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Dr. M.L. Nehdi, P.Eng. is Professor at Western University, Canada. He was Technical Director for Imasco Minerals, BCS, and MTL. He is a Fellow of ACI, CSCE, and EIC, and is a recipient of several awards, including PEO’s R&D Medal, CSCE’s Horst Leipholz Medal, CSCE’s Whitman Wright Award, ICE's Bill Curtin Medal, Ontario Premier’s Research Excellence Award, and an ACI Member Award for Professional Achievement. He is a prolific author with more than 400 publications, and is ranked among the world’s most cited civil engineers.
Infiltration of groundwater through reinforced concrete pipe (RCP) joints under hydrostatic pressure has been a major costly challenge in municipal sewer network systems. Analysis of an exclusive designwise infiltration test data of RCP joints showed that conventional regression analysis failed to produce reliable predictions. Accordingly, tree-based machine-learning techniques including random forest, extra trees, and gradient boosting classifiers have been deployed in this study to create reliable models. A large designwise data set identifying failure of RCP joints and the effect of key design parameters was collected using a novel experimental program. Due to the resulting unbalanced experimental data set, oversampling techniques including synthetic minority over-sampling technique (SMOTE) and density based synthetic minority over-sampling technique (DBSMOTE) were employed to enhance predictive performance. Gradient boosting coupled with DBSMOTE offered a robust machine-learning model for predicting RCP joint hydrostatic infiltration. The hybrid gradient boosting classification (GBC)-DBSMOTE model achieved superior predictive accuracy in terms of several classification indicators, with promising capability to create RCP joint hydrostatic infiltration performance charts that capture the effects of key design parameters, such as pressure duration and level, pipe size, and gasket sealing. The robust predictive model could produce design charts that aid municipalities in proactively averting sewage system infiltration problems at low cost, instead of the prevailing reactive approach to this problem.
Lui S. Wong; Afshin Marani; Moncef L. Nehdi. Gradient Boosting Coupled with Oversampling Model for Prediction of Concrete Pipe-Joint Infiltration Using Designwise Data Set. Journal of Pipeline Systems Engineering and Practice 2021, 12, 04021015 .
AMA StyleLui S. Wong, Afshin Marani, Moncef L. Nehdi. Gradient Boosting Coupled with Oversampling Model for Prediction of Concrete Pipe-Joint Infiltration Using Designwise Data Set. Journal of Pipeline Systems Engineering and Practice. 2021; 12 (3):04021015.
Chicago/Turabian StyleLui S. Wong; Afshin Marani; Moncef L. Nehdi. 2021. "Gradient Boosting Coupled with Oversampling Model for Prediction of Concrete Pipe-Joint Infiltration Using Designwise Data Set." Journal of Pipeline Systems Engineering and Practice 12, no. 3: 04021015.
Accurate prediction of the shear capacity of reinforced concrete shear walls (RCSW) is essential for the wind and seismic design of buildings. However, due to the diverse structural configurations, multitude of load scenarios, and highly nonlinear relations between the design parameters and the shear load capacity, this prediction is very complex. Existing pertinent design code provisions such as the American Concrete Institute ACI-318 and the Eurocode rely on empirical expressions that have various limitations and attain low predictive accuracy. Hence, in this paper, we pioneer a novel hybrid intelligent model to predict the ultimate shear capacity of RCSW. The support vector regression (SVR) and response surface model (RSM) were coupled based on two calibrating strategies in a novel hybrid modelling approach called RSM-SVR. The accuracy, tendency and uncertainty of the proposed SVR-RSM model along with that of three existing empirical relations and two design code provisions were assessed using various statistical metrics based on a comprehensive experimental database retrieved from the open literature. The existing design codes and empirical models were found to be inflicted with high variability and did not capture the influence of the key design parameters on the shear capacity in a robust and rational manner. Conversely, it is shown that the proposed RSM-SVR modeling approach achieved superior accurate predictions for the shear strength of RCSW. The proposed RSM-SVR model enhanced RMSE for the training (testing) dataset by 510% (150%) compared to the Baghi et al. model, 550% (190%) compared to the ACI 318-14 design code, 530% (155%) compared to the Chandra et al. model, 320% (145%) compared to the RSM model, and 450% (90%) compared to the SVR model. The novel approach also better captured the influence of the key design parameters, demonstrating robust tendency and much lower uncertainty. Thus, the proposed novel model could be harvested in intelligent generative design and for the enhancement of pertinent provisions in design codes. The proposed method achieves outstanding performance, while maintaining superior computational efficiency and low run time.
Behrooz Keshtegar; Moncef L. Nehdi; Nguyen-Thoi Trung; Reza Kolahchi. Predicting load capacity of shear walls using SVR–RSM model. Applied Soft Computing 2021, 112, 107739 .
AMA StyleBehrooz Keshtegar, Moncef L. Nehdi, Nguyen-Thoi Trung, Reza Kolahchi. Predicting load capacity of shear walls using SVR–RSM model. Applied Soft Computing. 2021; 112 ():107739.
Chicago/Turabian StyleBehrooz Keshtegar; Moncef L. Nehdi; Nguyen-Thoi Trung; Reza Kolahchi. 2021. "Predicting load capacity of shear walls using SVR–RSM model." Applied Soft Computing 112, no. : 107739.
