Research

13 pages, 1555 KiB

Open AccessArticle

Shear Strength Prediction of Concrete Beams Reinforced with FRP Bars and Stirrups Using Gene Expression Programming

by Yasmin Murad, Eman Saleh, Ahmad Tarawneh, Ghassan Almasabha and Abdullah Alghossoon

Sustainability 2023, 15(8), 6335; https://doi.org/10.3390/su15086335 - 07 Apr 2023

Cited by 1 | Viewed by 1244

Existing reinforced concrete (RC) structures in humid regions suffer from deterioration due to the corrosion of ordinary reinforcement bars damaging the whole system. The deterioration of the transverse reinforcement leads to shear failure, which is one of the most dangerous failure modes. Therefore, [...] Read more.

Existing reinforced concrete (RC) structures in humid regions suffer from deterioration due to the corrosion of ordinary reinforcement bars damaging the whole system. The deterioration of the transverse reinforcement leads to shear failure, which is one of the most dangerous failure modes. Therefore, researchers suggested using fiber-reinforced polymer (FRP) bars as a replacement for reinforcement bars in humid regions to integrate sustainability and improve their serviceability and durability. A simple model that can accurately estimate the shear strength of concrete beams designed with FRP longitudinal bars and stirrups is lacking. This research proposed a simplified Gene expression programming (GEP) based model to estimate the shear strength of FRP concrete beams. Seven parameters that principally dominate the shear behavior of FRP beams were utilized to create the GEP model. The parameters are the beam width, beam depth, concrete compressive strength, FRP tensile longitudinal reinforcement ratio, area of stirrups, spacing between the stirrups, and the ultimate FRP strength of stirrups. A comparison was made between the GEP and ACI-440 models; the R² values of the total database were 92% and 54% for the GEP and ACI models, respectively. The R² of the GEP model is considerably higher than that measured for the ACI model, and the errors of the GEP model are low, which affirms that the GEP is superior to the ACI model in estimating the shear strength of FRP beams. The trends of the GEP and ACI-440 models and the empirical results are similar, confirming the GEP model’s consistency. Using the GEP model to estimate the shear strength of concrete beams designed with FRP longitudinal bars and stirrups is recommended. Full article

(This article belongs to the Special Issue Robust and Resilient Structures for Sustainable Development)

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16 pages, 544 KiB

Open AccessArticle

Assessment of Urban Sustainability—The Case of Amman City in Jordan

by Firas M. Sharaf

Sustainability 2023, 15(7), 5875; https://doi.org/10.3390/su15075875 - 28 Mar 2023

Cited by 2 | Viewed by 2227

Abstract

Accelerated urbanization causes an increasing number of city dwellers, insufficient and overburdened infrastructure and services, and negative environmental impacts and climate change impacts. Measuring the city’s progress toward sustainability is essential to support decision-making and policy development. This study aims to establish an [...] Read more.

Accelerated urbanization causes an increasing number of city dwellers, insufficient and overburdened infrastructure and services, and negative environmental impacts and climate change impacts. Measuring the city’s progress toward sustainability is essential to support decision-making and policy development. This study aims to establish an assessment and monitoring method of sustainable development goals at the city level, focusing on identifying indicators that are compatible with the city context to update and monitor progress toward sustainability. A review of the literature on sustainability assessment methods and tools is presented. A comprehensive framework for city sustainability assessment and a checklist of indicators. Amman city in Jordan is suggested. A Voluntary Local Review (VLR) report of Amman was presented to the United Nations in 2022. The report reviews Amman’s progress toward achieving the SDGs; however, it lacks clear and a quantitative assessment of the city’s sustainability, particularly SDG 11, which this paper seeks to address. The checklist survey questions were formulated according to the sub-indicators of the UN-Habitat SDG indicator metadata. The checklist was distributed to respondents from the Municipality of Amman and related organizations to the VLR. The respondents evaluated the sub-indicators of Goal 11 and gave performance level scores in three levels: low, average, and optimal. The sum of the indicator values is quantitatively presented in tables. The findings reveal that the indicator values of the city sustainability assessment framework, as applied in this paper, can be adjusted within the characteristics and constraints of the local context in a two-year observation period to provide updated data for decision-makers regarding the current status and future implementation of sustainability agendas. Full article

