value [64]. Component 1 comprised five factors of loading from 0.841 to 0.910, whereas there were three factors of loading for component 2 in the range of 0.627 to 0.892.

Therefore, the hidden or latent variables that exist in the perception of sustainability application in urban bridge design are two unobserved variables. To confirm the component model, a test of reliability and validity of the component factors was conducted. The reliability statistics of the N = 8 items of factors resulted in a Cronbach’s alpha of 0.715, which is acceptable for the value of 0.7 as a standard value. With respect to validity, there are different approaches to testing the component model. Face validity is the face observation of the relationships of component factors with respect to the rotated component matrix. Convergent validity considers that the variables within a single factor are highly correlated [64]. This is evident by the factor loadings. The sample size of the dataset determines whether loadings are sufficient or substantial [65]. The factor loadings for N = 204 samples must be greater than 0.4; in this case, the factor loadings are greater than 0.6, as shown in Table 9.

The other type of validity that can be tested before component structure confirmation is discriminant validity, which refers to the extent to which factors are distinct and uncorrelated. It is recommended that variables have stronger relationships with their own factor than with another factor [66]. There are two main ways to assess discriminant validity during an EFA [67]. The first method is to examine the component or pattern matrix. Only one variable should be heavily weighted. If “cross loadings” do exist (variable loads on multiple factors), then they should differ by more than 0.2 [52,64]. Correlations between factors should not exceed 0.7 (49% of shared variance) [64]. The second method is to examine the component transformation matrix (

Table 10. Component transformation matrix of sustainability perception.

Component Transformation Matrix
Component	1	2
1	0.992	−0.128
2	0.128	0.992
Extraction method: principal component analysis. Rotation method: Varimax with Kaiser normalization.

). Correlations between factors should not exceed 0.7 (0.7 × 0.7 = 0.49 = 49% of shared variance) [68].

Table 10 shows that the correlation matrix of the components that load across the factors is 0.128 (25% shared variance), which is far below the 0.70 (49%) correlation coefficient, and that the component self-correlation is greater than the cross correlations (0.992 > 0.128). Therefore, the developed component matrix has proven to be a working model that can be used to identify the latent components of the perception of sustainability in the design of urban bridges in Ethiopia. The data, including the latent variables, can be reorganized as shown in Figure 6.

(b)

Issues Affecting Sustainable Design Practice of Urban Bridges.

The task was to identify whether there were any unobservable aspects among the eight measured variables affecting sustainability-related issues in urban bridge design for which data are available. A total of 12 initial parameters were chosen to measure how sustainability-related concerns were affecting society, with the purpose of determining whether there were any underlying dimensions. The four variables that were removed were unable to meet the criteria established by the EFA analysis.

A dataset can be considered for PCA analysis if its KMO (Kaiser–Meyer–Olkin) measure of sampling is adequate (>0.5) and if its Bartlett’s test of sphericity is significant (<0.05). The test for both requirements was acceptable, with a KMO of 0.797 and a significance of sphericity of 0.001 (

Table 11. KMO and Bartlett’s test of issues affecting sustainability.

KMO and Bartlett’s Test
Kaiser–Meyer–Olkin Measure of Sampling Adequacy.		0.797
Bartlett’s Test of Sphericity	Approx. Chi-Square	731.562
	df	28
	Sig.	0.001

).

EFA was conducted using the PCA method of extraction to accommodate the common and unique variances of the factors in a rotation method of Varimax, assuming orthogonal rotation would generate negligible correlations between factors. Therefore, after many iterations of loading and excluding the cross loadings and loadings with values of less than 0.5, a communality of factor extraction was observed, as shown in

Table 12. Factor communality of issues affecting sustainability.

Communalities
	Initial	Extraction
Lack of access to current and relevant information	1.000	0.632
Inadequate instructions about design input, process, and outputs	1.000	0.806
Lack of sustainable design codes and standards	1.000	0.481
Problems in understanding sustainable design processes and criteria	1.000	0.687
Lack of structured protocols or frameworks	1.000	0.601
Conventionality of the current design practice	1.000	0.712
Demands high effort	1.000	0.538
Inadequate current design facilities and techniques	1.000	0.802
Extraction method: principal component analysis

. Eight communalities were considered in the analysis.