Construction activities have been a primary cause for depleting natural resources and are associated with stern environmental impact. Developing concrete mixture designs that meet project specifications is time-consuming, costly, and requires many trial batches and destructive tests that lead to material wastage. Computational intelligence can offer an eco-friendly alternative with superior accuracy and performance. In this study, coal waste was used as a recycled additive in concrete. The flexural strength of a large number of mixture designs was evaluated to create an experimental database. A hybrid artificial neural network (ANN) coupled with response surface methodology (RSM) was trained and employed to predict the flexural strength of coal waste-treated concrete. In this process, four influential parameters including the cement content, water-to-cement ratio, volume of gravel, and coal waste replacement level were specified as independent input variables. The results show that concrete incorporating 3% recycled coal waste could be a competitive and eco-efficient alternative in construction activities while attaining a superior flexural strength of 6.7 MPa. The RSM-modified ANN achieved superior predictive accuracy with an RMSE of 0.875. Based on the experimental results and model predictions, estimating the flexural strength of concrete incorporating waste coal using the RSM-modified ANN model yielded superior accuracy and can be used in engineering practice to save the effort, cost, and material wastage associated with trial batches and destructive laboratory testing while producing mixtures with enhanced flexural strength.
Farshad Dabbaghi; Maria Rashidi; Moncef Nehdi; Hamzeh Sadeghi; Mahmood Karimaei; Haleh Rasekh; Farhad Qaderi. Experimental and Informational Modeling Study on Flexural Strength of Eco-Friendly Concrete Incorporating Coal Waste. Sustainability 2021, 13, 7506 .
AMA StyleFarshad Dabbaghi, Maria Rashidi, Moncef Nehdi, Hamzeh Sadeghi, Mahmood Karimaei, Haleh Rasekh, Farhad Qaderi. Experimental and Informational Modeling Study on Flexural Strength of Eco-Friendly Concrete Incorporating Coal Waste. Sustainability. 2021; 13 (13):7506.
Chicago/Turabian StyleFarshad Dabbaghi; Maria Rashidi; Moncef Nehdi; Hamzeh Sadeghi; Mahmood Karimaei; Haleh Rasekh; Farhad Qaderi. 2021. "Experimental and Informational Modeling Study on Flexural Strength of Eco-Friendly Concrete Incorporating Coal Waste." Sustainability 13, no. 13: 7506.
Self-compacting concrete (SCC) became a strong candidate for various construction applications owing to its excellent workability, low labor demand, and enhanced finish-ability, and because it provides a solution to the problem of mechanical vibration and related noise pollution in urban settings. However, the production of Portland cement (PC) as a primary constituent of SCC is energy-intensive, contributing to about 7% of global carbon dioxide (CO2) emissions. Conversely, the use of alternative geopolymer binders (GBs) in concrete can significantly reduce the energy consumption and CO2 emissions. In addition, using GBs in SCC can produce unique sustainable concrete with unparallel engineering properties. In this outlook, this work investigated the development of some eco-efficient self-compacting geopolymer concretes (SCGCs) obtained by incorporating different dosages of fly ash (FA) and ground blast furnace slag (GBFS). The structural, morphological, and mechanical traits of these SCGCs were examined via non-destructive tests like X-ray diffraction (XRD) and scanning electron microscopy (SEM). The workability and mechanical properties of six SCGC mixtures were examined using various measurements, and the obtained results were analyzed and discussed. Furthermore, an optimized hybrid artificial neural network (ANN) coupled with a metaheuristic Bat optimization algorithm was developed to estimate the compressive strength (CS) of these SCGCs. The results demonstrated that it is possible to achieve appropriate workability and mechanical strength through 50% partial replacement of GBFS with FA in the SCGC precursor binder. It is established that the proposed Bat-ANN model can offer an effective intelligent method for estimating the mechanical properties of various SCGC mixtures with superior reliability and accuracy via preventing the need for laborious, costly, and time-consuming laboratory trial batches that are responsible for substantial materials wastage.
Iman Faridmehr; Moncef Nehdi; Ghasan Huseien; Mohammad Baghban; Abdul Sam; Hassan Algaifi. Experimental and Informational Modeling Study of Sustainable Self-Compacting Geopolymer Concrete. Sustainability 2021, 13, 7444 .
AMA StyleIman Faridmehr, Moncef Nehdi, Ghasan Huseien, Mohammad Baghban, Abdul Sam, Hassan Algaifi. Experimental and Informational Modeling Study of Sustainable Self-Compacting Geopolymer Concrete. Sustainability. 2021; 13 (13):7444.
Chicago/Turabian StyleIman Faridmehr; Moncef Nehdi; Ghasan Huseien; Mohammad Baghban; Abdul Sam; Hassan Algaifi. 2021. "Experimental and Informational Modeling Study of Sustainable Self-Compacting Geopolymer Concrete." Sustainability 13, no. 13: 7444.
