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Tracking physical resources at the construction site can generate information to support effective decision-making and building production control. However, the methods for conventional tracking usually offer low reliability. This study aims to propose the integrated Smart Twins 4.0 to track and manage metallic formworks used in cast-in-place concrete wall systems using internet of things (IoT) (operationalized by radio frequency identification [RFID]) and building information modeling (BIM), focusing on increasing quality and productivity.

Design science research is the research approach, including an exploratory study to map the constructive system, the integrated system development, an on-site pilot implementation in a residential project and a performance evaluation based on acquired data and the perception of the project’s production team.

In all rounds of requests, Smart Twins 4.0 registered and presented the status from the formworks and the work progress of buildings in complete correspondence with the physical progress providing information to support decision-making during operation. Moreover, analyses of the system infrastructure and implementation details can drive researchers regarding future IoT and BIM implementation in real construction sites.

The primary contribution is the system proposal, centralized into a mobile app that contains a Web-based virtual model to receive data in real time during construction phases and solve a real problem. The paper describes Smart Twins 4.0 development and its requirements for tracking physical resources considering theoretical and practical previous research regarding RFID, IoT and BIM.

This study focused on exploring the performance factors (PFs) that impact Infrastructure Sanitation Projects (ISSPs) in the construction sector. The aim was twofold: firstly, to identify these crucial PFs and secondly, to develop a robust performance model capable of effectively measuring and assessing the intricate interdependencies and correlations within ISSPs. By achieving these objectives, the study aimed to provide valuable insights into and tools for enhancing the efficiency and effectiveness of sanitation projects in the construction industry.

To achieve the study's aim, the methodology for identifying the PFs for ISSPs involved several steps: extensive literature review, interviews with Egyptian industry experts, a questionnaire survey targeting industry practitioners and an analysis using the Relative Importance Index (RII), Pareto principle and analytic network process (ANP). The RII ranked factor importance,  and Pareto identified the top 20% for ANP, which determined connections and interdependencies among these factors.

The literature review identified 36 PFs, and an additional 13 were uncovered during interviews. The highest-ranked PF is PF5, while PF19 is the lowest-ranked. Pareto principle selected 11 PFs, representing the top 20% of factors. The ANP model produced an application for measuring ISSP effectiveness, validated through two case studies. Application results were 92.25% and 91.48%, compared to actual results of 95.77% and 97.37%, indicating its effectiveness and accuracy, respectively.

This study addresses a significant knowledge gap by identifying the critical PFs that influence ISSPs within the construction industry. Subsequently, it constructs a novel performance model, resulting in the development of a practical computer application aimed at measuring and evaluating the performance of these projects.

The study is aimed to compare the prediction success of basic machine learning and ensemble machine learning models and accordingly create novel prediction models by combining machine learning models to increase the prediction success in construction labor productivity prediction models.

Categorical and numerical data used in prediction models in many studies in the literature for the prediction of construction labor productivity were made ready for analysis by preprocessing. The Python programming language was used to develop machine learning models. As a result of many variation trials, the models were combined and the proposed novel voting and stacking meta-ensemble machine learning models were constituted. Finally, the models were compared to Target and Taylor diagram.

Meta-ensemble models have been developed for labor productivity prediction by combining machine learning models. Voting ensemble by combining et, gbm, xgboost, lightgbm, catboost and mlp models and stacking ensemble by combining et, gbm, xgboost, catboost and mlp models were created and finally the Et model as meta-learner was selected. Considering the prediction success, it has been determined that the voting and stacking meta-ensemble algorithms have higher prediction success than other machine learning algorithms. Model evaluation metrics, namely MAE, MSE, RMSE and R², were selected to measure the prediction success. For the voting meta-ensemble algorithm, the values of the model evaluation metrics MAE, MSE, RMSE and R² are 0.0499, 0.0045, 0.0671 and 0.7886, respectively. For the stacking meta-ensemble algorithm, the values of the model evaluation metrics MAE, MSE, RMSE and R² are 0.0469, 0.0043, 0.0658 and 0.7967, respectively.

The study shows the comparison between machine learning algorithms and created novel meta-ensemble machine learning algorithms to predict the labor productivity of construction formwork activity. The practitioners and project planners can use this model as reliable and accurate tool for predicting the labor productivity of construction formwork activity prior to construction planning.

The study provides insight into the application of ensemble machine learning algorithms in predicting construction labor productivity. Additionally, novel meta-ensemble algorithms have been used and proposed. Therefore, it is hoped that predicting the labor productivity of construction formwork activity with high accuracy will make a great contribution to construction project management.

This paper aims at discovering the traditional techniques of Persian architecture for covering large-span spaces with a kind of ribbed vault titled “Karbandi”. This structure is generated by intersecting several arches with a harmonic stellar geometry. Preliminary studies show that span factor affects the structural form of karbandi and large-span cases, despite similar architectural forms, have different structural systems and specific construction methods. The main focus of this paper is how karbandi has been designed and built on large-spans. To answer this question, the configuration and construction of a large-span karbandi in Tabriz Bazaar were recognized.

