Ensuring high-quality fill compaction is crucial for the stability and longevity of infrastructures and affects the sustainability of urban infrastructure networks. The purpose of this paper is to provide a refined analysis and insight understanding of the current practice, limitations, challenges, and future development trends of compaction methods from the perspective of the development stage. This paper offers a comprehensive overview of the evolution of compaction methods and classifies compaction quality control methods into four groups through quantitative analysis of literature: traditional compaction methods, digital compaction methods, automated compaction methods, and intelligent compaction methods. Each method's properties and issues are succinctly stated. Then, the research on three key issues in intelligent compaction including compaction quality evaluation algorithms, dynamic optimal path planning, and implementation of unmanned technology is summarized. Currently, the field of intelligent compaction is far from mature, a few challenges and limitations need further investigation: coupling problems of multiple indicators in intelligent evaluation algorithms, unmanned roller groups collaborative control problems, and intelligent decision-making and optimization problems of multi-vehicle compaction paths. This review serves as a valuable reference for systematically understanding the development of compaction methods.

In recent years, the comprehensive and extensive development of urban underground space (UUS) has gained substantial popularity with the efficient guidance of UUS planning. This study discussed the research trends and paradigm shift in UUS planning over the past few decades. Bibliometric and comparative studies were conducted to identify the contributions of the research in this field. The analysis identified the overall temporal development trend of UUS planning and the research hot spots, namely, the primary use of UUS and UUS planning technology. Additionally, the study identified academic collaborative relationships through country and institution co-occurrence network analysis. The diversified development philosophy, planning systems, key planning scenarios, and data-driven technology pertaining to UUS planning have been extracted through keyword co-occurrence network analysis. Moreover, the planning systems, planning management, and planning practices for UUS in various countries, including Singapore, Japan, Finland, Canada, and China, were also systematically reviewed. By doing so, the worldwide UUS planning evolution has been identified. The paradigm shift for UUS planning has been clarified, involving technical method, result form, control mode, and control elements. Furthermore, the conceptual data-driven framework for UUS planning, which orients multiple development concepts, has been proposed to meet the requirement of next frontier development.

Preventing/mitigating natural disasters in urban areas can indirectly be part of the 17 sustainable economic and social development intentions according to the United Nations in 2015. Four types of natural disasters—flooding, heavy rain-induced slope failures/landslides; earthquakes causing structure failure/collapse, and land subsidence—are briefly considered in this article. With the increased frequency of climate change-induced extreme weathers, the numbers of flooding and heavy rain-induced slope failures/landslides in urban areas has increased in recent years. There are both engineering methods to prevent their occurrence, and more effectively early prediction and warning systems to mitigate the resulting damage. However, earthquakes still cannot be predicted to an extent that is sufficient to avoid damage, and developing and adopting structures that are resilient against earthquakes, that is, structures featuring earthquake resistance, vibration damping, and seismic isolation, are essential tasks for sustainable city development. Land subsidence results from human activity, and is mainly due to excessive pumping of groundwater, which is a “natural” disaster caused by human activity. Countermeasures include effective regional and/or national freshwater management and local water recycling to avoid excessive pumping the groundwater. Finally, perspectives for risk warning and hazard prevention through enhanced field monitoring, risk assessment with multi-criteria decision-making (MCDM), and artificial intelligence (AI) technology.

Wind-driven sand erosion is the leading primary reason of earth deterioration in dry lands and a major global issue. Desert dust emissions and topsoil degradation caused by wind pose a global danger to the ecosystem, economy, and individual health. The aim of the current study is to critically analyze the different types of biopolymers and their interaction mechanism with sands for desert sand stabilization. Extensive experimental data with different percentages of biopolymers has been presented on various wind erosion studies using wind tunnel testing and their control rate on desert sand stabilization. Also, studies related to evaluating the engineering properties of sand using biopolymers were analyzed. Other biological approaches, namely Microbial-induced calcite precipitation (MICP) and Enzyme-induced carbonate precipitation (EICP), have been discussed to regulate wind-driven sand erosion in terms of percentage calcite formation at different compositions of urea and calcium chloride. Comparative analysis of MICP and EICP with biopolymer treatment and their limitations have been discussed. Biopolymers are not only demonstrated adeptness in engineering applications but are also helpful for environment safety. Biopolymers are suggested to be novel and nature-friendly soil-strengthening material. This review focuses on the fundamental mechanisms of biopolymer treatment to reduce wind-driven sand loss and its future scope as a binder for sand stabilization. The mechanism of soil-biopolymer interaction under various soil conditions (water content, density, and grain size distribution) and climatic circumstances (drying-wetting cycles) needs to be explored. Furthermore, before applying on a large scale, one should evaluate sand-biopolymer interaction in terms of durability and viability.