Explosive and impact events, which in recent years have inflicted colossal human and economic losses, are dire warnings that civil infrastructure is not immune to blast and explosion scenarios. Thus, designing resilient new civil infrastructure and retrofitting the existing one to enhance its blast resistance is paramount. Retrofitting reinforced concrete (RC) slabs using external fiber-reinforced polymer (FRP) is known to enhance blast resistance, mitigate displacements and cracking, and contain debris. However, existing approaches to simulate the structural response to blast loading require competent knowledge, substantial modeling efforts, and high computational cost. Therefore, this study investigates the practicality of deploying machine learning to predict the maximum displacement of FRP strengthened RC slabs under blast loading as a novel approach to achieve simplified and accurate predictions. A Gaussian process regression algorithm was implemented for model development considering several influential features of the application. Due to the limited pertinent data in the open literature, a novel approach based on Tabular Generative Adversarial Network (TGAN) was used to generate 200 additional synthetic data used for model training. A design parameter prediction model was also proposed to predict the cross-section of FRP retrofitting considering blast parameters and the displacement specified by the design code. Statistical performance metrics including MAE, MAPE, and R2 indicate that the developed model achieved predictions with superior accuracy. Feature importance analyses were also conducted and corroborated by existing experimental and numerical studies. Based on the proposed model and its validation and feature importance analyses, the implementation of ML was proven to be a viable approach for structural response predictions under blast loading. Thus, the proposed model can potentially provide designers with accurate results for FRP retrofitting of RC slabs against blast loading through a highly simplified approach at low computational cost.
Monjee K. Almustafa; Moncef L. Nehdi. Machine learning prediction of structural response for FRP retrofitted RC slabs subjected to blast loading. Engineering Structures 2021, 244, 112752 .
AMA StyleMonjee K. Almustafa, Moncef L. Nehdi. Machine learning prediction of structural response for FRP retrofitted RC slabs subjected to blast loading. Engineering Structures. 2021; 244 ():112752.
Chicago/Turabian StyleMonjee K. Almustafa; Moncef L. Nehdi. 2021. "Machine learning prediction of structural response for FRP retrofitted RC slabs subjected to blast loading." Engineering Structures 244, no. : 112752.
Considering its superior engineering properties, ultrahigh performance concrete (UHPC) has emerged as a strong contender to replace normal strength concrete (NSC) in diverse construction applications. While the mechanical properties of UHPC have been thoroughly explored, there is still dearth of studies that quantify the durability of UHPC, especially for sustainable mixtures made with local materials. Therefore, this research aims at investigating the alkali-silica reactivity (ASR) potential in sustainable UHPC in comparison with that of NSC. Sustainable UHPC mixtures were prepared using waste untreated coal ash (CA), raw slag (RS), and locally produced steel fibers. UHPC and benchmark NSC specimens were cast for assessing the compressive strength, flexural strength, and ASR expansion. Specimens were exposed to two curing regimes: accelerated ASR conditions (as per ASTM C1260) and normal water curing. UHPC specimens incorporating RS achieved higher compressive and flexural strengths in comparison with that of identical UHPC specimens made with CA. ASR expansion of control NSC specimens exceeded the ASTM C1260 limits (>0.20% at 28 days). Conversely, experimental results demonstrate that UHPC specimens incurred much less ASR expansion, well below the ASTM C1260 limits. Moreover, UHPC specimens incorporating steel fibers exhibited lower expansion compared to that of companion UHPC specimens without fibers. It was also observed that the mechanical properties of NSC specimens suffered more drastic degradation under accelerated ASR exposure compared to UHPC specimens. Interestingly, UHPC specimens exposed to accelerated ASR conditions attained higher mechanical properties compared to that of reference identical specimens cured in normal water. Therefore, it can be concluded that ASR exposure had insignificant effect on sustainable UHPC incorporating CA and RS, especially for specimens incorporating fibers. Results indicate that UHPC is a robust competitor to NSC for the construction of mega-scale projects where exposure to ASR conducive conditions prevails.
Safeer Abbas; Wasim Abbass; Moncef Nehdi; Ali Ahmed; Muhammad Yousaf. Investigation of Alkali-Silica Reactivity in Sustainable Ultrahigh Performance Concrete. Sustainability 2021, 13, 5680 .
AMA StyleSafeer Abbas, Wasim Abbass, Moncef Nehdi, Ali Ahmed, Muhammad Yousaf. Investigation of Alkali-Silica Reactivity in Sustainable Ultrahigh Performance Concrete. Sustainability. 2021; 13 (10):5680.
Chicago/Turabian StyleSafeer Abbas; Wasim Abbass; Moncef Nehdi; Ali Ahmed; Muhammad Yousaf. 2021. "Investigation of Alkali-Silica Reactivity in Sustainable Ultrahigh Performance Concrete." Sustainability 13, no. 10: 5680.
Traffic-flow modelling has been of prime interest to traffic engineers and planners since the mid-20th century. Most traffic-flow models were developed for the purpose of characterizing homogeneous traffic flow. Some of these models are extended to characterize the complex interactions involved in heterogeneous traffic flow. Existing heterogeneous traffic-flow models do not characterize the driver behavior leading to gap filling in heterogeneous traffic conditions. This study aimed at explaining the gap-filling behavior in heterogeneous traffic flow by using the effusion model of gas particles. The driver’s behavior leading to gap filling in heterogeneous traffic was characterized through developing analogies between the traffic flow and the Maxwell–Boltzmann equation for effusion of gases. This model was subsequently incorporated into the Payne–Whitham (PW) model by replacing the constant anticipation term. The proposed model was numerically approximated by using Roe’s scheme, and numerical simulation of the proposed model was then carried out by using MATLAB. The results of the proposed and PW models were therefore compared. It is concluded that the new model proposed in this study not only produces better results compared to the PW model, but also better captures the expected reality. The main difference between the behavior of the two models is that the effect of bottleneck in the density of traffic is propagated in the form of a shockwave travelling backwards in time in the new model, while the PW model does not exhibit this effect.