(This article belongs to the Special Issue Robust and Resilient Structures for Sustainable Development)

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13 pages, 3121 KiB

Open AccessArticle

Machine Learning-Based Flexural Capacity Prediction of Corroded RC Beams with an Efficient and User-Friendly Tool

by Abdelrahman Abushanab, Tadesse Gemeda Wakjira and Wael Alnahhal

Sustainability 2023, 15(6), 4824; https://doi.org/10.3390/su15064824 - 08 Mar 2023

Cited by 8 | Viewed by 3161

Abstract

Steel corrosion poses a serious threat to the structural performance of reinforced concrete (RC) structures. Thus, this study evaluates the flexural capacity of RC beams through machine learning (ML)-based techniques with six parameters used as input features: beam width, beam effective depth, concrete [...] Read more.

Steel corrosion poses a serious threat to the structural performance of reinforced concrete (RC) structures. Thus, this study evaluates the flexural capacity of RC beams through machine learning (ML)-based techniques with six parameters used as input features: beam width, beam effective depth, concrete compressive strength, reinforcement ratio, reinforcement yield strength, and corrosion level. Four single and ensemble ML models are evaluated; namely, decision tree, support vector machine, adaptive boosting, and gradient boosting. Hyperparameters of each model were optimized using grid search and K-fold cross-validation with root mean squared error used as the performance index. The predictive performance of each model was assessed using four statistical performance metrics. The analysis results demonstrated that the decision tree model exhibited overfitting and limited generalization ability. The adaptive boosting model also had a slight overfitting issue. In addition, the support vector machine reported comparable accuracy to that of adaptive boosting. Conversely, the proposed gradient boosting ensemble model achieved the best performance with strong generalization ability, as indicated by its lowest mean absolute error of 2.78 kN.m, mean absolute percent error of 13.40%, and root mean squared error of 3.56 kN.m, and the highest coefficient of determination of 97.30% on the test dataset. The optimized gradient boosting model has been deployed into a graphical user interface, allowing for practical implementation of the model and enabling fast, efficient, and intelligent prediction of the flexural capacity of corroded RC beams. Full article

(This article belongs to the Special Issue Robust and Resilient Structures for Sustainable Development)

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18 pages, 6979 KiB

Open AccessArticle

Machine Learning Prediction Model for Shear Capacity of FRP-RC Slender and Deep Beams

by Ahmad Tarawneh, Abdullah Alghossoon, Eman Saleh, Ghassan Almasabha, Yasmin Murad, Mahmoud Abu-Rayyan and Ahmad Aldiabat

Sustainability 2022, 14(23), 15609; https://doi.org/10.3390/su142315609 - 24 Nov 2022

Cited by 1 | Viewed by 1551

Abstract

FPR reinforcing bars have emerged as a promising alternative to steel bars in construction, especially in corrosive environments. Literature includes several shear strength models proposed for FRP-RC members. This study presents a detailed evaluation of design shear models proposed by researchers and design [...] Read more.

FPR reinforcing bars have emerged as a promising alternative to steel bars in construction, especially in corrosive environments. Literature includes several shear strength models proposed for FRP-RC members. This study presents a detailed evaluation of design shear models proposed by researchers and design codes. The evaluation was conducted through an extensive surveyed database of 388 FRP-RC beams without shear reinforcement tested in shear. Gene expression programming (GEP) has been utilized in this study to develop accurate design models for the shear capacity of slender and deep FRP-RC beams. Parameters used in the models are concrete compressive strength (f’_c), section depth (d), section width (b), modular ratio (n), reinforcement ratio (ρ_f), shear span-to-depth ratio (a/d). The proposed model for slender beams resulted in an average tested-to-predicted ratio of 0.98 and a standard deviation of 0.21, while the deep beams model resulted in an average tested-to-predicted ratio of 1.03 and a standard deviation of 0.29. For deep beams, the model provided superior accuracy over all models. However, this can be attributed to the fact that the investigated models were not intended for deep beams. The deep beams model provides a simple method compared to the strut-and-tie method. Full article

