Artificial intelligence (AI) models are computational models that use AI techniques like Artificial Neural Networks (ANNs) [
41]. The ANN consists of networks of an input layer, hidden layers, and an output layer, which are parallelly interconnected [
42].
2.3. Factors Included in Deterioration Models
2.3.1. Factors Considered in North America
Although some of the states used the same methods and types in developing the deterioration models, different factors were considered in each model, based on the local conditions. Age, average daily traffic (ADT), and average daily truck traffic (ADTT) were found to be the most common factors adopted in the deterioration models developed in North America [
8,
38,
42,
43,
44,
45,
46,
47,
48,
49,
50,
51,
52,
53,
54,
55,
56,
57,
58,
59,
60,
61,
62].
Madanat et al. [
44], Moomen et al. [
14], and Saeed et al. [
53] have developed different deterioration models for Indiana. The common factors found in the three studies were the following: age, ADT, state or non-state type of bridges, freeze and thaw cycles, number of spans, bridge length, skewness, and type of services under the bridge. Deck protection was adopted only by Moomen et al. [
14]. Madanat et al. [
44] were the only ones to consider the humidity, wearing surface type, bridge width, and structure type in their model.
In Ohio, Ramani [
56] considered geographical location and bridge material type, while AlThaqafi and Chou [
57] included skewness, ADT, design type, number of spans, bridge length, deck width, and design load in their model. The age of the bridge was the only common factor between the two models.
Winn [
42] and Chyad [
58] used ANN, deterministic, and stochastic approaches to develop a deterioration model in Michigan. Common factors used in these studies were age, ADT, and structure type.
Weissmann et al. [
60] developed a deterioration model in Texas utilizing inventory data, which considered climate factors such as rainfall. Other studies [
10,
59,
60,
61] have also considered the environmental effects on bridge deterioration when developing different deterioration models. Shengzhi et al. [
62] categorized the US into five zones: Hot-Humid, Mixed-Humid, Hot-Dry/Mixed-Dry, Cold/Very Cold, and Marine. There are several states that considered the effect of the environmental parameters in their deterioration models [
14,
43,
44,
48,
53,
54].
Foster [
63] conducted a survey of the state departments of transportation (DOTs) to investigate their experience with bridge deterioration models. Twenty-nine states responded, with seventeen states that have a deterioration model in their BMS and seven states that do not use bridge deterioration models in their BMS. The most common factors in the deterioration models reported were age, superstructure’s material, condition rating, average daily traffic (ADT), and deck wearing surface.
In Canada, Marcous [
45] considered highway class, region, age, ADT, and span length in the deterioration model. Morcous et al. [
55] included more factors in their model such as ADTT, skew angle, structural system, wearing surface, and girder material and spacings. Another study in Ontario by Martinez et al. [
59] was conducted to predict the condition rating of the province’s bridges using different types of models and a set of variables for the models. The set of variables includes rehabilitation time, number of spans, bridge length and width, region, age, and bridge material.
2.3.2. Factors Considered in Other Countries
To forecast the bridge condition rating using condition rating data, Khairullah and Roszilah [
64] developed an ANN deterioration model in Malaysia. Another study by Al Hussein [
61] utilized the inventory data of London bridges, where the defect type, exposure condition, and bridge component type were used as input parameters for the ANN deterioration model development.
A study in Japan was conducted by Miao [
65], where an ANN deterioration model was developed using age, ADT, ADTT, and bridge geometry as factors included in the model. The study also involved climate-related factors such as snow and rain precipitation, temperature change, chloride, and carbon dioxide content. The same environmental factors were used in a study in China but without considering the rain precipitation, chloride, and carbon dioxide content [
66].
Environmental factors were also considered in deterioration models in Australia. In addition to age, ADT, ADTT, and structure type were adopted by Callow [
11], and the chloride and sulphate content were included in the ANN deterioration model developed by Hasan [
67].