The extracted factors of communality were investigated in the total variance explained (

Table 13. Total variance explained by issues affecting sustainability.

Total Variance Explained
Component	Initial Eigenvalues			Extraction Sums of Squared Loadings			Rotation Sums of Squared Loadings
	Total	% of Variance	Cumulative%	Total	% of Variance	Cumulative%	Total	% of Variance	Cumulative%
1	3.979	49.741	49.741	3.979	49.741	49.741	2.648	33.096	33.096
2	1.281	16.012	65.753	1.281	16.012	65.753	2.613	32.657	65.753
3	0.739	9.243	74.996
4	0.648	8.096	83.092
5	0.490	6.128	89.220
6	0.353	4.411	93.632
7	0.321	4.013	97.644
8	0.188	2.356	100.000
Extraction method: principal component analysis

), showing that two components (component 1 and component 2) out of eight variables have an initial eigenvalue of more than one (1.0) and that these components explained more than 65% of the total variance of factors.

The initial factor model may be mathematically sound, but it may be challenging to interpret, necessitating a rotation. Interpretation is very challenging if several factors have substantial loadings on the same variable. This kind of interpretational issue is resolved by rotation. The primary goal of rotation is to create a relatively basic structure in which one component may have a high factor loading while all other factors have a low factor loading. The rotated matrix of components affecting issues of sustainability was extracted based on the orthogonal rotations (Varimax method); the findings are shown in

Table 14. Rotated component matrix of issues affecting sustainability.

Rotated Component Matrix a
	Component
	1	2
Inadequate current design facilities and techniques	0.895
Inadequate instructions about design input, process, and outputs	0.826
Lack of access to current and relevant information	0.765
Lack of sustainable design codes and standards	0.591
Conventionality of the current design practice		0.825
Lack of structured protocols or frameworks		0.746
Problems in understanding sustainable design processes and criteria		0.738
Demands high effort		0.726
Extraction method: principal component analysis Rotation method: Varimax with Kaiser normalization

^a. Rotation converged in three iterations

.

The rotated loadings of factors (Table 14) were assigned to the two components with loadings of 0.5 and above, in agreement with the standard value [64]. Component one comprised four factors of loading from 0.594 to 0.895, whereas there were four factors of loading for component 2 in the range of 0.726 to 0.825.

As a result, the hidden or latent variables that exist in the issues of sustainability application in urban bridge design are two unobserved variables; a test of reliability and validity of the component factors was performed to confirm the component model. Reliability statistics of the N = 8 items of factors resulted in a Cronbach’s alpha of 0.851, which is acceptable, as it exceeds the minimum value of 0.7. Discriminant validity was also employed to test the component model using an examination of the component transformation matrix, as shown below (

Table 15. Component transformation matrix of issues affecting sustainability.

Component Transformation Matrix
Component	1	2
1	0.712	0.702
2	−0.702	0.712
Extraction method: principal component analysis Rotation method: Varimax with Kaiser normalization

). Correlations between factors should not exceed 0.7 (0.7 × 0.7 = 0.49 = 49% of shared variance) [68].

Table 15 shows that the correlation matrix of the components that load across the factors is 0.702 (49% shared variance), which nearly equal to the 0.70 (49%) correlation coefficient, and that the component self-correlation is greater than the cross correlations (0.712 > 0.702). Therefore, the developed component matrix has proven to be a working model that can be used to identify the latent components of the issues affecting sustainability in the design of urban bridges in Ethiopia. The data, including the latent variables, can be reorganized as shown in Figure 7.

(c)

Existing Obstacles for the Practice of Sustainability of Urban Bridge Design.

It is crucial to determine whether there are any fewer unobservable elements among the seven variables that quantify challenges to sustainability in urban bridge design. A total of 16 initial measurement items for sustainability barriers were found, with the goal of determining whether there were any underlying dimensions. Using EFA analysis, nine variables were eliminated because of low loadings, as well as the loading of two components and other factors.

