Civil Infrastructure Projects Research Articles

Dynamic Monitoring and Analysing Method of Construction Project Performance Based on Digital Twin

Higher-level management and monitoring systems must be developed to maintain increasing global civil infrastructure projects. One such innovation in the construction industry is the Digital Twin (DT), which helps businesses address the evolving needs for smart city development and infrastructure growth through enhanced project management and execution. In this paper, we introduce a DT framework that will digitally describe construction areas in real-time through the use of Internet of Things (IoT) sensors, Building Information Modelling (BIM), and machine learning (ML). The computer program periodically collects and analyses data using sensors associated with the environment along with 3D imaging sensors (LiDAR, photogrammetric cameras). More Effective project management and monitoring are the result of linking this data with BIM systems, allowing for reports on the construction development. In order to promote proactive management, this system uses ML algorithms to analyze patterns and predict future risks. For superior prediction and decision-making by the DT, this system's dual-input layout processes both spatial and temporal data. The framework enables a method to collect data in real time and use them to achieve early detection of issues, improved resource management, and enhanced project outcomes.

Integrated approach for sustainability assessment and reporting for civil infrastructures projects: Delivering the UN SDGs

Large civil infrastructure projects, such as airports, highways, railways, townships, and other developments, are critical for contributing to the social, economic, and environmental pillars of sustainability along with the UN-SDGs. However, it is not enough to simply assess their impacts; organizations and stakeholders involved in creating, operating, and maintaining these projects must also report on them. Recent reports by UNEP indicate that the civil infrastructure sector is slow in adopting these principles of sustainability assessment and reporting. The aim of this study is to develop an integrated approach for assessing the sustainability of civil infrastructure projects and sustainability reporting for organizations and stakeholders while achieving the UN-SDGs. To achieve this aim, the study employs a two-step process. In the first step, a correspondence matrix is derived from the sustainability assessment parameters of various infrastructure rating tools (InRTs), Global Reporting Initiative (GRI) standards, and UN-SDGs targets. In the second step, a composite indicator called the Sustainability Alignment Index (SAI) is developed. The SAI comprises two sub-indices, namely the TBL-Sustainability Coverage Index (TSCI) and the TBL-Sustainability Disclosure Quality Index (TSDQI). The correspondence matrix, indices, and sub-indices are then used to evaluate the quantitative and qualitative aspects of the sustainability disclosures of selected civil infrastructure companies in Asia and Europe. The study's findings question the comprehensiveness of the evaluating criteria used in InRTs. It also reveals the lack of alignment between an infrastructure project's sustainability assessment criteria, GRI standards, and UN-SDGs targets. Moreover, the results emphasize the insufficient coverage of environmental sustainability assessment and reporting parameters in the disclosures made by the companies. The developed correspondence matrix, indices, and sub-indices can be used by organizations for comparing both quantitative and qualitative measures to understand their relative positions. Additionally, it will enable the public, policymakers, and other key stakeholders to compare different dimensions of sustainability more effectively.

Analysing resilience and leagility in post-pandemic sustainable supply chain management: a systematic literature review

PurposeThis study aims to comprehend the role of leagility and resilience in developing sustainable global supply chains, mitigating short-term disruptions and long-term economic impacts from various disasters, in the context of Australian civil infrastructure projects.Design/methodology/approachThe study employs systematic review approach to establish that addressing project success dimensions (i.e. leagility, resilience and sustainability) requires an integrated and extended supply chain approach, encompassing traditional supply chain strategic model elements (i.e. cost/capital, quality and service goals) and supply chain eco-system (i.e. organisations, societies, economies and nature).FindingsThe study underscores the need to enhance supply chain leagility and resilience to achieve sustainability. This can be achieved by developing skills needed to plan across project phases and time frames, aligning with short and long-term organisational goals, assuming smart risks in the face of uncertainty.Research limitations/implicationsThis study extends the traditional supply chain strategic model by introducing new priorities to minimise the consequences of disruptions and to effectively respond to them. The integration of leanness, agility and resilience ensures a sustainable supply chain even in the times of uncertainty, disruption and volatility.Originality/valueThis research provides an opportunity for practitioners and policymakers to rethink and redesign the conventional supply chain model of cost, capital, quality and service objectives. It introduces pioneering concepts by acknowledging and incorporating emerging priorities, especially in Australian civil infrastructure projects. The study integrates leagility and resilience into the existing strategic framework, adding crucial dimensions for sustainable supply chains in infrastructure companies.