Muhammad Khan; Salman Saeed; Moncef Nehdi; Rashid Rehan. Macroscopic Traffic-Flow Modelling Based on Gap-Filling Behavior of Heterogeneous Traffic. Applied Sciences 2021, 11, 4278 .
AMA StyleMuhammad Khan, Salman Saeed, Moncef Nehdi, Rashid Rehan. Macroscopic Traffic-Flow Modelling Based on Gap-Filling Behavior of Heterogeneous Traffic. Applied Sciences. 2021; 11 (9):4278.
Chicago/Turabian StyleMuhammad Khan; Salman Saeed; Moncef Nehdi; Rashid Rehan. 2021. "Macroscopic Traffic-Flow Modelling Based on Gap-Filling Behavior of Heterogeneous Traffic." Applied Sciences 11, no. 9: 4278.
Eco-friendly and sustainable materials that are cost-effective, while having a reduced carbon footprint and energy consumption, are in great demand by the construction industry worldwide. Accordingly, alkali-activated materials (AAM) composed primarily of industrial byproducts have emerged as more desirable alternatives to ordinary Portland cement (OPC)-based concrete. Hence, this study investigates the cradle-to-gate life-cycle assessment (LCA) of ternary blended alkali-activated mortars made with industrial byproducts. Moreover, the embodied energy (EE), which represents an important parameter in cradle-to-gate life-cycle analysis, was investigated for 42 AAM mixtures. The boundary of the cradle-to-gate system was extended to include the mechanical and durability properties of AAMs on the basis of performance criteria. Using the experimental test database thus developed, an optimized artificial neural network (ANN) combined with the cuckoo optimization algorithm (COA) was developed to estimate the CO2 emissions and EE of AAMs. Considering the lack of systematic research on the cradle-to-gate LCA of AAMs in the literature, the results of this research provide new insights into the assessment of the environmental impact of AAM made with industrial byproducts. The final weight and bias values of the AAN model can be used to design AAM mixtures with targeted mechanical properties and CO2 emission considering desired amounts of industrial byproduct utilization in the mixture.
Iman Faridmehr; Moncef Nehdi; Mehdi Nikoo; Ghasan Huseien; Togay Ozbakkaloglu. Life-Cycle Assessment of Alkali-Activated Materials Incorporating Industrial Byproducts. Materials 2021, 14, 2401 .
AMA StyleIman Faridmehr, Moncef Nehdi, Mehdi Nikoo, Ghasan Huseien, Togay Ozbakkaloglu. Life-Cycle Assessment of Alkali-Activated Materials Incorporating Industrial Byproducts. Materials. 2021; 14 (9):2401.
Chicago/Turabian StyleIman Faridmehr; Moncef Nehdi; Mehdi Nikoo; Ghasan Huseien; Togay Ozbakkaloglu. 2021. "Life-Cycle Assessment of Alkali-Activated Materials Incorporating Industrial Byproducts." Materials 14, no. 9: 2401.
Ultrahigh-performance concrete (UHPC) is a novel material demonstrating superior mechanical, durability and sustainability performance. However, its implementation in massive structures is hampered by its high initial cost and the lack of stakeholders’ confidence, especially in developing countries. Therefore, the present study explores, for the first time, a novel application of UHPC, incorporating hybrid steel fibers in precast tunnel lining segments. Reduced scale curved tunnel lining segments were cast using UHPC incorporating hybrid 8 mm and 16 mm steel fibers at dosages of 1%, 2% and 3% by mixture volume. Flexural and thrust load tests were conducted to investigate the mechanical behavior of UHPC tunnel lining segments thus produced. It was observed that the flow of UHPC mixtures decreased due to steel fibers addition, yet steel fibers increased the mechanical and durability properties. Flexural tests on lining segments showed that both the strain hardening (multiple cracking) and strain softening (post-peak behavior) phases were enhanced due to hybrid addition of steel fibers in comparison with the control segments without fibers. Specimens incorporating 3% of hybrid steel fibers achieved 57% increase in ultimate load carrying capacity and exhibited multiple cracking patterns compared to that of identical UHPC segments with 1% fibers. Moreover, segments without fibers incurred excessive cracking and spalling of concrete at the base under the thrust load test. However, more stable behavior was observed for segments incorporating steel fibers under the thrust load, indicating its capability to resist typical thrust loads during tunnel lining field installation. This study highlights the potential use of UHPC with hybrid steel fibers for improved structural behavior. Moreover, the use of UHPC allows producing structural members with reduced cross-sectional dimensions, leading to reduced overall structural weight and increased clear space.
Safeer Abbas; Moncef Nehdi. Mechanical Behavior of Ultrahigh-Performance Concrete Tunnel Lining Segments. Materials 2021, 14, 2378 .
AMA StyleSafeer Abbas, Moncef Nehdi. Mechanical Behavior of Ultrahigh-Performance Concrete Tunnel Lining Segments. Materials. 2021; 14 (9):2378.
Chicago/Turabian StyleSafeer Abbas; Moncef Nehdi. 2021. "Mechanical Behavior of Ultrahigh-Performance Concrete Tunnel Lining Segments." Materials 14, no. 9: 2378.
