Search results

The combination of an Engineered Cementitious Composite (ECC) layer and steel plate to reinforce RC beams (ESRB) is a new strengthening method. The ESRB was proposed based on the steel plate at the bottom of RC beams, aiming to solve the problem of over-reinforced RC beams and improve the bearing capacity of RC beams without affecting their ductility.

In this paper, the finite element model of ESRB was established by ABAQUS. The results were compared with the experimental results of ESRB in previous studies and the reliability of the finite element model was verified. On this basis, parameters such as the width of the steel plate, thickness of the ECC layer, damage degree of the original beam and cross-sectional area of longitudinal tensile rebar were analyzed by the verified finite element model. Based on the load–deflection curve of ESRB, ESRB was discussed in terms of ultimate bearing capacity and ductility.

The results demonstrate that when the width of the steel plate increases, the ultimate load of ESRB increases to 133.22 kN by 11.58% as well as the ductility index increases to 2.39. With the increase of the damage degree of the original beam, the ultimate load of ESRB decreases by 23.7%–91.09 kN and the ductility index decreases to 1.90. With the enhancement of the cross-sectional area of longitudinal tensile rebar, the ultimate bearing capacity of ESRB increases to 126.75 kN by 6.2% and the ductility index elevates to 2.30. Finally, a calculation model for predicting the flexural capacity of ESRB is proposed. The calculated results of the model are in line with the experimental results.

Based on the comparative analysis of the test results and numerical simulation results of 11 test beams, this investigation verified the accuracy and reliability of the finite element simulation from the aspects of load–deflection curve, characteristic load and failure mode. Furthermore, based on load–deflection curve, the effects of steel plate width, ECC layer thickness, damage degree of the original beam and cross-sectional area of longitudinal tensile rebar on the ultimate bearing capacity and ductility of ESRB were discussed. Finally, a simplified method was put forward to further verify the effectiveness of ESRB through analytical calculation.

In recent decades, infrastructure has continued to develop as an important basis for social development and people's lives. Resource management of these large-scale projects has been immensely concerned because dozens of construction enterprises (CEs) often work together. In this situation, resource collaboration among enterprises has become a key measure to ensure project implementation. Thus, this study aims to propose a systematic multi-agent resource collaborative decision-making optimization model for large projects from a matching perspective.

The main contribution of this work was an advancement of the current research by: (1) generalizing the resource matching decision-making problem and quantifying the relationship between CEs. (2) Based on the matching domain, the resource input costs and benefits of each enterprise in the associated group were comprehensively analyzed to build the mathematical model, which also incorporated prospect theory to map more realistic decisions. (3) According to the influencing factors of resource decision-making, such as cost, benefit and attitude of decision-makers, determined the optimal resource input in different situations.

Numerical experiments were used to verify the effectiveness of the multi-agent resource matching decision (MARMD) method in this study. The results indicated that this model could provide guidance for optimal decision-making for each participating enterprise in the resource association group under different situations. And the results showed the psychological preference of decision-makers has an important influence on decision performance.

While the MARMD method has been proposed in this research, MARMD still has many limitations. A more detailed matching relationship between different resource types in CEs is still not fully analyzed, and relevant studies about more accurate parameters of decision-makers’ psychological preferences should be conducted in this area in the future.

Compared with traditional projects, large-scale engineering construction has the characteristics of huge resource consumption and more participants. While decision-makers can determine the matching relationship between related enterprises, this is ambiguous and the wider range will vary with more participants or complex environment. The MARMD method provided in this paper is an effective methodological tool with clearer decision-making positioning and stronger actual operability, which could provide references for large-scale project resource management.

Large-scale engineering is complex infrastructure projects that ensure national security, increase economic development, improve people's lives and promote social progress. During the implementation of large-scale projects, CEs realize value-added through resource exchange and integration. Studying the optimal collaborative decision of multi-agent resources from a matching perspective can realize the improvement of resource transformation efficiency and promote the development of large-scale engineering projects.