(This article belongs to the Special Issue Robust and Resilient Structures for Sustainable Development)

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17 pages, 2713 KiB

Open AccessArticle

Robust Prediction of Shear Strength of SFRC Using Artificial Neural Networks

by Ruba Odeh and Roaa Alawadi

Sustainability 2022, 14(14), 8516; https://doi.org/10.3390/su14148516 - 12 Jul 2022

Viewed by 1101

Abstract

The assessment of shear behavior in SFRC beams is a complex problem that depends on several parameters. This research aims to develop an artificial neural network (ANN) model that has six inputs nodes that represent the fiber volume (V_f), fiber [...] Read more.

The assessment of shear behavior in SFRC beams is a complex problem that depends on several parameters. This research aims to develop an artificial neural network (ANN) model that has six inputs nodes that represent the fiber volume (V_f), fiber factor (F), shear span to depth ratio (a/d), reinforcement ratio (ρ), effective depth (d), and concrete compressive strength (

f_{c}^{'}

) to predict shear capacity of steel fiber-reinforced concrete beams, using 241 data test gathered from previous researchers. The proposed ANN model provides a good implementation and superior accuracy for predicting shear strength compared to previous literature, with a Root Mean Square Error (RMSE) of 0.87, the average ratio (v_test/v_predicted) of 1.00, and the coefficient of variation of 22%. It was shown from parametric analysis the reinforcement ratio and shear span to depth ratio contributed the most impact on the shear strength. It can also be noticed that all parameters have a nearly linear impact on the shear strength except the shear span to depth ratio has an exponential effect. Full article

(This article belongs to the Special Issue Robust and Resilient Structures for Sustainable Development)

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20 pages, 2885 KiB

Open AccessArticle

Investigation of Resilience Characteristics of Unbound Granular Materials for Sustainable Pavements

by Salamat Ullah, Arshad Jamal, Meshal Almoshaogeh, Fawaz Alharbi and Jawad Hussain

Sustainability 2022, 14(11), 6874; https://doi.org/10.3390/su14116874 - 04 Jun 2022

Cited by 5 | Viewed by 1691

Abstract

In this study, a comprehensive laboratory testing program was designed to study the resilience characteristics of unbound granular materials (aggregate base coarse) using the repeated load triaxial test (RLTT). During the experimental program, the resilient modulus of unbound granular material was examined using [...] Read more.

In this study, a comprehensive laboratory testing program was designed to study the resilience characteristics of unbound granular materials (aggregate base coarse) using the repeated load triaxial test (RLTT). During the experimental program, the resilient modulus of unbound granular material was examined using different moisture content levels, material gradation using Fuller’s equation, and stress levels. The results show that the moisture content, material gradation, and stress level have a major influence on the resilient modulus of unbound granular materials. Furthermore, a linear model has been developed between moisture content and the resilient modulus. The model significantly predicts the change in resilient modulus by changing moisture content. The study also aimed to improve the modified Uzan model by adding the effect of moisture content. An improved modified Uzan stress moisture model has been developed, which shows a strong relationship between the resilient modulus, stress, and moisture content. This study can be used as a benchmark for validating other numerical data. Full article

(This article belongs to the Special Issue Robust and Resilient Structures for Sustainable Development)

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25 pages, 3003 KiB

Open AccessArticle

Prediction of Bidirectional Shear Strength of Rectangular RC Columns Subjected to Multidirectional Earthquake Actions for Collapse Prevention

by Yingbo Pang, Iftikhar Azim, Momina Rauf, Muhammad Farjad Iqbal, Xinguang Ge, Muhammad Ashraf, Muhammad Atiq Ur Rahman Tariq and Anne W. M. Ng

Sustainability 2022, 14(11), 6801; https://doi.org/10.3390/su14116801 - 02 Jun 2022

Cited by 1 | Viewed by 1340

Abstract

The understanding of the effects of multidirectional loadings imposed on major load bearing elements such as reinforced concrete (RC) columns by seismic actions for collapse prevention is of utmost importance, and a few simplified models are available in the literature. In this study, [...] Read more.