As natural quarry materials become increasingly scarce and uneconomical, the construction industry has turned to sustainable alternatives such as construction and demolition (C&D) wastes and recycled glass for road construction. The aim of this study was to evaluate the performance of mixtures consisting of recycled glass (RG), crushed brick (CB) and recycled concrete aggregate (RCA) under varied traffic conditions. This assessment was conducted through wheel-tracking (WT) tests under simulated high-traffic conditions, which involved subjecting the mixtures to elevated vertical loads and an increased number of load cycles compared to previous studies. The study revealed that both RCA + 20%RG and RCA + 20%CB blends displayed comparable or slightly greater mean surface deformations than natural crushed rock under default conditions. The default conditions specified by the local road authority include an 8 kN wheel load and 40,000 loading cycles. The study further revealed that both blends displayed a consistent increase in rut depth as the number of cycles increased up to 100,000 while being subjected to a 20 kN wheel load. The maximum rut depth of RCA + 20%RG was close to the lower end of the maximum allowable rut depth range specified by road authorities. This suggests that these blends are at the limits of carrying heavier loads on highly trafficked roads.

Uniaxial compressive strength (UCS) has become a highly essential strength parameter in the mining, civil and geomechanical industries. Estimating the exact value of the strength of rock has become a matter of great concern in real life. Despite this, there have been many works to indirectly/directly estimate the UCS of rocks. This study introduces a novel stacked generalisation methodology for estimating the UCS of rocks in geomechanics. In this study, generalised regression neural network (GRNN), radial basis function neural network (RBFNN), and random forest regression (RF) were used as the base learners and the multivariate adaptive regression spline (MARS) functioned as the meta-learner for the proposed stacking method. The proposed 3-Base learner stack model exhibited dominance over single applied AI methods of GRNN, RBFNN, and RF when confirmed with similar datasets by employing performance metrics like the Nash–Sutcliffe Efficiency Index (NSEI), Root Mean Squared Error (RMSE), Performance Index (PI), Scatter Index (SI) and Bayesian Information Criterion (BIC). The proposed 3-Base learner stack model scored the least RMSE, PI, and SI scores of 1.02775, 0.50691, and 0.00788 respectively for the testing datasets. In addition, it also produced the utmost NSEI value of 0.99969 and the least BIC value of 16.456 as likened to other competing models (GRNN, RBFNN and RF), reaffirming its power in forecasting the UCS of rocks in geomechanical engineering.

The surface morphology of the structural surface of the rock mass plays a crucial role in determining its macroscopic physical and mechanical properties, including shear strength and seepage characteristics. The morphological characteristics of the rock mass structure exhibit significant anisotropy and size effects. The distribution characteristics of the two key indicators that affect the morphological characteristics of the structure were analyzed, revealing that the undulation degree and undulation angle conform to the normal distribution and Weibull distribution, respectively. The present study defines a method for quantifying the 3D roughness of structural surface based on the features of undulation degree and undulation angle. Through quantitative analysis, it was observed that the roughness parameters exhibit anisotropic characteristics at different sampling intervals and shear directions.

The present study demonstrates the development of an Android Application that aims to calculate the allowable bearing pressure for shallow foundations and safe load on pile foundations using the SPT data. The application was built using Android Studio 2020, utilizing XML for the User Interface and Java for the coding. The application offers support for various foundation types, including strip, square, rectangle, and circular shapes for shallow foundations and circular shape for pile foundations. The in-situ SPT data entered by the user was corrected and then processed to calculate soil properties. Subsequently, the bearing pressure for shallow foundation and safe load on the pile was computed adhering to relevant codes. The developed application was verified by comparing the results with already solved examples in the literature. The developed application may be considered under Intelligence in Geotechnics. The created application will be helpful for field engineers to estimate soil parameters and allowable bearing pressure on-site quickly. As a result, it decreases the amount of time and effort necessary for design and thus eliminates the need to refer to tables, codes, and consultants.