The current research on engineering resources decision-making lacks a matching relationship, which leads to unclear decision objectives, ambiguous decision processes and poor operability decision methods. To solve these issues, a novel approach was proposed to reveal the decision mechanism of multi-agent resource optimization in large-scale projects. This paper could bring inspiration to the research of large-scale project resource management.

In recent years, cost overrun becomes a common problem in steel building construction projects. The average percentage can vary widely depending on the project type, size, complexity and location. The steel structure change ratio in Taiwan is from 1 to 18% in statistics. The contractors always put every possible effort into preventing or mitigating project cost overruns, and one of the approaches is an accurate cost overrun risk estimate. Traditional project cost overrun risk assessment models mainly focus on macro-level evaluation and may not function well for the project-specific level (micro-level). This study creates a network-like connection model between the outcome (i.e. cost overrun risk) and the associated root causes in which the project status evaluation checklists of design, manufacturing, construction and interfaces are used to evaluate the checklists' influences through the Bayesian network (BN) composed by intermediate causes.

Due to the constraint of data availability, BN nodes, relationships and conditional probabilities are defined to establish a BN-based steel building project cost overrun assessment model following the knowledge of experts. Because of the complexity of the BN, the construction of the BN structure is first to build BN's fault tree (FT) hierarchy. And then, basic BN framework is constructed by the transformation of the FT hierarchy. Furthermore, some worthwhile additional arcs among BN nodes are inserted if necessary. Furthermore, conditional probability tables (CPTs) among BN nodes are explored by experts following the concept of the ranked node. Finally, the BN-based model was validated against the final cost analysis reports of 15 steel building projects done in Taiwan and both were highly consistent. The overall BN-based model construction process consists of three steps: (1) FT construction and BN framework transformation, (2) CPT computation and (3) model validation.

This study established a network-like bridge model between the outcome (i.e. cost overrun risk) and the root causes in a network of which cost influences are evaluated through the project-specific status evaluation checklists of design, manufacturing, construction and interfaces. This study overcame several limitations of the previous cost overrun risk assessment models: (1) few past research support assessment of cost overrun based on real-time project-owned data and (2) the traditional causal models inadequately depict interdependencies among influence factors of cost overrun at the network. The main influence factors of the cost overrun risk at the steel building projects in Taiwan were also examined using sensitivity analysis. The main root causes of cost overrun in steel building projects are design management and interface integration.

The proposed model belongs to the project-specific causal assessment model using real-time project-owned status checklist data as input. Such a model was seldom surveyed in the past due to the complicated interdependence among causes in the network. For practical use, a convenient and simple regression equation was also developed to forecast the cost overrun risk of the steel building project based on the root causes as input. Based on the analysis of cost overrun risk and significant influence factors, proper tailor-made preventive strategies are established to reduce the occurrence of cost overrun at the project.

Recently, the repairing of reinforced concrete (RC) structures attracted great research attentions, but the research interests were mainly concentrated on common repairing types. To this end, in this paper, a repairing of pre-loaded RC beams strengthened by aramid reinforcement polymers (AFRP) is presented. Furthermore, the purpose of this paper is to study the behavior of pre-loaded RC Deep beams under sustained load. The AFRP has many advantages such as controlling stresses distribution around the openings, controlling failure modes, and enhancing the structural capacity of pre-cracked RC beams.

Four specimens were experimentally tested: one specimen without strengthening, which is considered as control specimen, one strengthened specimen using AFRP without pre-cracking and two specimens subjected to pre-cracking load before prior to AFRP application. Furthermore, after validation of experimental data by using ANSYS software, a parametric study was conducted to investigate the effect of pre-damage level on shear capacity of RC beams. For pre-cracked beams, loading was first applied until the cracking stage, followed by specimen repairing with epoxy injection, and then the specimens were loaded again until failure point.

The result showed that pre-damage level and AFRP strengthening have great influence on the ultimate strength and failure mode. In addition, the results obtained from experimental tests were compared with those from numerical validation with ANSYS and showed good agreement.

Based on ACI guidelines, an analytical equation for calculating the shear strength of strengthened RC beams with openings subjected to pre-damage was then proposed, and the calculated results were compared with those from the tests, with differences not exceeding 10%.