Automated Detection of Social Conflict Drivers in Civil Infrastructure Projects Using Natural Language Processing

Early detection and mitigation of social conflict in civil infrastructure projects is essential due to its significant impact on project performance and social governance. Nevertheless, there is no scientific system for monitoring conflict drivers in a timely manner in practice. Furthermore, previous studies of social conflict in the civil engineering and management domains have relied on manual literature reviews and case studies. Although these qualitative approaches have provided context-specific insights, they are limited in their generalizability and broad perspectives. Against this backdrop, this study presents an automated process for detecting conflict drivers from news articles using ChatGPT. The authors collected news articles related to civil infrastructure projects implemented in the Republic of Korea using web crawling. Then, ChatGPT was used to extract conflict-related keyphrases from the article collections and classify the keyphrases into predefined conflict drivers. The result showed a notable performance with a micro average F1-score of 85.7%. Moreover, the authors confirmed the validity of the keyphrase extraction and classification results through two illustrative case studies. The proposed process and methods contribute to facilitating data-driven conflict management. Although this study focused on conflict drivers of public infrastructure projects, other types of information extraction tasks can benefit from the presented framework.

Effect of climate change on the behaviour of thermo-active diaphragm walls

Energy geo-structures are becoming more common as a renewable energy solution which utilises shallow geothermal energy to provide heating and cooling to buildings and civil infrastructure projects. There has been an upward trend in the use of thermo-active geo-structures in the United Kingdom, from 150 in 2005 to over 5000 in 2012 [2]. Studies so far have shown that diaphragm walls subjected to combined thermo-mechanical loading show overall increases in lateral displacements, bending moments, shear forces, axial forces, and settlements on the retained side with thermal cycles [6, 7, 8].
 This study uses a variation of a validated numerical model [4] to predict the behaviour of thermo-active diaphragm walls in the longer-term including accounting for the influence of climate change. This numerical model also assesses the impact of different modelling assumptions on the model output by comparing a simplified model with a more complex model where atmospheric temperatures affecting ground surface temperatures are included. Research from the IPCC shows increases in ambient temperatures and in maximum extreme temperatures as a result of climate change. Two models are compared in the IPCC report [5], Representative Concentration Pathway (RCP) 2.6 where governments across the world work together to meet the recommendations in the report to reduce overall warming, and RCP8.5 where the current trends of heating continue without any governmental interventions. According to climate model RCP2.6, global mean surface air temperatures will increase by around 1.5°C and extreme daily temperatures (i.e. days where temperatures exceed expected temperatures for the time of year) will increase by around 2°C over the 50-year period chosen for this study, whilst using RCP8.5 global mean surface air temperature increases by approximately 3°C and extreme daily temperatures increase by approximately 5°C.
 This study uses the same model geometry as a model validated through centrifuge testing [4] which is a 5m deep cantilever diaphragm wall, like those that would be used in deep excavations for open-topped tunnels in large civil infrastructure projects. The numerical model was developed using the semi-coupled thermo-hydro-mechanical (THM) thermal module available with PLAXIS 2D and uses the HS-Small soil model [3] to allow for variation in shear modulus at small strains (i.e. those associated with retaining walls) to be captured more accurately. Thermal loads were applied by applying heat flows through a plate element with the same mechanical properties of the model diaphragm wall used in centrifuge testing. This model attempts to capture the influence of atmospheric temperatures by applying thermal boundary conditions to the ground surface as well as to the thermo-active diaphragm wall. Studies have demonstrated that a more complex model capturing atmospheric temperatures increases the impact of thermal loading [4], whilst also capturing a more realistic temperature profile throughout the entire depth of the soil body both adjacent to the wall and in the free field condition. In this study, atmospheric temperatures were captured in RCP2.6 by cycling between 30°C and -5°C over the course of the cycle to represent a typical ‘hottest’ and ‘coldest’ day of the year in cycle 1, increasing to 32°C and -5°C in line with the temperature increases described in the IPCC report [5]. Ambient mean temperatures between heating and cooling cycles increased from 20°C to 21.5°C over the 50-year period. In the same model, the temperature within the diaphragm
 wall cycled between 35°C and 10°C in cycle 1, representing the typical operation of a ground source heat pump (GSHP) in heating and cooling. These temperature inputs increased to 37°C and 10°C over the 50-year period. The RCP8.5 atmospheric temperatures cycled between 30°C and -5°C to represent summer and winter in cycle 1 and increase to 35°C and -5°C over the 50-year period. Thermal flows in the diaphragm wall cycled between 35°C and 10°C in cycle 1 and increased to 40°C and 10°C over the 50-year period. The results from this study show increases in lateral displacement, maximum bending moments, positive and negative shear forces and axial forces (compressive (-) and tensile (+)). These results are shown in Figure 1.
 Significantly, the RCP2.6 model shows that these increases begin to stabilise over the 50-year period, with the increases in the final cycles becoming negligible. With the RCP8.5 model these increases are increasing linearly at the end of the modelling period which can potentially have significant implications not only in terms of serviceability of the structure, but potentially on ultimate limit state as linear increases suggest that design limits will eventually be reached. The findings from this study generally agree with other similar studies, such as a study that analysed the geotechnical performance of energy piles with additional environmental loading as a result of climate change which found that additional compressive and tensile stresses are generated during heating and cooling phases, respectively [1].

