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Developing a reliable cost estimate at the early stage of construction projects is challenging due to inadequate project information. Most of the information during this stage is qualitative, posing additional challenges to achieving accurate cost estimates. Additionally, there is a lack of tools that use qualitative project information and forecast the budgets required for project completion. This research, therefore, aims to develop a model for setting project budgets (excluding land) during the pre-conceptual stage of residential buildings, where project information is mainly qualitative.

Due to the qualitative nature of project information at the pre-conception stage, a natural language processing model, DistilBERT (Distilled Bidirectional Encoder Representations from Transformers), was trained to predict the cost range of residential buildings at the pre-conception stage. The training and evaluation data included 63,899 building permit activity records (2021–2022) from the Victorian State Building Authority, Australia. The input data comprised the project description of each record, which included project location and basic material types (floor, frame, roofing, and external wall).

This research designed a novel tool for predicting the project budget based on preliminary project information. The model achieved 79% accuracy in classifying residential buildings into three cost_classes ($100,000-$300,000, $300,000-$500,000, $500,000-$1,200,000) and F1-scores of 0.85, 0.73, and 0.74, respectively. Additionally, the results show that the model learnt the contextual relationship between qualitative data like project location and cost.

The current model was developed using data from Victoria state in Australia; hence, it would not return relevant outcomes for other contexts. However, future studies can adopt the methods to develop similar models for their context.

This research is the first to leverage a deep learning model, DistilBERT, for cost estimation at the pre-conception stage using basic project information like location and material types. Therefore, the model would contribute to overcoming data limitations for cost estimation at the pre-conception stage. Residential building stakeholders, like clients, designers, and estimators, can use the model to forecast the project budget at the pre-conception stage to facilitate decision-making.

Faced with the growing need to find new viable water supply models for urban areas, this article studies and maps the strategies and identifies the key criteria of sustainable development present in pioneering water supply systems in the medieval period. The main aim is to determine which of its innovative principles could be applied in present-day cities.

From a methodological perspective, two types of cases were established, such as water supply models for human consumption and pre-industrial hydraulic systems, all of which are located in Italy. For the first group, the cases of Venice and Siena were analysed, while for the second, in the context of the cities along the Aemilian Way, the case of Bologna was selected.

Five key criteria resulted from the analysis of the cases: exploitation, self-sufficiency, maintenance, rationalisation and reuse. The said concepts were defined and contextualised within the framework of the Sustainable Development Goals.

The Middle Ages were a historic moment in technological reinvention, before the development of modern systems of sanitation. With very limited resources, these traditional systems focused on rational use and deep cultural and geographical knowledge. This is why its recognition is of great importance today, in a time full of instabilities, with a view to the work that needs to be done for the development of more sustainable communities.

To make the robot that have real autonomous ability is always the goal of mobile robot research. For mobile robots, simultaneous localization and mapping (SLAM) research is no longer satisfied with enabling robots to build maps by remote control, more needs will focus on the autonomous exploration of unknown areas, which refer to the low light, complex spatial features and a series of unstructured environment, lick underground special space (dark and multiintersection). This study aims to propose a novel robot structure with mapping and autonomous exploration algorithms. The experiment proves the detection ability of the robot.

A small bio-inspired mobile robot suitable for underground special space (dark and multiintersection) is designed, and the control system is set up based on STM32 and Jetson Nano. The robot is equipped with double laser sensor and Ackerman chassis structure, which can adapt to the practical requirements of exploration in underground special space. Based on the graph optimization SLAM method, an optimization method for map construction is proposed. The Iterative Closest Point (ICP) algorithm is used to match two frames of laser to recalculate the relative pose of the robot, which improves the sensor utilization rate of the robot in underground space and also increase the synchronous positioning accuracy. Moreover, based on boundary cells and rapidly-exploring random tree (RRT) algorithm, a new Bio-RRT method for robot autonomous exploration is proposed in addition.

According to the experimental results, it can be seen that the upgraded SLAM method proposed in this paper achieves better results in map construction. At the same time, the algorithm presents good real-time performance as well as high accuracy and strong maintainability, particularly it can update the map continuously with the passing of time and ensure the positioning accuracy in the process of map updating. The Bio-RRT method fused with the firing excitation mechanism of boundary cells has a more purposeful random tree growth. The number of random tree expansion nodes is less, and the amount of information to be processed is reduced, which leads to the path planning time shorter and the efficiency higher. In addition, the target bias makes the random tree grow directly toward the target point with a certain probability, and the obtained path nodes are basically distributed on or on both sides of the line between the initial point and the target point, which makes the path length shorter and reduces the moving cost of the mobile robot. The final experimental results demonstrate that the proposed upgraded SLAM and Bio-RRT methods can better complete the underground special space exploration task.