Data can be considered for PCA analysis their KMO (Kaiser–Meyer–Olkin) measure of sampling is adequate (>0.5) and if its Bartlett’s test of sphericity is significant (<0.05). The test for both requirements was acceptable, with a KMO = 0.730 and a significance of sphericity of 0.001 (

Table 16. KMO and Bartlett’s test of sustainability obstacles.

KMO and Bartlett’s Test
Kaiser–Meyer–Olkin Measure of Sampling Adequacy.		0.730
Bartlett’s Test of Sphericity	Approx. Chi-Square	550.899
	df	21
	Sig.	0.000

).

Therefore, EFA was conducted using the PCA method of extraction to accommodate the common and unique variances of the factors in a rotation method of Varimax, assuming orthogonal rotation would generate negligible correlations between factors. Therefore, after many iterations of loading and excluding the cross loadings and loadings of less than 0.5, a communality of factor extraction was observed, as shown in

Table 17. Factor communality of sustainability obstacles.

Communalities
	Initial	Extraction
Lack of codes and standards	1.000	0.508
Perception of extra cost being incurred	1.000	0.748
Perception of extra time being incurred	1.000	0.764
Unwillingness to change the conventional way of designing	1.000	0.764
Lack of support from officials and/or politicians	1.000	0.731
Lack of professionals who are equipped with the concept	1.000	0.578
Uncertainty in the liability for final works	1.000	0.510
Extraction method: principal component analysis

. Seven communalities were considered in the analysis.

The extracted factors of communality were investigated in the total variance explained (

Table 18. Total variance explained by sustainability obstacles.

Total Variance Explained
Component	Initial Eigenvalues			Extraction Sums of Squared Loadings			Rotation Sums of Squared Loadings
	Total	% of Variance	Cumulative%	Total	% of Variance	Cumulative%	Total	% of Variance	Cumulative%
1	3.256	46.519	46.519	3.256	46.519	46.519	2.473	35.330	35.330
2	1.345	19.219	65.738	1.345	19.219	65.738	2.129	30.408	65.738
3	0.815	11.639	77.377
4	0.602	8.602	85.979
5	0.446	6.370	92.349
6	0.342	4.880	97.229
7	0.194	2.771	100.000
Extraction method: principal component analysis

), showing that two components (component 1 and component 2) out of seven variables had an initial eigenvalue of more than one (1.0) and that these components explained more than 65% of the total variance of the factors (Table 18).

Because the initial factor model could be technically sound but interpretively challenging, a rotation was necessary. Interpretation would be exceedingly challenging if several factors had substantial loadings on the same variable. The fundamental goal of rotation is to create a relatively simple structure in which one component may have a high factor loading and all other factors may have a low factor loading. The rotated matrix of components affecting issues of sustainability was extracted based on the orthogonal rotations (Varimax method); the findings are shown in

Table 19. Rotated component matrix of sustainability obstacles.

Rotated Component Matrix a
	Component
	1	2
Perception of extra time being incurred	0.868
Perception of extra cost being incurred	0.857
Lack of codes and standards	0.688
Uncertainty in the liability for final works	0.634
Lack of support from officials and/or politicians		0.852
Unwillingness to change the conventional way of designing		0.838
Lack of professionals who are equipped with the concept		0.732
Extraction method: principal component analysis Rotation method: Varimax with Kaiser normalization

^a. Rotation converged in three iterations

.

The rotated loadings of factors (Table 19) were assigned to the two components with a loading of 0.5 and above, in agreement with the standard value [64]. Component 1 comprised four factors of loading from 0.634 to 0.868, whereas there were three factor loadings for component 2 ranging from 0.732 to 0.852.

Therefore, the hidden or latent variables that exist in the existing obstacles of sustainability application in urban bridge design are two unobserved variables. To confirm the component model, a test of reliability and validity of the component factors was conducted. Reliability statistics of the N = 7 items of factors resulted in a Cronbach’s alpha of 0.805, which is acceptable, as it exceeds the minimum value of 0.7. Discriminant validity was also employed to test the validity of component models using an examination of the component transformation matrix (

Table 20. Component transformation matrix for the existing obstacles to sustainability.

Component Transformation Matrix
Component	1	2
1	0.768	0.640
2	−0.640	0.768
Extraction method: principal component analysis Rotation method: Varimax with Kaiser normalization

). Correlations between factors should not exceed 0.7 (0.7 × 0.7 = 0.49 = 49% of shared variance) [68].