Finite Element Analysis of Segmental Precast Concrete Panel Reinforced Earth Retaining Wall

Reinforced earth retaining walls are being utilized more frequently in civil infrastructure projects as an alternate design structure to typical reinforced concrete walls and other structures for supporting backfill. The objective of the present study is to model and analyze a 2-dimensional Segmental Precast Concrete Panel (SPCP) wall using the 2-dimensional finite-element tool PLAXIS and analyze the behaviour of the wall concerning the effect of reinforcement type and surcharge loads. The present work also includes the investigations of the effects of reinforcement type and surcharge loads, as well as the influence of different supporting systems on the deformations and ground-surface settlements of the SPCP wall. Ribbed steel reinforcements, Polyethylene Terephthalate (PET) geogrids and Density Polyethylene (HDPE) geogrids are used to evaluate the wall deformations for reinforcement types. For ribbed-steel reinforcement, ground settlements and wall deformations are 14% and 25% less compared to those of PET and HDPE geogrid reinforcements, respectively. With the increase in surcharge on the backfill soil, wall deformations and surface-ground settlements are increased significantly by 150%. To decrease the deformations of walls resting on soft soil, pile foundations and aggregate piers are considered as supporting solutions. In the case of reinforced blocks with pile foundation as supporting systems, the wall deformations and settlement are 60% lesser than for aggregate pier supporting systems. KEYWORDS: Segmental precast concrete panel, PLAXIS, Reinforcement type, Aggregate pier, Pile Foundation, Wall deformation

On the load bearing mechanisms of cemented granular material: A mesoscale FE approach