The use of waste polyethylene terephthalate (PET) plastics derived from shredded bottles in concrete is not formalized yet, especially in reinforced members such as beams and columns. The disposal of plastic wastes in concrete is a viable alternative to manage those wastes while minimizing the environmental impacts associated to recycling, carbon dioxide emissions and energy consumption.

This paper evaluates the suitability of 2D deterministic and stochastic finite element (FE) modeling to predict the shear strength behavior of reinforced concrete (RC) beams without stirrups. Different concrete mixtures prepared with 1.5%–4.5% PET additions, by volume, are investigated.

Test results showed that the deterministic and stochastic FE approaches are accurate to assess the maximum load of RC beams at failure and corresponding midspan deflection. However, the crack patterns observed experimentally during the different stages of loading can only be reproduced using the stochastic FE approach. This later method accounts for the concrete heterogeneity due to PET additions, allowing a statistical simulation of the effect of mechanical properties (i.e. compressive strength, tensile strength and Young’s modulus) on the output FE parameters.

Data presented in this paper can be of interest to civil and structural engineers, aiming to predict the failure mechanisms of RC beams containing plastic wastes, while minimizing the experimental time and resources needed to estimate the variability effect of concrete properties on the performance of such structures.

Even though it is widely used, reinforced concrete (RC) is susceptible to damage from various environmental factors. The hazard of a fire attack is particularly severe because it may cause the whole structure to collapse. Furthermore, repairing and strengthening existing structures with high-performance concrete (HPC) has become essential from both technical and financial points of view. In particular, studying the postfire behavior of HPC with normal strength concrete substrate requires experimental and numerical investigations. Accordingly, this study aims to numerically investigate the post-fire behavior of reinforced composite RC slabs.

Consequently, in this study, a numerical analysis was carried out to ascertain the flexural behavior of simply supported RC slabs strengthened with HPC and exposed to a particularly high temperature of 600°C for 2 h. This behavior was investigated and analyzed in the presence of a number of parameters, such as HPC types (fiber-reinforced, 0.5% steel, polypropylene fibers [PPF], hybrid fibers), strengthening side (tension or compression), strengthening layer thickness, slab thickness, boundary conditions, reinforcement ratio and yield strength of reinforcement.

The results showed that traction-separation and full-bond models can achieve accuracy compared with experimental results. Also, the fiber type significantly affects the postfire performance of RC slab strengthened with HPC, where the inclusion of hybrid fiber recorded the highest ultimate load. While adding PPF to HPC showed a rapid decrease in the load-deflection curve after reaching the ultimate load.

The proposed model accurately predicted the thermomechanical behavior of RC slabs strengthened with HPC after being exposed to the fire regarding load-deflection response, crack pattern and failure mode. Moreover, the considered independent parametric variables significantly affect the composite slabs’ behavior.

This research paper aims to investigate reinforced concrete (RC) walls' behaviour under fire and identify the thermal and mechanical factors that affect their performance.

A three-dimensional (3D) finite element (FE) model is developed to predict the response of RC walls under fire and is validated through experimental tests on RC wall specimens subjected to fire conditions. The numerical model incorporates temperature-dependent properties of the constituent materials. Moreover, the validated model was used in a parametric study to inspect the effect of the fire scenario, reinforcement concrete cover, reinforcement ratio and configuration, and wall thickness on the thermal and structural behaviour of the walls subjected to fire.

The developed 3D FE model successfully predicted the response of experimentally tested RC walls under fire conditions. Results showed that the fire resistance of the walls was highly compromised under hydrocarbon fire. In addition, the minimum wall thickness specified by EC2 may not be sufficient to achieve the desired fire resistance under considered fire scenarios.

There is limited research on the performance of RC walls exposed to fire scenarios. The study contributed to the current state-of-the-art research on the behaviour of RC walls of different concrete types exposed to fire loading, and it also identified the factors affecting the fire resistance of RC walls. This guides the consideration and optimisation of design parameters to improve RC walls performance in the event of a fire.