Table 20 shows that the correlation matrix of the components that load across the factors is 0.640 (41% shared variance), which is less than the 0.70 (49%) correlation coefficient, and the component self-correlation is greater than the cross correlations (0.768 > 0.640). Therefore, the developed component matrix has proven to be a working model that can be used to identify the latent components of the existing obstacles to sustainability in the design of urban bridges in Ethiopia. The data, including the latent variables, can be reorganized as existing obstacles of sustainable urban bridge design, as shown in Figure 8.

4. Discussion

In this section, the perceptions, issues, and existing obstacles to sustainable urban bridge design in the context of the Ethiopian infrastructure delivery system are explained. The principal components found to impact the process of sustainable urban bridge design are discussed below to justify how these parameters are critical in practice.

4.1. Perceptions of Sustainability in Urban Bridge Design Practice

The opinions of design professionals on bridge design practice to meet the requirements of sustainability are deemed to be lacking in awareness and understanding of the design output impacts. The effect of creating awareness between stakeholders and institutions plays a role of prior relevance in sustainable design practice. Designers should recognize the effect of incorporating sustainability parameters. The perception that sustainable design necessitates high efficiency and extra time must be dispelled through appropriate fora and discussions. All stakeholders must give a reason to reconsider the efficiency of the design process in general.

Although the intent of the bridge design process is to create a safe bridge that satisfies all functional requirements at a cost that is acceptable to the owners, the demands of the present and future societies go far beyond that. The requirements that societies have for urban bridge design are gradually shifting toward novelty, harmony, personalization, and sustainability [69]. This will influence the perceptions of designers in response to urban demands [70].

The design process impacts the core values of the ecosystem. A full understanding requires consideration of institutional performance, social benefits, environmental protection, economic optimization, and adaptable technology. The design process depends on a holistic view of the criteria. The perceptions of the stakeholders towards sustainability, design objectives, and design problems are necessary throughout the design process.

The need for bridge access for future maintenance, the use of high-quality materials, longevity, and cost savings were found to often be factors that bridge designers considered more significant than sustainability [71]. Understanding the key issues that arise during the life cycle of bridges helps to enhance design and lifetime behavior, promoting the sustainability of bridge infrastructure [72]. By carefully considering recommendations to create awareness and design solutions to support the integration of sustainability concepts into implementation, this gap could be narrowed.

Awareness among all designers, the client, and the public plays a major role in design practice. A continuous campaign is also recommended to create a balanced view of the design process. Basic training on how to advance designers skills would play a variety of roles. The realization of sustainable urban bridge design is not easy; it requires committed action from all responsible bodies.

4.2. Issues Affecting Sustainable Design Practice for Urban Bridges

The issues that affect the application of sustainability in urban bridge design are the lack of design codes and standards and a lack of design protocols and frameworks. The current design facilities and techniques used for the design process are inadequate, indicating a way to comprehensively address the design objectives. The design instructions for design input, process, and output are not clear or sufficient. Issues in design assignment are exacerbated by a lack of access to current and relevant information via sustainable design codes and standards.

In terms of its capacity to arouse enthusiasm or address the significant concerns of sustainability, population, equity, and diversity, the urban and related design sector has reached its worst point [24]. In addressing the concept of sustainability to provide optimal urban infrastructure, adopted design philosophies lack a complete understanding of the urban region [73,74]. Contemporary urban infrastructure design, according to Sorkin (2013), is excessively “restrictive” and “boring”.

Necessary and important protocols and frameworks would change the current design practice to shift to sustainable design. Frameworks, design protocols, and guidelines would minimize the problems created by the gap in understanding of sustainable processes and criteria. Professionals’ understanding that sustainability requires significant effort must also be clarified and resolved using working systems such as protocols and frameworks.

The main issues affecting sustainable urban bridge design practice revolve around the lack of systematic working codes and standards. Implementation can also be guided by setting, assessment, and evaluation protocols. A more comprehensive mechanism can also be advanced by introducing a design framework. The growing complexity (stakeholder, economic, environmental, legal, resource, and infrastructure-related) and a sense of imminent urgency that we might be running out of time make the work of reconceptualizing the subject increasingly challenging [24].