The understanding of the effects of multidirectional loadings imposed on major load bearing elements such as reinforced concrete (RC) columns by seismic actions for collapse prevention is of utmost importance, and a few simplified models are available in the literature. In this study, the distinguishing features of two machine-learning (ML) methods, namely, multi expression programming (MEP) and adaptive neuro-fuzzy inference system (ANFIS) are exploited for the first time to develop eight novel prediction models (M1-to M4-MEP and M1-to M4-ANFIS) with different combinations of input parameters to predict the biaxial shear strength of RC columns (V). The performance of the developed models was assessed using various statistical indicators and by comparing them with the experimental values. Based on the statistical analysis of the developed models, M1-ANFIS and M1-MEP performed very well and exhibited the best overall efficiency of the studied ML methods. Simple mathematical formulations were also provided by the MEP algorithm for the prediction of V, using which the M1-MEP model was finalized based on its performance, accuracy, and generalization capability. A parametric analysis was also performed for the model to show that the mathematical formulation provided by MEP accurately represents the system under consideration and is imperative for prediction purposes. Based on its performance, the model can thus be recommended to update the current code provisions and engineering practices. Full article

(This article belongs to the Special Issue Robust and Resilient Structures for Sustainable Development)

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17 pages, 1357 KiB

Open AccessArticle

Exploring Implementation of Blockchain for the Supply Chain Resilience and Sustainability of the Construction Industry in Saudi Arabia

by Naif Al Azmi, Ghaleb Sweis, Rateb Sweis and Farouq Sammour

Sustainability 2022, 14(11), 6427; https://doi.org/10.3390/su14116427 - 24 May 2022

Cited by 19 | Viewed by 3889

Abstract

The construction industry plays an essential role in economic development since it is one of the largest industries all over the world. Blockchain has the potential to reshape the structure of all accessible networks in the future. Construction businesses are increasingly interested in [...] Read more.

The construction industry plays an essential role in economic development since it is one of the largest industries all over the world. Blockchain has the potential to reshape the structure of all accessible networks in the future. Construction businesses are increasingly interested in embracing blockchain technology to improve supply chain sustainability performance and supply chain resilience in times of globally increasing risks and volatility. This study evaluates the readiness of actors involved in the producing of concrete goods to emphasize the necessity to bring blockchain into the construction industry, as it may be a solution for supply chain resilience and long-term sustainable growth. Qualitative and quantitative research methods were used in collecting and analyzing the data and testing the hypotheses. Data were collected using an online questionnaire distributed to 300 employees who work within the biggest concrete producing companies in Saudi Arabia. 120 respondents completed the questionnaires. Additionally, confirmatory semi-structured interviews with experts in supply chain financing, IT departments, and procurement departments have been conducted; the study’s findings revealed a low level of blockchain knowledge within Saudi Arabia’s construction industry, since (90%) of respondents have not worked with Blockchain technology. Several technologists barely understand it, and the level of readiness is very low. However, there is a lot of potential, and it is worth investing in it combined with other technologies such as BIM technology. In this study, the authors have sought to provide a measure of Saudi professionals’ attitudes and understanding of blockchain technology solutions within the construction industry in Saudi Arabia. Furthermore, the study’s novelty aimed to provide a grasp of the conceptual, theoretical, and fundamental features of blockchain technology in the construction industry, as blockchain solutions could benefit the global economy by increasing levels of monitoring, tracing, and confidence in international supply chain resilience. Full article

(This article belongs to the Special Issue Robust and Resilient Structures for Sustainable Development)

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