The blockchain that uses cryptocurrency is a paradigm shift in the way of data storage, retrieval, and verification due to the concept of decentralization. This paradigm is essential to ensure the security of crucial data in any project. Adding a smart contract to the blockchain would facilitate the automation of various processes. Thus, the cryptocurrency blockchain that uses the smart contract can be considered a suitable platform for an ecosystem of many industries. The construction industry needs a highly secure automated management system due to its complex contractual relationships and transactions between parties. Therefore, integrating the blockchain with the smart contract creates the most appropriate ecosystem to be developed. This study introduces an ecosystemic prototype using a programmable smart contract within a novel cryptocurrency blockchain for construction. The purpose of the prototype is to guarantee a decentralized system as an independent economic environment for the construction industry. The system guarantees the security of financial transactions and focuses on the payment clauses in the construction contract as well. The results depended on three well-known hypothetical case scenarios from the construction site and were displayed in the form of extracted access data tables. The prototype proved the efficiency of the decentralized system for the construction industry by minimizing human-factor interference in the transaction process and thus reducing time waste and cost.

Infrastructural assets such as roads, bridges, and buildings make a considerable contribution to national economies. These assets deteriorate due to aging, environmental conditions, and other external factors. Maintaining the performance of an asset in line with rational repair strategies represents a considerable challenge for decision-makers, who may not pay attention to developing adequate maintenance plans or leave the assets unmaintained. Worldwide, organizations are under pressure to ensure the sustainability of their assets. Such organizations may burden their treasury with random maintenance operations, especially with a limited budget. This research aims to develop a generalized condition assessment approach to monitor and evaluate existing facility elements. The proposed approach represents a methodology to determine the element condition index (CI). The methodology is reinforced with an artificial neural network (ANN) model to predict the element deterioration. The performance of this model was evaluated by comparing the obtained predicted CIs with ordinary least squares (OLS) regression model results to choose the most accurate prediction technique. A case study was applied to a group of wooden doors. The ANN model showed reliable results with R² values of 0.99, 0.98, and 0.99 for training, cross-validation, and testing sets, respectively. In contrast, the OLS model R² value was 1.00. These results show the high prediction capability of both models with an advantage to the OLS model. Applying this approach to different elements can help decision-makers develop a preventive maintenance schedule and provide the necessary funds.

The Architecture, Engineering, and Construction (AEC) industry faces significant global challenges, including frequent project delays, budget overruns, and inadequate stakeholder collaboration. To address these issues, Value Engineering (VE) and Building Information Modeling (BIM) have been increasingly used in large-scale, complex construction projects. Although many studies highlight the benefits of integrating VE with BIM, its full practical potential has not yet been realized. This study aims to investigate the integration of VE and BIM within a Common Data Environment (CDE) to improve decision making and project outcomes. A comprehensive framework was developed, consisting of four interconnected modules: (1) Creating the CDE, (2) Developing the BIM Model, (3) Implementing Value Engineering, and (4) Conducting a Value Engineering Study. Central to this framework is the introduction of the VEIDEA” data bank, a structured system based on the OmniClass classification, which stores and organizes VE ideas. Additionally, the framework incorporates the Analytical Hierarchy Process (AHP) to automate the evaluation phase, assisting designers and VE teams in making data-driven decisions on design alternatives. Empirical results from a case study of an office building show significant cost savings, with a 20% reduction in reinforced concrete (RC) slab costs and a 39% reduction in flooring material costs. These findings demonstrate the potential for integrating VE and BIM to enhance cost-effectiveness and overall project performance. This study offers a novel approach to optimizing project collaboration, decision making, and efficiency in the AEC industry.

The architecture, Engineering, and Construction (AEC) industry is dealing with serious challenges on a global scale, i.e., project delays, budget overruns, and insufficient stakeholder collaboration. In this context, Value Engineering (VE) and Building Information Modeling (BIM) have been used recently to address these challenges and cope with large-scale and complex construction projects. An extensive body of literature highlights significant advantages of integrated VE within BIM technology. Despite this, it is full practical potential has not yet been realized. For this reason, this paper aims to investigate the integration of Value Engineering (VE) and Building Information Modeling (BIM) within a Common Data Environment (CDE) for the Architecture, Engineering, and Construction (AEC) industry. In doing this research, a comprehensive framework was developed and systematically implemented into practice through the use of four interconnected modules: (M. 1); Creating CDE, (M. 2); Design BIM Model Development, (M. 3); Value Analysis / Engineering Model, and (M. 4); Value Engineering Study. Introducing a new term the ""VEIDEA"" databank based on the OmniClass classification system provides a structured database utilized to store, manage, and accumulate VE ideas. Moreover, the study incorporates the Analytical Hierarchy Process (AHP) to automate the evaluation phase. Such a framework seeks to assist designers and VE team members make informed decisions when choosing appropriate design alternatives. Lastly, a case study method was adopted to examine the proposed framework for an office building. Quantitative data showed a significant link between cost savings and VR integration with BIM technology. For instance, cost of the RC slabs dropped by 20%, while flooring materials cost dropped by a 39%. Empirically, evidence from the case study supports literature regarding t the potential for significant enhancements in both cost-effectiveness and overall project performance through this innovative integration. In the context of knowledge contribution, this study provides an innovative way to assist designers and VE team members in making well-informed decisions when choosing appropriate design alternatives.

Artificial intelligence has attracted increasing attention in the construction industry; however, automated time scheduling remains limited in practical applications. Schedule development remains manual, requiring planners to analyze project documents, define activities, estimate durations, and identify relationships based on logical sequence. This process primarily depends on individual experience and skills, making it both time-consuming and prone to human error. From an engineering design perspective, delayed or inconsistent schedule development weakens design-to-construction feedback, limiting the ability to evaluate constructability and time implications of alternative design decisions during early-stage planning. This study proposes an integrated BIM–Natural Language Processing (NLP) framework to automate activity identification, duration estimation, and logical sequencing for construction scheduling. The framework extracts project data from Revit, organizes it into a bill of quantities format, and then generates an activity list, each activity with a unique ID. Using Sentence-BERT (SBERT) embeddings, the framework estimates activity durations based on semantic similarity. The same semantic process is combined with rule-based reasoning to identify logical relationships, including sequences, supported by an Excel-based reference dictionary that includes logical relationships, productivity, and ID structure. Finally, the framework incorporates a crashing module that proportionally adjusts the duration of activities on the longest path to target the project’s completion time without violating relationships. The proposed framework was validated using real construction project data and produced reliable results. By producing a tool-ready schedule directly from design-model information, the proposed workflow enables earlier schedule feedback loops and supports design-informed planning by allowing designers and planners to assess the time consequences of model-driven scope changes. The results demonstrate that integrating BIM and NLP can transform conventional schedules into faster, more consistent processes, thereby supporting the construction industry.