AbstractThe numerical investigation of cemented granular material (CGM) is a demanding task that requires the combination of various scientific disciplines. At the mesoscale, CGM is decomposed into the constituents of particles, cement matrix and void pores. The combination of these constituents, as found in nature or in civil infrastructure projects, creates a highly heterogeneous structure, whose morphology defines the response of the CGM under mechanical loading. The composite internal structure is quantified by means of x‐ray Computed Tomography, which provides 16‐bit grayscale 3D images of the scanned microsamples. These images are decomposed into the three constituent materials to be investigated and modeled. Still, the ability of simple Cartesian grids to adequately carry information about curved geometries and provide accurate representations is questioned. In order to overcome staircase effect errors, the interfaces of the material inclusions are smoothed and re‐defined, making use of Gaussian blurred signed distance fields (SDF). These level‐set derived interfaces serve as a reference for the volume reconstruction of the image via tetrahedra. The comparison of the raw and the smoothened images to analytical geometry Standards suggests that the above reconstruction alleviates the grid's inherited uncertainities. In addition, a comparison between a non smoothed and a smoothed meshed image is performed in terms of mechanical response, utilizing the Finite Element (FE) Method. The results suggest that even a slight change in the granular contact fabric can alter the unfolding stress transmission mechanisms.

Automated clash resolution for reinforcement steel design in precast concrete wall panels via generative adversarial network and reinforcement learning

The increasing adoption of precast concrete elements (PCEs) is evident in civil infrastructure projects. The structural integrity of prefabricated structures relies on the quality of connections between adjacent components, necessitating the design of reinforcing steel bars (rebars) in PCEs. Dealing with PCEsand the complex design codes for rebars' arrangement can be labor-intensive, impractical, and error-prone when using traditional computer software due to their irregular shapes. This often results in frequent rebar clashes on construction sites. On the other hand, Building Information Modeling (BIM) technology, which is widely employed for structural design in the industry, offers potential solutions to these challenges. Several studies have proposed clash resolution strategies for moving components using optimization algorithms; these strategies are only suitable for regular reinforced concrete (RC) structures. The optimized path of rebars cannot be adjusted to avoid obstacles in PCEs. Due to strict design codes and large dimensions, existing studies do not possess the learning knowledge from design codes or drawings needed to generate clash-free rebar arrangements for real-world PCEs automatically. To overcome this limitation, we present a BIM-based framework that utilizes Generative Adversarial Network (GAN) and Deep Reinforcement Learning (DRL) to automatically generate clash-free rebar designs in prefabricated concrete wall panels (PCWPs). Our method employs GAN to learn from designers’ experiences from existing design drawings and generate 2D preliminary rebar designs, which are then transformed into mesh environments for DRL. The DRL engine incorporates state, action, and rewards designed according to buildability constraints and design codes. We conduct extensive experiments on real-world PCWPs to assess the efficacy of the proposed method. The results indicate that our framework can reduce engineering time for rebar designs by up to 80% compared to manual design, while achieving clash-free designs that adhere to design codes.

A novel approach for industrial concrete defect identification based on image processing and deep convolutional neural networks

The preservation of structural integrity and durability is essential for the long-term viability of civil infrastructure projects. Addressing concrete defects promptly is crucial to achieving this objective. In this research, the research proposes a novel method for concrete defect analysis, harnessing the potential of image processing and deep learning techniques. It employs a fusion-based deep convolutional neural network (CNN), amalgamating the features of Inception V3, VGG16, and AlexNet architectures to identify and classify six distinct concrete defect characteristics, namely Cracks, Crazing, Efflorescence, Pop-out, Scaling, and Surface Cracks. Through rigorous training and validation, we thoroughly assess the performance of the proposed fusion-based CNN model. The testing phase reveals precision rates, with Crazing showing the lowest precision at 95%, and Cracks/Pop-outs achieving 98%, while other defect classifications exhibit exceptional 100% precision rates. The overall efficacy of our proposed model is evaluated using accuracy and F1-score metrics. Our findings demonstrate an impressive overall accuracy of 98.31% and an F1-score of 0.98, affirming the robustness and reliability of our approach. The outcomes of this study signify a significant advancement toward accurate and automated detection and classification of concrete defects.