Data collection of the research was initially done in three parallel directions through the archival study of restoration documents, direct observation of the corpus of the vaultings and interviews with the master mason of the Haj-Mohammad-Qoli Timche restoration team. Then by cross-referencing the gathered data, the construction process of the karbandi was simulated in Rhino 6 and Grasshoppers software and its BIM-M models were created in three levels of development: LOD300, LOD350 and LOD400. In the next step, the preliminary BIM-M models of the karbandi were presented to the interviewed mason and revised and completed based on his comments.

Analyzing the BIM models by reverse engineering, resulted in (1) Discovering a unique self-supporting masonry construction method applied for the erection of karbandi vaulting on large-spans. (2) Finding the effect of scale factor on the architectural and structural form of the karbandi vault. (3) Discovering the connection types of the karbandi vault based on the construction details.

Despite the wide applications of karbandi vaults throughout history, very little information of their construction techniques is available. The techniques have mostly been experientially and orally passed down from masters to apprentices and rarely been documented. The quest to design and construct a karbandi vault is therefore like solving a puzzle whose most important guide is historical cases. Due to the geometric complexity of karbandi and its ability to cover large-span spaces, solving the puzzle can lead to achieving some technical ideas for masonry cross-ribbed vaulting. A great riddle of the karbandi vaults is how to design and build them on a large span.

The paper aims to investigate the comfort-related performances of an innovative solar shading solution based on a new composite patented material that consists of a cement-based matrix coupled with a stretchable three-dimensional textile. The paper’s aim is, through a performance-based generative design approach, to develop a high-performance static shading system able to guarantee adequate daylit spaces, a connection with the outdoors and a glare-free environment in the view of a holistic and occupant-centric daylight assessment.

The paper describes the design and simulation process of a complex static shading system for digital manufacturing purposes. Initially, the optical material properties were characterized to calibrate radiance-based simulations. The developed models were then implemented in a multi-objective genetic optimization algorithm to improve the shading geometries, and their performance was assessed and compared with traditional external louvres and overhangs.

The system developed demonstrates, for a reference office space located in Milan (Italy), the potential of increasing useful daylight illuminance by 35% with a reduced glare of up to 70%–80% while providing better uniformity and connection with the outdoors as a result of a topological optimization of the shape and position of the openings.

The paper presents the innovative nature of a new composite material that, coupled with the proposed performance-based optimization process, enables the fabrication of optimized shading/cladding surfaces with complex geometries whose formability does not require ad hoc formworks, making the process fast and economic.

Due to its longer length compared to other construction materials and distinctive stacking patterns, obtaining construction steel bars in congested construction sites with limited storage capacity becomes challenging. Lack of storage space in crowded places prompts the need for building steel bar storage choice optimization. Therefore, this study aims to optimize the construction steel bar procurement plan by providing when and how much rebar to order and how to stack different sizes of rebar considering limited storage capacity.

A novel approach has been presented in this paper by integrating 4D building information modelling (BIM) and mixed-integer linear programming (MILP). This technique uses BIM to retrieve material quantities, including rebar, during the design phase. Following that, activities are scheduled depending on the duration determined by crew productivity data and material quantity. Then, based on the prior price, the price of each unit of rebar is projected for the duration of construction using the exponential smoothing method. After that, the MILP approach is used to generate an optimal steel bar procurement plan for limited storage space following the scheduled rebar-related operations.

The developed strategy minimizes overall procurement costs and ensures the storage of rebar as per standard guidelines. An optimal rebar procurement and storage plan to construct a six-storied RC frame has been presented in this paper as a demonstrative example to show the effectiveness of the proposed method.

This work partially satisfies a long-sought research need for establishing a comprehensive construction steel bar procurement system, making it a very useful source of information for practitioners and researchers. The proposed method can be used to minimize a key performance limitation that the conventional rebar procurement practice for crowded building sites may experience.

Damage to reinforced concrete (RC) structural elements is inevitable. Such damage can be the result of several factors, including aggressive environmental conditions, overloading, inadequate design, poor work execution, fire, storm, earthquakes etc. Therefore, repairing and strengthening is one way to improve damaged structures, so that they can be reutilized. In this research, the use of an ultra high-performance fibre-reinforced concrete (UHPFRC) layer is proposed as a strengthening material to rehabilitate damaged-RC beams. Different strengthening schemes pertaining to the structural performance of the retrofitted RC beams due to the flexural load were investigated.

A total of 13 normal RC beams were prepared. All the beams were subjected to a four-point flexural test. One beam was selected as the control beam and tested to failure, whereas the remaining beams were tested under a load of up to 50% of the ultimate load capacity of the control beam. The damaged beams were then strengthened using a UHPFRC layer with two different schemes; strip-shape and U-shape schemes, before all the beams were tested to failure.