In Europe, a deterioration model was developed in Finland based on age, freeze–thaw cycles, carbonation depth, and reinforcement corrosion [
25]. In Serbia, the deterioration model included structural components and inspection gap [
68]. In the Netherlands, Kallen and Noortwijk [
69] considered age and condition data of the bridges to develop a Markov deterioration model.
Santos et al. [
70] in Brazil categorized the bridge data according to geographic location, material type, and superstructure type to develop Markovian and ANN deterioration models. For each bridge category, age, deck width, and bridge length were considered as parameters in both models. Another study in Brazil was conducted by Furtado and Ribeiro [
71] in which they used age, bridge length, number of spans, bridge material, traffic volume, and different classes of environmental aggressiveness as variables for the semi-Markovian model. A stochastic model that considered age and environmental conditions was also developed in Japan [
72].
2.3.3. Discussion of the Factors
Figure 1 summarizes the thirty-three factors found, and these could be classified into six groups; environmental factors, structural-related factors, dimensional factors, factors related to geographic location, traffic, and other factors. In addition, the structural-, traffic-, and dimensional-related factors are the most included in the development of deterioration models. Figure 2 shows several structural-related factors which were included in different deterioration models. The age of the bridge and the superstructure type are the main factors that were included in the development of DMs. Furthermore, snow, rain, temperature, humidity, freeze index, number of freeze–thaw cycles, number of cold days, carbon dioxide, chloride, sulphate, and salt usage are some of the environmental conditions that are included in DMs, as shown in Figure 3.
Figure 1. Categories adopted in the development of DMs.
Figure 2. Structural-related factors adopted in the development of DMs.
Figure 3. Environmental factors adopted in the development of DMs.
Traffic, dimensional factors, geographic location factors, and other categories are summarized in Table 2. In addition, the table provides the frequency of using each factor and relevant references that considered different factors. Some factors have been rarely adopted by researchers when developing deterioration models. Examples of these factors include deck and steel reinforcement protection, type of defect, inspection gap, bridge elevation, girder material and spacing, approach surface type, sulphate and carbon dioxide content, and salt usage.
Table 2. Summary of other factors included in different DMs.
Category |
Factor |
No of Times Mentioned |
References |
Dimensional factors |
Bridge elevation |
1 |
[65] |
Span length |
8 |
[38,44,45,46,55,73,74,75] |
No of spans |
15 |
[3,10,14,38,42,44,49,53,57,59,67,71,76,77,78] |
Bridge width |
13 |
[3,38,44,46,57,59,65,66,67,70,74,75,79,80] |
Bridge length |
18 |
[3,10,14,46,47,49,53,57,59,65,67,70,71,74,75,76,78,81] |
Factors related to geographic location |
Services under the bridge |
6 |
[11,14,48,53,60,78] |
State or interstate |
6 |
[8,14,44,51,53,78] |
Region or location |
7 |
[48,50,54,55,56,70,76] |
Traffic |
ADT |
31 |
[3,8,10,11,38,42,43,44,45,46,47,48,49,50,51,52,55,57,58,60,65,67,70,71,73,74,75,76,77,79,80,82] |
ADTT |
18 |
[3,10,11,14,42,46,51,52,53,54,55,65,67,70,71,73,77,78] |
Others |
Wearing surface type |
5 |
[8,44,49,52,55] |
Time of rehabilitation |
5 |
[43,59,60,76,82] |
Inspection gap |
3 |
[3,47,68] |
Defect type |
2 |
[60,83] |
It can be recognized that the development of deterioration models depends on different factors, based on the available data. The majority of the BMSs within the GCC are still under development and many of them do not include well-defined deterioration models. Therefore, identifying the most important factors that can impact bridge deterioration within the GCC can help the local agencies in the planning and development of their BMSs.
The concrete bridge’s structural deterioration can be affected by several factors. Such factors include, but are not limited to, environmental conditions, structural systems, bridge configuration, geographic location, traffic data, etc. These factors differ in their level and impact on the bridges within different regions or countries. Therefore, a bridge deterioration model should be different for various geographic locations.