An approach to estimate influence region of field investigation sampling points, investigation zone in civil infrastructure projects and brief review of existing field investigation process

Geotechnical investigation is a cyclic process. The interdependency between different steps involved in whole process, i.e., from planning, performing investigation, transporting samples and analysis of data obtained from field or lab, present opportunities for improvements and further discussion.For estimating number of investigation sampling points required for a particular site and selection of investigation methods, today we mostly relay on information from previously conducted site investigations, local experience from that area or site. In geotechnical investigation getting field samples which represents actual ground conditions for the whole site is well known challenge and has been highlighted in many research papers.In this paper we are presenting concept of influence region of geotechnical investigation points and an approach for estimation of the same. We believe the influence region concept; its estimation approach will provide a mathematical model and will help in deciding number of sampling points at the geotechnical investigation sites. In author’s opinion the presented concept will further improve the quality of geotechnical ground investigation process and will provide help in getting representative, quality field samples for laboratory analysis.The presented concept and estimation approach in this paper is based on author’s field experience and exposure gained from working in Swedish infrastructure projects. We think the influence region concept will further improve the existing geotechnical ground investigation process and will open opportunities for its validation through focused scientific research.A brief overview of the existing ground investigation processes has been covered in the paper, along with examples from geotechnical site investigations from Trafikverket (The Swedish Transport Administration) projects in Sweden.

Measures to Enhance the Use of Geosynthetics for Civil Infrastructure Projects: An Example from Ghana

This study sought to establish measures to enhance the use of geosynthetics for civil infrastructure projects in Ghana and determine the relative impact of each measure. It employed the Delphi technique, and a structured questionnaire aided the data collection from 15 expert panellists. At least a good consensus was reached in the views of the expert panellists regarding 14 measures to enhance the use of geosynthetics in Ghana after two rounds of a Delphi survey. Out of the 14 measures, there was a strong consensus in the views of the expert panellists regarding 3 measures as they recorded median values within the range (VHI: 9.00-10.00) signifying very high impact. These measures were, readily availability of geosynthetic products in the market, availability of design manuals for geosynthetics design, and educating construction practitioners on geosynthetics options for meeting their infrastructure needs. A good consensus was attained in the views of the expert panellists for the remaining 11 measures as they recorded median scores within the range (HI: 7.00-8.99) signifying high impact. Moreso, 2 out of the 14 measures were unique to this study in Ghana: setting up of a geosynthetic think tank to advocate for the use of geosynthetics, and promoting geosynthetics usage in the manifestos of political parties. In terms of relative impact index ranking, readily availability of geosynthetic products in the market with RII score of 0.91 ranked 1st whereas increase geosynthetics education and exposure for prospective and practising civil engineers with RII of 0.79 ranked 14th. This study is unique in that it applies the Delphi technique and empirically establishes the measures to enhance the use of geosynthetics for civil infrastructure projects giving an example from Africa, specifically Ghana which hitherto was not in existence.

Hybrid Fiber Optic Cable for Strain Profiling and Crack Growth Measurement in Rock, Cement, and Brittle Installation Media.

Brillouin scattering-based distributed fiber optic sensing (DFOS) technologies such as Brillouin optical time domain reflectometry (BOTDR) and Brillouin optical time domain analysis (BOTDA) have broad applicability for the long term and real-time monitoring of large concrete structures, underground mine excavations, pit slopes, and deep subsurface wellbores. When installed in brittle media, however, the meter scale spatial resolution of the BOTDR/A technology prohibits the detection or measurement of highly localized deformations, such as those which form at or along cracks, faults, and other discontinuities. This work presents a novel hybrid fiber optic cable with the ability to self-anchor to any brittle installation media without the need for manual installation along fixed interval points. Laboratory scale testing demonstrates the ability of the hybrid fiber optic cable to measure strains across highly localized deformation zones in both tension and shear. In addition, results show the applicability of the developed technology for strain monitoring in high displacement environments. Linear relationships are proposed for use in estimating the displacement magnitude along discontinuities in brittle media from strain signals collected from the hybrid fiber optic cable. The hybrid fiber optic cable has broad potential applications, such as geomechanical monitoring in underground mines, surface pits, large civil infrastructure projects, and deep subsurface wellbores. The benefits of fiber optic sensing, such as the intrinsic safety of the sensors, the long sensing range, and real time capabilities make this a compelling technique for long term structural health monitoring (SHM) in a wide range of industrial and civil applications.