Reinforced Concrete Pipe (RCP) is widely used in storm and wastewater management owing to its resiliency and reliability. This study proposes nonlinear 3D finite-element models (FEMs) to explore the effects of reinforcement configuration on RCP structural performance. RCP having 825-mm, 1200-mm, and 1800-mm in diameter with the three reinforcement configurations commonly used by industry, namely single-cage, double-cage, and triple-cage, were modelled for evaluating 65D, 100D, and 140D pipe design classes. FEM predicted load–deflection was validated using experimental results on full-scale RCP specimens. Average FEM prediction error of service and ultimate loads was 6.8% and 6.3%, respectively. FEM stress contours suggested agreement with experimental observations. The development of stress in the concrete and steel reinforcement during the there-edge bearing test (TEBT) was evaluated and discussed. A thorough parametric analysis was performed on developed single and double-cage FEMs and demonstrated that the influence of the reinforcement area, cover, positioning, and yield strength on RCP behavior could be rationally captured by the numerical models.
Abdul-Aziz Younis; Ahmed Shehata; Abdullah Ramadan; Lui Sammy Wong; Moncef L. Nehdi. Modeling structural behavior of reinforced-concrete pipe with single, double and triple cage reinforcement. Engineering Structures 2021, 240, 112374 .
AMA StyleAbdul-Aziz Younis, Ahmed Shehata, Abdullah Ramadan, Lui Sammy Wong, Moncef L. Nehdi. Modeling structural behavior of reinforced-concrete pipe with single, double and triple cage reinforcement. Engineering Structures. 2021; 240 ():112374.
Chicago/Turabian StyleAbdul-Aziz Younis; Ahmed Shehata; Abdullah Ramadan; Lui Sammy Wong; Moncef L. Nehdi. 2021. "Modeling structural behavior of reinforced-concrete pipe with single, double and triple cage reinforcement." Engineering Structures 240, no. : 112374.
Geopolymers and alkali-activated materials (AAM) have emerged as a promising sustainable alternative to ordinary Portland cement (OPC). Although much effort has recently been devoted to a wide range of research on the self-healing of cracks in OPC-based composites, little is known about the self-healing potential of AAMs. Therefore, this study explores the crack self-healing capability of NaOH-activated slag composites using a portfolio of testing methods, including electrical conductivity, mercury intrusion porosimetry, inductively coupled plasma optical emission spectroscopy, scanning electron microscopy (SEM) coupled with energy-dispersive X-ray spectroscopy, and X-ray microcomputed tomography. Experimental results indicate that alkali-activated slag-based composites incorporating calcium hydroxide achieved higher levels of self-healing than control specimens without calcium hydroxide. X-ray micro-computed tomography coupled with three-dimensional image analysis demonstrated that the observed self-healing was a surface mechanism that only occurred at surface cracks. Calcium carbonate was found to be the main self-healing product in all test specimens. Leaching experimental results indicated that the concentration of Ca2+ ions in the AAM matrix plays a critical role in calcium carbonate precipitation and, thus, in the self-healing potential.
L. V. Zhang; A. R. Suleiman; M. L. Nehdi. Crack Self-Healing in NaOH-Activated Slag-Based Composites Incorporating Calcium Hydroxide. Journal of Materials in Civil Engineering 2021, 33, 04021012 .
AMA StyleL. V. Zhang, A. R. Suleiman, M. L. Nehdi. Crack Self-Healing in NaOH-Activated Slag-Based Composites Incorporating Calcium Hydroxide. Journal of Materials in Civil Engineering. 2021; 33 (4):04021012.
Chicago/Turabian StyleL. V. Zhang; A. R. Suleiman; M. L. Nehdi. 2021. "Crack Self-Healing in NaOH-Activated Slag-Based Composites Incorporating Calcium Hydroxide." Journal of Materials in Civil Engineering 33, no. 4: 04021012.
Autoclaved aerated concrete (AAC) beams typically incur counter-arch deflection with stored stress induced by the high-temperature and pressure steam curing. This study investigates the effects of the resulting self-stress on the flexural performance of steel rebar reinforced AAC beams. Accordingly, a simplified experimental model was designed to decouple the bar self-stress from other load mechanisms that exist in full-scale structures. Surcharge loading was applied, and the self-stress of steel rebar was measured by the releasing method. The cracking pattern of the AAC test beams and the associated load-deflection curves were analyzed. The values of self-stress in different locations of the beam were obtained, and the influence of steel bar self-stress on the cracking load was calculated. The test results show that the cracking moment resisted by the self-stress accounted for 65.7% of the theoretical cracking moment. Numerical simulation results confirmed that asymmetric reinforced AAC beams incur a counter-arch phenomenon, and further quantified the corresponding effect of self-stress on the cracking load. Therefore, accurately accounting for the rebar self-stress can more accurately define the cracking load capacity of AAC beams and should be considered in design, which should make AAC beams a stronger contender in diverse field applications.
Chunyi Xu; Moncef L. Nehdi; Zanqing Wu; Jiaying Li. Effect of rebar self-stress on behavior of autoclaved aerated simply supported R/C thin beams subject to uniform transverse dead load. Engineering Structures 2021, 238, 112242 .
AMA StyleChunyi Xu, Moncef L. Nehdi, Zanqing Wu, Jiaying Li. Effect of rebar self-stress on behavior of autoclaved aerated simply supported R/C thin beams subject to uniform transverse dead load. Engineering Structures. 2021; 238 ():112242.