The frequent occurrence of building collapse accidents not only causes significant casualties, but also jeopardizes local economies. This paper adopts a combinatory assessment approach to showcase the lessons learned from a recent building collapse in Changsha, China. The proposed approach blends the system thinking approach and strategic environmental assessment (SEA) model. It delineates the causes of collapse and provide key leverage points for safety management. The results show that the primary causes for the collapse are the poor construction quality, illegal alterations, and lack of regulations enforcement. The management of rural housing construction in Hunan Province achieved a total score of 4 out of 30. It was also determined that the key prevention measures for abating these deleterious phenomena involve ensuring quality assurance/quality control, efficiently assessing safety risk, and timely performing structural health monitoring. This study is bound to enhance the understanding of collapse accidents and foster the achievement of sustainable cities and communities.

This study compared the extent to which COVID-19 impacted travel demand of bike-sharing and taxi in New York City, and further explored how the changes in travel demand were associated with the built environment through four typical regression models, namely, least squares (OLS) regression, geographically weighted regression (GWR), temporally weighted regression (TWR), and geographically and temporally weighted regression (GTWR) models. In particular, this study looked at two phases: the lockdown phase (during which travel demand decreased dramatically) and initial recovery phase (during which travel demand started to increase). The results suggested that 1) GTWR performed better than the other three model types; 2) shared bike ridership rebounded much more significantly during the recovery phase than taxi ridership; 3) Commercial Point of Interest (POI) was positively associated with the change of ridership in both lockdown and recovery phases.

Constructing sustainable cities for the future usually encounters some challenges such as reducing the environmental footprint through using eco-friendly materials. Coarse recycled aggregate retrieved from demolished concrete structures is considered one of the eco-friendly building materials. This study aims to investigate the impact of strengthened recycled aggregate with pozzolan slurry on the different properties of concrete. Three various groups of pozzolan slurries; silica fume with fly ash, cement with fly ash, and nano-silica are used to strengthen the inferior properties of recycled aggregate. Findings showed that the proposed treatment method efficiently improved the quality of recycled aggregate. Also, this method achieved eco-friendly concrete with preferable mechanical behavior and greater resistivity against chloride diffusion. The wastage of compressive strength was 10–20%, flexural strength was 5–16%, and the elastic modulus was 13–30% of the recycled aggregate concrete in comparison with normal concrete at 28 days. By considering the application of recycled aggregate in the manufacture of new concrete, this study's results can serve as principles for achieving sustainable concrete infrastructure in the smart cities of the future.

The pressure infiltration behavior of bentonite slurry (a mixture of water and bentonite) in front of a slurry tunnel boring machine (TBM) determines the effectiveness of tunnel face support when tunneling through saturated sand. This paper provides a comprehensive review of relevant studies, encompassing the rheology of bentonite slurry, laboratory experiments, numerical simulations for modeling slurry infiltration in sand, and an exploration of the membrane behavior of filter cake. The review found that variations in test conditions for bentonite slurry are the primary contributing factor leading to discrepancies in rheological measurement results. Conventional column-based slurry infiltration tests often impose a high hydraulic gradient on the soil sample, making the observations from these tests incomparable to real tunnel scenarios where the hydraulic gradient is much lower. Two primary slurry infiltration types were identified: one involving an external filter cake alongside an infiltration zone, and the other featuring solely an infiltration zone. The filter cake effectively stops further infiltration of bentonite and serves as a media for transferring the slurry pressure to the soil skeleton. Owing to the viscoplastic properties of bentonite slurry, a decrease in flow velocity fosters an increase in rheological resistance, thereby aiding in the stabilization of the excavation process. The inclusion of fine sand, seawater, and liquids with acidic or heavy metal properties could notably undermine both the characteristics of bentonite slurry and the sealing capacity of the filter cake. Hence, it becomes crucial to effectively control the workability of bentonite slurry throughout the process of slurry TBM tunneling.