An Assessment of the Extent of Application of Geosynthetics to Civil Infrastructure Projects in Ghana

This study was informed by the lack of literature that gave a country-specific holistic account of the extent of the application of geosynthetics to civil infrastructure projects. Thus, this current study determined the extent that geosynthetics had been applied to civil infrastructure projects in Ghana and, established the relative level of application of geosynthetics to civil infrastructure projects in Ghana. Structured questionnaire aided in data collection. Data were analysed using frequency, percentage, mean, standard deviation, and one sample t-test. The study revealed that there was a low level of application of geosynthetics to civil infrastructure projects in Ghana. However, Ghana’s case was of great concern as geosynthetics had been applied to only 7 out of the 28 civil infrastructure projects identified in previous studies. Pipe projects had the most application of geosynthetics whereas geosynthetics were least applied to retaining structures. Moreso, the study further revealed that only 6 out of the 9 types of geosynthetics had been applied to civil infrastructure projects in Ghana namely: geopipes, geomembranes, geotextiles, geogrids, geonets, and geocells. Relatively, geopipes were the most applied geosynthetics whereas geocells were the least applied geosynthetics to civil infrastructure projects in Ghana. This study serves as reference material on the extent of application of geosynthetics to civil infrastructure projects in Ghana which hitherto was lacking in the construction industry in Ghana. It also serves as the basis for future studies on the application of geosynthetics as it contributes to the existing body of literature on the subject.

Enhancing the Stability and Placement Accuracy of BIM Model Projections for Augmented Reality-Based Site Management of Infrastructure Projects

The utilization of Building Information Modeling (BIM) on-site has been limited due to the lack of an appropriate medium for visualizing and accessing models and project data intuitively in the field. AROS, an Augmented Reality-based site management system, was developed to allow the visual projection of BIM models and relevant data directly to field personnel. Detailed field experiments with inspection experts revealed specific issues with the stability and accurate placement of model projections in AROS. Projection stability was improved by reducing the number of triangle meshes of the model to relieve the need for processing power. Investigations revealed that simplification rates of 40% and 20% were optimal for rectilinear and curvilinear components, respectively. Projection placement was improved by implementing a hybrid of target anchoring methods. Specifically, ARWorldMap was used to add additional anchor points, which were identified from the topologies of the structure and surrounding planes. Post-evaluations demonstrated increased stability and reductions in displacement errors. The formalizations provide measures for using AR and BIM models when applying these technologies to large-scale civil infrastructure projects.

Enhanced repetitive scheduling formulation for meeting deadlines and resource constraints in linear and scattered projects

The majority of civil infrastructure projects involve activities that are repeated across a number of linear units (e.g., highway sections) or scattered units (e.g., multi-bridge rehabilitations). For these projects, Critical Path Method (CPM)/line-of-balance (LOB) repetitive scheduling combines the benefits of CPM and LOB analyses to maintain work continuity across units, yet assumes simple sequential unit order and results in schedule delays for practical projects with non-identical units, constrained resources, and (or) strict deadlines. To improve CPM/LOB scheduling, this paper introduces powerful and easy-to-use enhancements, including (1) designed interruptions to reduce time gaps; (2) efficient resource-constrained first-come first-serve crew assignment; (3) crew adjustment loop to meet deadlines; and (4) representation of flexible unit sequence. These simplified enhancements computationally produce schedules that respect deadlines, individual resource limits, and desired sequence among units. Example projects are then presented to prove that the proposed enhancements offer flexible scheduling features that can meet the strict constraints of infrastructure projects.