4.3. Existing Obstacles to the Application of Sustainability to Urban Bridge Design

Understanding designers’ present sustainable design methods is essential, particularly the difficulties and roadblocks that could appear as they work to produce more sustainable results. The existing obstacles to attaining sustainability in bridge design are deemed to be incorrect perceptions of sustainability and a lack of support for sustainability practices. The perceptions among professionals about sustainability are that the concept demands extra time and cost while the design process is underway. However, sustainability facilitates and manages the design process in an efficient manner with minimal time [29]. The creation of awareness will be critical here because the challenge of practicing sustainable design will be ineffective unless it is understandable. The uncertainty of the final work has also been an existing obstacle to implementing the sustainable design of urban bridges. A professional’s unwillingness to shift from a conventional design paradigm to sustainable design practice is also a barrier to easily follow the new trend.

Regarding the conception, planning, and development of the built, infrastructural, operational, and functional forms of cities, current problems clearly call for an unprecedented paradigm shift to be disentangled and overcome. The new urban thinking is based on a holistic approach and long-term perspective. It is critical to first create, implement, and widely use innovative approaches and solutions for urban planning and development [75]. This necessitates solving comprehensive problems with urban infrastructure.

The lack of support for the provision of codes and standards has an impact on the delivery of sustainable infrastructure. Unless there is an established assessment and evaluation system for sustainability, the practice will be slow. The tools and rating mechanism are also critical for the practice. Additionally, the level of understanding and skill in implementing the concept is essential, together with proper support from officials and politicians who devise policies, rules, and guidelines. The institutional support and willingness of professionals to equip themselves with the concept would facilitate sustainability considerations in the design process.

A comprehensive understanding of the subject will help designers and consultants implement and improve their sustainable design practices and offer new information to software developers about the needs of the industry for more effective development of sustainable design tools [76]. To effectively support these activities, it is crucial to match these understandings with the potential and preparedness of digital technologies.

The current design practice of urban bridges in Ethiopia is at a critical juncture for the implementation of sustainability design concepts. This practice could be addressed by instituting a working support system for sustainability and raising career development awareness of emerging design approaches. Furthermore, the institutional level of preparedness in the launching, awarding, and monitoring of urban bridges plays a valuable role. Digital technologies play a significant part in the development of a more effective and responsible infrastructure to fulfill social, environmental, and economic needs, and techniques have evolved to become an innovative force throughout the lifespan of modern AEC projects [76].

5. Conclusions

The challenges associated with practicing sustainable urban bridge design lie in five principal factors: lack of awareness of design impacts on the ecosystem, a limited understanding of sustainability concepts, a lack of design codes of practice and standards, a lack of design protocols and frameworks, and a lack of support for sustainability practice. Challenges could be addressed through the collaboration of stakeholders such as the Ethiopian Roads Administration, consultants, and professionals. The public should also play a role in design consultations and public presentations by inquiring as to how the concept of sustainability is incorporated into the design objective. In the broader sense, the Ethiopian Ministry of Urban Development and Infrastructure should also set policies, strategies, and standards to achieve sustainable infrastructure through design.

The perspectives of design professionals on bridge design are inadequate in terms of awareness and comprehension of the effects of the design output. The perception that sustainable design necessitates high efficiency and extra time must be dispelled through appropriate fora and discussions. A continuous campaign is also recommended to create a balanced view of the design process. The fundamental issues affecting sustainable urban bridge design practice revolve around the lack of working codes and standards. The current design facilities and techniques used for the design process are inadequate. Professionals’ understanding that sustainability requires significant effort must also be clarified and resolved using guiding tools such as protocols and frameworks. The design practice of urban bridges in Ethiopia is at a critical juncture for implementation of sustainability design concepts. The existing obstacles to attaining sustainability in bridge design are deemed to be incorrect perceptions of sustainability and a lack of support for sustainability practices. Unless there is an established assessment and evaluation system, the practice will be slow. The digitization of the design process will also be a focal point of research in the Ethiopian AEC industry. The planning and design of critical infrastructure must consider both present and future societies throughout the built environment’s life cycle.