Chicago/Turabian StyleChunyi Xu; Moncef L. Nehdi; Zanqing Wu; Jiaying Li. 2021. "Effect of rebar self-stress on behavior of autoclaved aerated simply supported R/C thin beams subject to uniform transverse dead load." Engineering Structures 238, no. : 112242.
While research on self-healing of cement-based materials has recently gained considerable attention and made sizable progress, there is still ongoing debate and controversy regarding the effect of crack closing induced by autogenous self-healing on mechanical strength recovery. Despite that several techniques have been used to capture and quantify the self-healing of surface cracks, the resulting effect on mechanical strength has not, to date, been explored and quantified in a rigorous and systematic manner. Therefore, in this study, a broad array of multi-scale techniques including non-destructive shear wave velocity, high-resolution X-ray computed tomography (µCT), and 3D image analysis was deployed to examine the effects of autogenous crack self-healing on the mechanical strength recovery in various mortar specimens. The influence of microstructural changes induced by additives such as swelling compounds, silica-based additions, and carbonating minerals on strength recovery under diverse environmental exposures was further explored. The results capture the relationship between the crack closing mechanism imparted by self-healing and mechanical strength recovery, therefore elucidating the discrepancies in mechanical strength recovery results reported in the open literature.
A. R. Suleiman; M. L. Nehdi. Effect of autogenous crack self-healing on mechanical strength recovery of cement mortar under various environmental exposure. Scientific Reports 2021, 11, 1 -14.
AMA StyleA. R. Suleiman, M. L. Nehdi. Effect of autogenous crack self-healing on mechanical strength recovery of cement mortar under various environmental exposure. Scientific Reports. 2021; 11 (1):1-14.
Chicago/Turabian StyleA. R. Suleiman; M. L. Nehdi. 2021. "Effect of autogenous crack self-healing on mechanical strength recovery of cement mortar under various environmental exposure." Scientific Reports 11, no. 1: 1-14.
While recycled aggregates and supplementary cementitious materials have often been hailed for enhancing concrete sustainability, their effects on the resistance of concrete to carbonation has been controversial. Thus, deploying robust machine learning tools to overcome the lack of understanding of the implications of incorporating such sustainable materials is of paramount importance. Accordingly, this study proposes a gradient boosting regression tree (GBRT) model to determine the carbonation depth of recycled aggregate concrete incorporating different mineral additions, including metakaolin, blast furnace slag, silica fume, and fly ash. For this purpose, a database comprising 713 pertinent experimental data records was retrieved from peer-reviewed publications and used for model development and testing. Furthermore, predictions of the GBRT model were compared with calculations of available mathematical formulations to determine the carbonation depth in concrete. The results demonstrate that the machine learning methodology outperformed all the mathematical models considered in this study. The GBRT proved to be a robust tool that could be used to provide an insight into the resistance of concrete to carbonation and could be extended to predicting other features of concrete incorporating diverse recycled materials.
Itzel Nunez; Moncef L. Nehdi. Machine learning prediction of carbonation depth in recycled aggregate concrete incorporating SCMs. Construction and Building Materials 2021, 287, 123027 .
AMA StyleItzel Nunez, Moncef L. Nehdi. Machine learning prediction of carbonation depth in recycled aggregate concrete incorporating SCMs. Construction and Building Materials. 2021; 287 ():123027.
Chicago/Turabian StyleItzel Nunez; Moncef L. Nehdi. 2021. "Machine learning prediction of carbonation depth in recycled aggregate concrete incorporating SCMs." Construction and Building Materials 287, no. : 123027.
Ordinary Portland cement concrete (OPC) is the world’s most consumed commodity after water. However, the production of cement is a major contributor to global anthropogenic CO2 emissions. In recent years, ultrahigh performance concrete (UHPC) has emerged as a strong contender to replace OPC in diverse applications. UHPC has much higher mechanical strength, and thus less material is used in a structural member to resist the same load. Moreover, it has a much longer service life, reducing the long-term need for repair and replacement of aging civil infrastructure. Thus, UHPC can enhance the sustainability of cement and concrete. However, there is currently no robust tool to estimate the sustainability benefits of UHPC. This task is challenging considering that such benefits can only be captured over the long-term since variables, such as population growth and cement demand per capita, become more uncertain. In addition, the problem of CO2 emissions from cement and concrete is a complex system affected by time-dependent feedback. The System Dynamics (SD) method has specifically been developed for modeling such complex systems. Accordingly, a SD model was developed in this study to test various pertinent policy scenarios. It is shown that UHPC can reduce cumulative CO2 emissions of cement and concrete—over the studied simulation period—by more than 17%. If supplementary cementitious materials are further deployed in UHPC and new technologies permit reducing the carbon footprint per unit mass of cement, emission savings can become more substantial. The model offers a flexible framework where the user controls various inputs and can extend the model to account for new data, without the need for reconstruction of the entire model.
Mubashar Sheheryar; Rashid Rehan; Moncef Nehdi. Estimating CO2 Emission Savings from Ultrahigh Performance Concrete: A System Dynamics Approach. Materials 2021, 14, 995 .
AMA StyleMubashar Sheheryar, Rashid Rehan, Moncef Nehdi. Estimating CO2 Emission Savings from Ultrahigh Performance Concrete: A System Dynamics Approach. Materials. 2021; 14 (4):995.