Utilization of serial tendering to reduce the value project

Abstract Serial tendering is better than other types of tendering when it comes to cost reduction, where civil infrastructure projects need a significant increase in the amount of tough planning, financial expenditures, engineering work, and resources of a different character than other types of construction projects. The effects of a lack of funding cause decrease in the completion speed of the project on time. The need to reduce the cost of bidding on recurrent civil infrastructure projects is critical. To achieve the desired goals of this research, this article will provide an overview of the type of bids used in the construction of schools implemented in the current financial perspective in Iraq, the extent of benefit, and the amount of possible reduction if continuous and serial tendering are used.

Computer vision for anatomical analysis of equipment in civil infrastructure projects: Theorizing the development of regression-based deep neural networks

There is high demand for heavy equipment in civil infrastructure projects and their performance is a determinant of the successful delivery of site operations. Although manufacturers provide equipment performance handbooks, additional monitoring mechanisms are required to depart from measuring performance on the sole basis of unit cost for moved materials. Vision-based tracking and pose estimation can facilitate site performance monitoring. This research develops several regression-based deep neural networks (DNNs) to monitor equipment with the aim of ensuring safety, productivity, sustainability and quality of equipment operations. Annotated image libraries are used to train and test several backbone architectures. Experimental results reveal the precision of DNNs with depthwise separable convolutions and computational efficiency of DNNs with channel shuffle. This research provides scientific utility by developing a method for equipment pose estimation with the ability to detect anatomical angles and critical keypoints. The practical utility of this study is the provision of potentials to influence current practice of articulated machinery monitoring in projects.

Public opinion on megaprojects over time: findings from four megaprojects in the UK

ABSTRACT Megaprojects, due to their size, scale, and technical complexity are expensive and controversial, and how they proceed over time is a key topic of interest. This paper seeks to identify the issues surrounding changes in public opinion over time and discusses a larger question on whether such knowledge can be generalized across projects. An automated text analysis technique called ‘sentiment analysis’ has been used to plot trajectories for four UK megaprojects from newspaper articles. The empirical setting includes two military and two (civil) infrastructure projects, allowing the exploration of differences between the two fields as a secondary line of analysis.

Non-normal distribution of residual flexural strengths of steel fiber reinforced concretes and its impacts on design and conformity assessment

Variability of concrete properties has strong influences on design and conformity assessment. However, variability of residual flexural strengths of steel fiber-reinforced concrete (SFRC) has yet been statistically investigated. In this study, a large number of SFRC specimens was sampled from a central mix concrete plant manufacturing precast SFRC elements for a real civil infrastructure project. Variability of residual flexural strengths of SFRC are reported. Results show that the residual flexural strengths of SFRC follow the log-normal distribution at large crack month opening displacement (CMOD). The origin that leads to the log-normal distribution of the residual flexural strengths of SFRC is discussed. The use of an assumed normal distribution of the residual flexural strengths of SFRC in the current design approach and conformity assessment results in more than 30% overdesign and more stringent acceptance criteria. This leads to higher materials cost and higher producer's risk, which discourage the wider adoption and application of SFRC. The logarithmic transformation provides a practical and convenient approach to design and to check the conformity of log-normally distributed SFRC properties adopting existing criteria. Compared to the typical lab-based study with limited number of specimens and test results from a well-controlled environment for material processing and specimen preparation, the field study reflects more closely the true material variability in real construction. Thus, the statistical analysis obtained and conclusions drawn from the current work are more representative.

BIM-Based Tunnel Information Modeling Framework for Visualization, Management, and Simulation of Drill-and-Blast Tunneling Projects

AbstractTunnel construction fundamentally differs from building and aboveground civil infrastructure projects. Drill-and-blast is one of the most common and flexible tunnel construction methods. Ho...