Real estate plays a crucial role in driving national economies. However, the process of transferring properties and engaging with various stakeholders can be hindered by a lack of adequate information, complex procedures, and excessive paperwork. The advent of digital real estate has revolutionized the industry and how stakeholders interact. The present study aims to conduct a bibliometric and systematic review of digital real estate, utilizing historical, institutional, country, and keyword analyses for the bibliometric review and Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines for the systematic review. Through thematic analysis, the study identified four key themes for transforming digital real estate: information communication technologies, data collection technologies, data networking tools, and digital decision-making systems. Additionally, the study proposes a digital real estate transformation framework that can assist stakeholders, urban planners, and decision-makers in embracing digital tools and technologies. The study concludes that digital real estate has the potential to revolutionize future urban planning and real estate development through the use of decision support systems and advanced technologies.

Efforts to reduce the weight of buildings and structures, counteract the seismic threat to human life, and cut down on construction expenses are widespread. A strategy employed to address these challenges involves the adoption of foam concrete. Unlike traditional concrete, foam concrete maintains the standard concrete composition but excludes coarse aggregates, substituting them with a foam agent. This alteration serves a dual purpose: diminishing the concrete’s overall weight, thereby achieving a lower density than regular concrete, and creating voids within the material due to the foam agent, resulting in excellent thermal conductivity. This article delves into the presentation of statistical models utilizing three different methods—linear (LR), non-linear (NLR), and artificial neural network (ANN)—to predict the compressive strength of foam concrete. These models are formulated based on a dataset of 97 sets of experimental data sourced from prior research endeavors. A comparative evaluation of the outcomes is subsequently conducted, leveraging statistical benchmarks like the coefficient of determination (R ²), root mean square error (RMSE), and mean absolute error (MAE), with the aim of identifying the most proficient model. The results underscore the remarkable effectiveness of the ANN model. This is evident in the ANN model’s R ² value, which surpasses that of the LR model by 36% and the non-linear model by 22%. Furthermore, the ANN model demonstrates significantly lower MAE and RMSE values compared to both the LR and NLR models.

Waterlogging in subway stations has a devastating impact on normal operation of important urban facilities and can cause harm to passengers and property. It is difficult to assess the vulnerability of metro stations to waterlogging because many complex factors are involved. This study proposes a hybrid model to assess the vulnerability of subway stations to waterlogging by integrating the entropy weight method (EWM) with a technique for order preference based on similarity to the ideal solution (TOPSIS) (the EWM-TOPSIS method). The model is based on analysis of factors influencing the vulnerability of subway stations to waterlogging. The proposed method was applied to a field case (Jinshahu station in Hangzhou, found to be vulnerable to waterlogging at level IV). The results from EWM-TOPSIS, EWM, and TOPSIS were compared. The results using the EWM-TOPSIS method were more accurate and reliable than those using EWM and TOPSIS. However, the reliability of EWM-TOPSIS was determined based on historical data, which cannot capture rapidly changing factors. Based on the assessment results, recommendations were made to promote the overall health and development of urban areas to satisfy the United Nations Sustainable Development Goal 11 (SDG11).

The installation of temporary retaining walls for excavation activities is considered a crucial and costly aspect in the realm of geotechnical engineering construction. Several past studies have been undertaken on the stability aspects of the deep mixing method for soil–cement column walls in soft Bangkok clay are available. However, there has been a lack of research focusing specifically on the relationship between execution time, cost, and stability of these walls, a topic on which this study is focused. The principal aim of this research was to investigate and make a comparative analysis of the stability, construction cost, and construction duration of retaining walls under varying construction site conditions. This study placed particular emphasis on shallow excavation conducted in the context of soft Bangkok clay, and its focus was to determine the most effective construction management strategies within the given contextual parameters. The investigated wall systems comprised of soil–cement columns (SC), stiffened soil–cement columns (SSC), and sheet pile walls. The SC had a diameter of a diameter of 0.6 m, while the SSC was composed of an embedded steel pipe with a diameter of 0.2 m (SSC-IRow Wall). The stability of the walls under investigation was assessed through the utilization of finite element (FE) simulation. The finite element model was initially calibrated through a comparison between the simulation results and the data obtained from field measurements. For a 4.5 m deep excavation with a required factor of safety > 1.3, the SCC and SSCC Walls were found to have an advantage over the conventional sheet pile wall. The SC Wall, consisting of two rows and measuring 7 m in length, demonstrated superior efficiency in terms of both time and cost, regardless of whether it was implemented in unconfined or confined construction sites. The utilization of the SSC-IRow Wall was suggested as an alternative in cases where the use of a thick SC Wall was prohibited. A systematic approach for the selection and design of the SC and SSC Wall systems was proposed, drawing upon a thorough examination and evaluation of the study findings. The results of this study possess the capacity to be utilized in excavation endeavors encompassing diverse depths in the context of soft Bangkok clay and comparable soil conditions.