Based on the background of robot autonomous exploration in underground special space, a new bio-inspired mobile robot structure with mapping and autonomous exploration algorithm is proposed in this paper. The robot structure is constructed, and the perceptual unit, control unit, driving unit and communication unit are described in detail. The robot can satisfy the practical requirements of exploring the underground dark and multiintersection space. Then, the upgraded graph optimization laser SLAM algorithm and interframe matching optimization method are proposed in this paper. The Bio-RRT independent exploration method is finally proposed, which takes shorter time in equally open space and the search strategy for multiintersection space is more efficient. The experimental results demonstrate that the proposed upgrade SLAM and Bio-RRT methods can better complete the underground space exploration task.

This study establishes a method for predicting ground vibrations caused by railway tunnels in unsaturated soils with spatial variability.

First, an improved 2.5D finite-element-method-perfect-matching-layer (FEM-PML) model is proposed. The Galerkin method is used to derive the finite element expression in the ub-pl-pg format for unsaturated soil. Unlike the ub-v-w format, which has nine degrees of freedom per node, the ub-pl-pg format has only five degrees of freedom per node; this significantly enhances the calculation efficiency. The stretching function of the PML is adopted to handle the unlimited boundary domain. Additionally, the 2.5D FEM-PML model couples the tunnel, vehicle and track structures. Next, the spatial variability of the soil parameters is simulated by random fields using the Monte Carlo method. By incorporating random fields of soil parameters into the 2.5D FEM-PML model, the effect of soil spatial variability on ground vibrations is demonstrated using a case study.

The spatial variability of the soil parameters primarily affected the vibration acceleration amplitude but had a minor effect on its spatial distribution and attenuation over time. In addition, ground vibration acceleration was more affected by the spatial variability of the soil bulk modulus of compressibility than by that of saturation.

Using the 2.5D FEM-PML model in the ub-pl-pg format of unsaturated soil enhances the computational efficiency. On this basis, with the random fields established by Monte Carlo simulation, the model can calculate the reliability of soil dynamics, which was rarely considered by previous models.

Modern subway transportation systems need to satisfy increasing safety demands to rapidly evacuate passengers under hazardous emergency circumstances, such as fires, accidents or terrorist attacks, to reduce passenger injuries or life losses. The emergency evacuation capacity (EEC) of a subway station needs to be revised timely, in case passenger demand increases or the evacuation route changes in the future. However, traditional ways of estimating EEC, e.g. fire drills are time- and resource-consuming and are difficult to revise from time to time. The purpose of this study is to establish an intuitive modelling approach to increase the EEC of subway stations in a stepwised manner.

This study develops an approach to combine agent-based evacuation modelling and building information modelling (BIM) technology to estimate the total evacuation time of a subway station.

Evacuation time can be saved (33% in the studied case) from iterative improvements including stopping escalators running against the evacuation flow and modifying the geometry around escalator exits. Such iterative improvements rely on integrating agent-based modelling and BIM.

The agent-based model can provide a more realistic simulation of intelligent individual movements under emergency circumstances and provides precise feedback on locations of evacuation bottlenecks. This study also examined the effectiveness of two rounds of stepwise improvements in terms of operation or design to increase the EEC of the station.

This study aims to document the hypogea churches in Lay Gayint Woreda, South Gondar, to provide information for future tourism development practices and serve as insurance against loss of value due to unmanaged deteriorative factors.

The study followed a descriptive research design with qualitative research approach. Data were collected through field observations, interviews and written sources examination.

The study explored the lesser-known hypogea churches, which have significant tourist attraction values, including environmental, historical and architectural significance. However, the use of these potential cultural resources for tourism development is not yet attempted, and their conservation status is found to be critical. This documentation work is significant both for the sake of future tourism development plans and as insurance against looming cultural losses.

This study did not record the ancient treasures of the churches because of the current political instabilities that hindered the access of data. However, it has implication for the need of an extensive documentation activity to trace the cultural resources in the remote areas of the country for future tourism development and conservation practices.

This paper documented the remote hypogea churches not only for the purpose of future tourism development plan but also as an insurance of their values against unmanaged destructive factors.

Drawing motivation from the greater exposure to uncertainty and condition changes that affect large projects due to their long lifecycle, this paper aims to investigate how the time factor affects the use of governance mechanisms to pursue the success of these projects.