Based on the test results, the control beam and all strengthened beams failed in the flexural mode. Compared to the control beam, the damaged-RC beams strengthened using the strip-shape scheme provided an increase in the ultimate load capacity ranging from 14.50% to 43.48% (or an increase of 1.1450 to 1.4348 times), whereas for the U-shape scheme beams ranged from 48.70% to 149.37% (or an increase of 1.4870–2.4937 times). The U-shape scheme was more effective in rehabilitating the damaged-RC beams. The UHPFRC mixtures are workable, as well easy to place and cast into the formworks. Furthermore, the damaged-RC beams strengthened using strip-shape scheme and U-shape scheme generated ductility factors of greater than 4 and 3, respectively. According to Eurocode8, these values are suitable for seismically active regions. Therefore, the strengthened damaged-RC beams under this study can quite feasibly be used in such regions.

Observations of crack patterns were not accompanied by measurements of crack widths due to the unavailability of a microcrack meter in the laboratory. The cost of the strengthening system application were not evaluated in this study, so the users should consider wisely related to the application of this method on the constructions.

Rehabilitation of the damaged-RC beams exhibited an adequate structural performance, where all strengthened RC beams fail in the flexural mode, as well as having increment in the failure load capacity and ductility. So, the used strengthening system in this study can be applied for the building construction in the seismic regions.

Aside from equipment, application of this strengthening system need also the labours.

The use of sand blasting on the surfaces of the damaged-RC beams, as well as the application of UHPFRC layers of different thicknesses and shapes to strengthen the damaged-RC beams, provides a novel innovation in the strengthening of damaged-RC beams, which can be applicable to either bridge or building constructions.

The Government’s investment in infrastructure projects is considerably high, especially in bridge construction projects. Government authorities must establish an initial forecasted budget to have transparency in transactions. Early cost estimating is challenging for Quantity Surveyors due to incomplete project details at the initial stage and the unavailability of standard cost estimating techniques for bridge projects. To mitigate the difficulties in the traditional preliminary cost estimating methods, there is a requirement to develop a new initial cost estimating model which is accurate, user friendly and straightforward. The research was carried out in Sri Lanka, and this paper aims to develop the artificial neural network (ANN) model for an early cost estimate of concrete bridge systems.

The construction cost data of 30 concrete bridge projects which are in Sri Lanka constructed within the past ten years were trained and tested to develop an ANN cost model. Backpropagation technique was used to identify the number of hidden layers, iteration and momentum for optimum neural network architectures.

An ANN cost model was developed, furnishing the best result since it succeeded with around 90% validation accuracy. It created a cost estimation model for the public sector as an accurate, heuristic, flexible and efficient technique.

The research contributes to the current body of knowledge by providing the most accurate early-stage cost estimate for the concrete bridge systems in Sri Lanka. In addition, the research findings would be helpful for stakeholders and policymakers to propose policy recommendations that positively influence the prediction of the most accurate cost estimate for concrete bridge construction projects in Sri Lanka and other developing countries.

The crane plays an essential role in modern construction sites as it supports numerous operations and activities on-site. Additionally, the crane produces a big amount of data that, if analyzed, could significantly affect productivity, progress monitoring and decision-making in construction projects. This paper aims to show the usability of crane data in tracking the progress of activities on-site.

This paper presents a pattern-based recognition method to detect concrete pouring activities on any concrete-based construction sites. A case study is presented to assess the methodology with a real-life example.

The analysis of the data helped build a theoretical pattern for concrete pouring activities and detect the different phases and progress of these activities. Accordingly, the data become useable to track progress and identify problems in concrete pouring activities.

The paper presents an example for construction practitioners and researcher about a practical and easy way to analyze the big data that comes from cranes and how it is used in tracking projects' progress. The current study focuses only on concrete pouring activities; future studies can include other types of activities and can utilize the data with other building methods to improve construction productivity.

The proposed approach is supposed to be simultaneously efficient in terms of concrete pouring detection as well as cost-effective. Construction practitioners could track concrete activities using an already-embedded monitoring device.

While several studies in the literature targeted the optimization of crane operations and of mitigating hazards through automation and sensing, the opportunity of using cranes as progress trackers is yet to be fully exploited.

Construction progress monitoring (CPM) is considered a difficult and tedious task in construction projects, which focuses on identifying discrepancies between the as-built product and the as-planned design. Computer vision (CV) technology is applied to automate the CPM process. However, the synergy between the CV and CPM in literature and industry practice is lacking. This study aims to fulfil this research gap.

A Delphi qualitative approach was used in this study by conducting two interview rounds. The collected data was analysed using manual content analysis.

This study identified seven stages of CPM; data acquisition, information retrieval, verification, progress estimation and comparison, visualisation of the results and schedule updating. Factors such as higher accuracy in data, less labourious process, efficiency and near real-time access are some of the significant enablers in instigating CV for CPM. Major challenges identified were occlusions and lighting issues in the site images and lack of support from the management. The challenges can be easily overcome by implementing suitable strategies such as familiarisation of the workforce with CV technology and application of CV research for the construction industry to grow with the technology in line with other industries.

This study addresses the gap pertaining to the synergy between the CV in CPM literature and the industry practice. This research contributes by enabling the construction personnel to identify the shortcomings and the opportunities to apply automated technologies concerning each stage in the progress monitoring process.