Chicago/Turabian StyleMubashar Sheheryar; Rashid Rehan; Moncef Nehdi. 2021. "Estimating CO2 Emission Savings from Ultrahigh Performance Concrete: A System Dynamics Approach." Materials 14, no. 4: 995.
The complexity of shear transfer mechanisms in steel fiber-reinforced concrete (SFRC) has motivated researchers to develop diverse empirical and soft-computing models for predicting the shear capacity of SFRC beams. Yet, such existing methods have been developed based on limited experimental databases, which makes their generalization capability uncertain. To account for the limited experimental data available, this study pioneers a novel approach based on tabular generative adversarial networks (TGAN) to generate 2000 synthetic data examples. A “train on synthetic - test on real” philosophy was adopted. Accordingly, the entire 2000 synthetic data were used for training a genetic programming-based symbolic regression (GP-SR) model to develop a shear strength equation for SFRC beams without stirrups. The model accuracy was then tested on the entire set of 309 real experimental data examples, which thus far are unknown to the model. Results show that the novel GP-SR model achieved superior predictive accuracy, outperforming eleven existing equations. Sensitivity analysis revealed that the shear-span-to-depth ratio was the most influential parameter in the proposed equation. The present study provides an enhanced predictive model for the shear capacity of SFRC beams, which should motivate further research to effectively train evolutionary algorithms using synthetic data when acquiring large and comprehensive experimental datasets is not feasible.
Wassim Ben Chaabene; Moncef L. Nehdi. Genetic programming based symbolic regression for shear capacity prediction of SFRC beams. Construction and Building Materials 2021, 280, 122523 .
AMA StyleWassim Ben Chaabene, Moncef L. Nehdi. Genetic programming based symbolic regression for shear capacity prediction of SFRC beams. Construction and Building Materials. 2021; 280 ():122523.
Chicago/Turabian StyleWassim Ben Chaabene; Moncef L. Nehdi. 2021. "Genetic programming based symbolic regression for shear capacity prediction of SFRC beams." Construction and Building Materials 280, no. : 122523.
Strengthening of deficient reinforced concrete (RC) beams using near surface mounted (NSM) fiber-reinforced polymer (FRP) bars has in recent years been gaining greater interest and increased field applications. While considerable research has explored the behavior of NSM-FRP strengthened rectangular-section RC beams and the effects of influential parameters, there is dearth of similar studies on RC T-section beams. Moreover, analytical models for predicting the flexural strength of NSM strengthened RC beams are yet to be confirmed experimentally. Thus, the present study investigates the behavior of RC T-section beams strengthened with NSM FRP bars under monotonic flexural loading and compares the experimental results with predictions of a flexural model derived from first principles. Ten RC T-section beam specimens strengthened with NSM FRP bars and three standard specimens were considered. The failure mode, cracking resistance, yielding, ultimate capacity, flexural stiffness, and ductility of specimens were compared and analyzed. Based on the experimental results, a general increase in flexural stiffness of the strengthened specimens was observed, especially at the post yield stage of loading. Analytical flexural strength predictions were calculated and corroborated with the experimental results. An adjustment parameter to the flexural stiffness prediction model was proposed to account for reductions in the effective area of the FRP bars used in the sectional strength calculations.
Y. Zhang; M. Elsayed; L.V. Zhang; M.L. Nehdi. Flexural behavior of reinforced concrete T-section beams strengthened by NSM FRP bars. Engineering Structures 2021, 233, 111922 .
AMA StyleY. Zhang, M. Elsayed, L.V. Zhang, M.L. Nehdi. Flexural behavior of reinforced concrete T-section beams strengthened by NSM FRP bars. Engineering Structures. 2021; 233 ():111922.
Chicago/Turabian StyleY. Zhang; M. Elsayed; L.V. Zhang; M.L. Nehdi. 2021. "Flexural behavior of reinforced concrete T-section beams strengthened by NSM FRP bars." Engineering Structures 233, no. : 111922.
Accurate prediction of the ultimate shear capacity of reinforced concrete shear walls (RCSWs) is essential for robust design of buildings under seismic and wind loads. However, the shear capacity of RCSWs depends on multiple complex design variables characterized by diverse geometric and materials properties. Thus, a powerful modeling framework is required. In this paper, a hybrid artificial intelligence model is proposed for predicting the ultimate shear capacity of RCSWs named artificial neural network (ANN) coupled with adaptive harmony search optimization (AHS) algorithm. Different statistical metrics were used to compare the performances of the ANN model coupled with AHS (ANN-AHS) to three existing empirical relations and two ANN models combined with harmony search (ANN-HS) and global-best harmony search (ANN-GHS). Results show that the proposed ANN-AHS achieved superior performance in modelling the shear strength of RCSWs compared to ANN-HS and ANN-GHS models. The soft-computing models have proven to be more accurate than existing empirical relations.
Behrooz Keshtegar; Moncef L. Nehdi; Reza. Kolahchi; Nguyen-Thoi Trung; Mansour Bagheri. Novel hybrid machine leaning model for predicting shear strength of reinforced concrete shear walls. Engineering with Computers 2021, 1 -12.
AMA StyleBehrooz Keshtegar, Moncef L. Nehdi, Reza. Kolahchi, Nguyen-Thoi Trung, Mansour Bagheri. Novel hybrid machine leaning model for predicting shear strength of reinforced concrete shear walls. Engineering with Computers. 2021; ():1-12.