To pursue its aim, the article applies the dichotomization between the hard and soft mechanisms of project governance to the analysis of a historical case study, whose findings are organized over the short, medium and long periods. The case selected is referred to the peculiar water system, made up of tunnels named “bottini,” that was in use in Siena (Italy) as the old aqueduct. Specifically, the study focuses on the project of expansion of this water system that was realized during the 14th century for the construction of the “Bottino maestro di Fontegaia.”

This article highlights the different relevance that, during the lifecycle of large projects, is assumed by hard and soft governance mechanisms, with the former having main relevance in a short and medium period, and the latter usually emerging in the medium period and, subsequently, playing a growing role for the project success in the long period.

The article contributes to the literature on large projects by providing novel insights about how the time factor impacts the governance of these projects. Furthermore, the case study, with its unique history, highlights the relevance of combining effectively the hard and the soft dimensions of project governance to pursue success.

This study aims to obtain methods to identify and find the place of damage, which is one of the topics that has always been discussed in structural engineering. The cost of repairing and rehabilitating massive bridges and buildings is very high, highlighting the need to monitor the structures continuously. One way to track the structure's health is to check the cracks in the concrete. Meanwhile, the current methods of concrete crack detection have complex and heavy calculations.

This paper presents a new lightweight architecture based on deep learning for crack classification in concrete structures. The proposed architecture was identified and classified in less time and with higher accuracy than other traditional and valid architectures in crack detection. This paper used a standard dataset to detect two-class and multi-class cracks.

Results show that two images were recognized with 99.53% accuracy based on the proposed method, and multi-class images were classified with 91% accuracy. The low execution time of the proposed architecture compared to other valid architectures in deep learning on the same hardware platform. The use of Adam's optimizer in this research had better performance than other optimizers.

This paper presents a framework based on a lightweight convolutional neural network for nondestructive monitoring of structural health to optimize the calculation costs and reduce execution time in processing.

Earthquakes pose a significant challenge to human safety and the durability of infrastructure, highlighting the urgent need for innovative disaster management strategies. This study addresses the gap in current earthquake disaster management approaches, which are often related to issues of transparency, centralization and sluggish response times. By exploring the integration of blockchain technology into seismic hazard management, the purpose of the research is to overcome these limitations by offering a novel framework for integrating blockchain technology into earthquake risk mitigation and disaster management strategies of smart cities.

This study develops an innovative approach to address these issues by introducing a blockchain-based seismic monitoring and automated decision support system for earthquake disaster management in smart cities. This research aims to capitalize on the benefits of blockchain technology, specifically its real-time data accessibility, decentralization and automation capabilities, to enhance earthquake disaster management. The methodology employed integrates seismic monitoring data into a blockchain framework, ensuring accurate, reliable and comprehensive information. Additionally, smart contracts are utilized to handle decision-making and enable rapid responses during earthquake disasters, offering an effective alternative to traditional approaches.

The study results highlight the system’s potential to foster reliability, decentralization and efficiency in earthquake disaster management, promoting enhanced collaboration among stakeholders and facilitating swift actions to minimize human and capital loss. This research lays the foundation for further exploration of blockchain technology’s practical applications in other disaster management contexts and its potential to transform traditional practices.

Current methodologies, while contributing to the reduction of earthquake-related impacts, are often hindered by limitations such as lack of transparency, centralization and slow response times. In contrast, the adoption of blockchain technology can address these challenges and offer benefits over various aspects, including decentralized control, improved security, real-time data accessibility and enhanced inter-organizational collaboration.

The paper focuses on the issue of manual rebar-binding tasks in the construction industry, which are marked by high labor intensity, high costs and inefficient operations. The rebar-binding robots that are currently available are not fully mature. Most of them can only bind one or two nodes in one position, which leads to significant time wastage in movement. Based on a new type of rebar-binding robot, this paper aims to propose a new movement and binding control that reduces manpower and enhances efficiency.

The robot is combined with photoelectric sensors, travel switches and other sensors. It is supposed to move accurately and run in a limited area on the rebar mesh through logical judgment, speed control and position control. Machine vision is used by the robot to locate the rebar nodes and then adjusts the binding-gun position to ensure that multiple rebar nodes are bound sequentially.

By moving on the rebar mesh with accuracy, the robot meets the positioning accuracy requirements of the binding module, with experimental testing accuracy within 5 mm. Furthermore, its ability to bind four rebar nodes in one place results in a high efficiency and a binding effect that meets building standards.

The innovative design of the robot can adapt itself to the rebar mesh, move accurately to the target position and bind four nodes at that position, which reduces the number of movements on the mesh. Repetitive and heavy rebar-binding tasks can be efficiently completed by the robot, which saves human resources, reduces worker labor intensity and reduces construction overhead. It provides a more feasible and practical solution for using robots to bind rebar nodes.