A detailed investigation was conducted to analyse the mechanical and durability features of a mixture of binary cement concrete modified with nanomaterials. In the context of the concrete matrix, the substitution of fractional cement content was carried out using Nano silica (NS) at concentrations of 1%, 2%, and 3%. Four distinct cementitious blends were subjected to a comprehensive match of tests, which encompassed compressive strength, flexural strength, split tensile strength, static modulus of elasticity, bulk density, water absorption, permeability, carbonation resistance, acid attack resistance, and rapid chloride penetration. The compositions of the mixes were investigated through the use of various microstructural analysis techniques, including scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (SEM-EDS), thermogravimetric analysis (TGA), X-ray diffraction (XRD), and Fourier-transform infrared spectroscopy (FTIR). The research revealed significant improvements in the mechanical and durability characteristics of the material. An increment in the mechanical and durability properties of mixtures were seen due to inclusion of marble power and Nano silica due to enhanced pozzolanic activities of composite and its filling effect. It is worth mentioning that Nano silica has shown the potential to mitigate the degradation caused by exposure to sulfuric acid. The SEM-EDX analysis demonstrated a decrease in the Ca/Si ratio when compared to the reference combination, suggesting an increase in the consumption of calcium hydroxide (CH) and the creation of a more compact calcium-silicate-hydrate (C-S-H) gel. The X-ray diffraction (XRD) findings indicate that NS has the ability to act as an accelerator for pozzolanic processes. This is achieved by consuming calcium hydroxide (CH) and promoting the creation of extra calcium silicate hydrate (C-S-H), which ultimately enhances the overall performance of the concrete mixture.

Given the importance and interest of buildings in the urban environment, numerous studies have focused on automatically extracting building outlines by exploiting different datasets and techniques. Recent advancements in unmanned aerial vehicles (UAVs) and their associated sensors have made it possible to obtain high-resolution data to update building information. These detailed, up-to-date geographic data on the built environment are essential and present a practical approach to comprehending how assets and people are exposed to hazards. This paper presents an effective method for extracting building outlines from UAV-derived orthomosaics using a semantic segmentation approach based on a U-Net architecture with a ResNet-34 backbone (UResNet-34). The novelty of this work lies in integrating a grey wolf optimiser (GWO) to fine-tune the hyperparameters of the UResNet-34 model, significantly enhancing building extraction accuracy across various localities. The experimental results, based on testing data from four different localities, demonstrate the robustness and generalisability of the approach. In this study, Locality-1 is well-laid buildings with roads, Locality-2 is dominated by slum buildings in proximity, Locality-3 has few buildings with background vegetation and Locality-4 is a conglomeration of Locality-1 and Locality-2. The proposed GWO-UResNet-34 model produced superior performance, surpassing the U-Net and UResNet-34. Thus, for Locality-1, the GWO-UResNet-34 achieved 94.74% accuracy, 98.11% precision, 84.85% recall, 91.00% F1-score, and 88.16% MIoU. For Locality-2, 90.88% accuracy, 73.23% precision, 75.65% recall, 74.42% F1-score, and 74.06% MioU was obtained.The GWO-UResNet-34 had 99.37% accuracy, 90.97% precision, 88.42% recall, 89.68% F1-score, and 90.21% MIoU for Locality-3, and 95.30% accuracy, 93.03% precision, 89.75% recall, 91.36% F1-score, and 88.92% MIoU for Locality-4.