Chicago/Turabian StyleBehrooz Keshtegar; Moncef L. Nehdi; Reza. Kolahchi; Nguyen-Thoi Trung; Mansour Bagheri. 2021. "Novel hybrid machine leaning model for predicting shear strength of reinforced concrete shear walls." Engineering with Computers , no. : 1-12.
Using stainless steel (SS) reinforcement can mitigate colossal corrosion damage inflicted to reinforced concrete (RC) structures worldwide. However, there is still dearth of studies on the seismic behavior of SS-RC structures. Hence, quasi-static tests were carried out in this study to explore the seismic performance of three RC frame edge joint specimens reinforced with SS having strength grade of 500 and one control RC specimen made with grade 400 normal steel. RC edge frame joints reinforced with ordinary steel and SS exhibited similar bending-shear failure patterns at the beam root. The load bearing capacity of the SS-RC edge fame joint specimens was greater than that of the control ordinary steel specimen. SS-RC specimens BJD-1, BJD-2 and BJD-3 had 66.7%, 33.3% and 25% higher cracking load capacity than that of the control specimen BDJ-4, respectively. The yield load increased by 54.5%, 42.3% and 50.4%; while the ultimate load increased by 22.3%, 35.2% and 16.8%, respectively. The yield and ultimate displacements of the specimens were both larger, while the displacement ductility coefficient was smaller, than that of the control specimen. In addition, the energy dissipation and equivalent viscous damping coefficients of the SS reinforced specimens BJD-1, BJD-2 and BJD-3 in both the cracking and yield stages were all greater than that of the control specimen BDJ-4 but were slightly lower in the limit stage. Generally, SS-RC specimens met design code ductility requirements under earthquake loading, with adequate plastic deformation. A constitutive relationship for SS rebar was proposed in this study and used to conduct finite element simulations of the tested specimens. Good correlation between simulation and experimental results was observed. Thus, a parametric study was conducted to numerically investigate the influence of the axial compression, longitudinal and hoop reinforcement ratios on the seismic behavior of SS-RC joints. The findings could provide insight and guidance for future design provisions of concrete structures reinforced with stainless steel.
Chunyi Xu; Moncef L. Nehdi; Maged A. Youssef; Tao Wang; Lei V. Zhang. Seismic performance of RC beam-column edge joints reinforced with austenite stainless steel. Engineering Structures 2021, 232, 111824 .
AMA StyleChunyi Xu, Moncef L. Nehdi, Maged A. Youssef, Tao Wang, Lei V. Zhang. Seismic performance of RC beam-column edge joints reinforced with austenite stainless steel. Engineering Structures. 2021; 232 ():111824.
Chicago/Turabian StyleChunyi Xu; Moncef L. Nehdi; Maged A. Youssef; Tao Wang; Lei V. Zhang. 2021. "Seismic performance of RC beam-column edge joints reinforced with austenite stainless steel." Engineering Structures 232, no. : 111824.
Each year, about 730 million tons of bottom ash is generated in coal fired power plants worldwide. This by-product can be used as partial replacement for Portland cement, favoring resource conservation and sustainability. Substantial research has explored treated and processed coal bottom ash (CBA) for possible use in the construction industry. The present research explores using local untreated and raw CBA in mitigating the alkali–silica reaction (ASR) of reactive aggregates in concrete. Mortar bar specimens incorporating various proportions of untreated CBA were tested in accordance with ASTM C1260 up to 150 days. Strength activity index (SAI) and thermal analysis were used to assess the pozzolanic activity of CBA. Specimens incorporating 20% CBA achieved SAI greater than 75%, indicating pozzolanic activity. Mixtures incorporating CBA had decreased ASR expansion. Incorporating 20% CBA in mixtures yielded 28-day ASR expansion of less than the ASTM C1260 limit value of 0.20%. Scanning electron microscopy depicted ASR induced microcracks in control specimens, while specimens incorporating CBA exhibited no microcracking. Moreover, low calcium-to-silica ratio and reduced alkali content were observed in specimens incorporating CBA owing to alkali dilution and absorption, consequently decreasing ASR expansion. The toxicity characteristics of CBA indicated the presence of heavy metals below the US-EPA limits. Therefore, using local untreated CBA in concrete as partial replacement for Portland cement can be a non-hazardous alternative for reducing the environmental overburden of cement production and CBA disposal, with the added benefit of mitigating ASR expansion and its associated costly damage, leading to sustainable infrastructure.
Safeer Abbas; Uzair Arshad; Wasim Abbass; Moncef Nehdi; Ali Ahmed. Recycling Untreated Coal Bottom Ash with Added Value for Mitigating Alkali–Silica Reaction in Concrete: A Sustainable Approach. Sustainability 2020, 12, 10631 .
AMA StyleSafeer Abbas, Uzair Arshad, Wasim Abbass, Moncef Nehdi, Ali Ahmed. Recycling Untreated Coal Bottom Ash with Added Value for Mitigating Alkali–Silica Reaction in Concrete: A Sustainable Approach. Sustainability. 2020; 12 (24):10631.
Chicago/Turabian StyleSafeer Abbas; Uzair Arshad; Wasim Abbass; Moncef Nehdi; Ali Ahmed. 2020. "Recycling Untreated Coal Bottom Ash with Added Value for Mitigating Alkali–Silica Reaction in Concrete: A Sustainable Approach." Sustainability 12, no. 24: 10631.