Actual Site Research Articles

Construction Payment Automation Through Scan-to-BIM and Blockchain-Enabled Smart Contract

Timely approvals and payments to the project participants are crucial for successful completion of construction projects. However, the construction industry faces persistent delays and non-payments to contractors. Despite the desirable benefits of automated payments and enhanced access to digitized data progress, most payment applications rely on centralized control mechanisms; inefficient procedures; and documentation that takes time to prepare, review, and approve. As such, there is a need for a reliable payment automation system that guarantees timely execution of payments upon the detection of completed works. Therefore, this study used a cutting-edge approach to automate construction payments by integrating blockchain-enabled smart contracts and scan-to-Building Information Modeling (BIM). In this approach, scan-to-BIM provides accurate, real-time building progress data, which serve as the source of verifiable off-chain data. A chain-link is then used to securely relay these data to the blockchain system. Blockchain-enabled smart contracts automate the execution of payments upon meeting contract conditions. The proposed approach was implemented on a real case study project. The actual site scan was captured using a photogrammetry 360° camera, which uses a combination of structured light and infrared depth sensing technology to capture 3D data and create detailed 3D models of spaces. This study leveraged accurate, real-time building progress data to automate payments using blockchain-enabled smart contracts upon work completion, thus reducing payment disputes by tying payments to verifiable construction progress, leading to faster release of payments. The findings show that this approach provides a transparent basis for payment, enhancing trust and allowing precise project progress tracking.

Intelligent Data-Driven Task Offloading Framework for Internet of Vehicles Using Edge Computing and Reinforcement Learning

Introduction: The Internet of Vehicles (IoV) was enabled through innovative developments featuring advanced automotive networking and communication to fulfill the need for real-time applications that are latency-sensitive, such as autonomous driving and emergency management. Given that the servers were much farther away from the actual site of operation, traditional cloud computing faced huge delays in processing. Mobile Edge Computing (MEC) resolved this challenge by enabling localized data processing, reducing latency and enhancing resource utilization.Methods: This study proposed an Efficient Mobile Edge Computing-based Internet of Vehicles Task Offloading Framework (EMEC-IoVTOF). The framework integrated deep reinforcement learning (DRL) to optimize task offloading decisions, focusing on minimizing latency and energy consumption while accounting for bandwidth and computational constraints. Offloading costs were calculated using mathematical modeling and further optimized through Particle Swarm Optimization (PSO). An adaptive inertia weight mechanism was implemented to avoid local optimization and enhance task allocation decisions.Result: The proposed framework was thus proved effective for any latency reduction and energy consumption optimization in efficiently improving the overall system performance. DRL and MEC together facilitate scalability in task distribution by ensuring robust performance in dynamic vehicular environments. Integration with PSO further enhances the decision-making process and makes the system highly adaptable to dynamic task demands and network conditions.Discussion:The findings highlighted the potential of EMEC-IoVTOF to address key challenges in IoV systems, including latency, energy efficiency, and bandwidth utilization. Future research could explore real-world deployment and adaptability to complex vehicular scenarios, further validating its scalability and reliability.

Dynamic Comfort Considerations in the Design of High-rise Buildings

<p>The following paper provides research analysis on the influence of wind on a building, considering the regional and local climate conditions, to improve dynamic stability and reduce wind disruption effects on high-rise reinforced concrete buildings. The main aspect of disturbance in living conditions is caused by wind loads on the building. The data is based on structural analysis, generated by the “Lira CAD 2013” ​​software, for a construction project of a 19-story residential building, completed in Georgia’s capital Tbilisi, 58 Kavtaradze St., in accordance with the given necessary requirements and restrictions by general terms and conditions of the Georgian state law. Computer aided modeling provides the ability to simulate and define parameters caused by the wind disruption on an actual site. Calculation process is based on variables specified by state law for each region of the country. Modified accelerations which have been caused by wind in multi-story, high-rise buildings have significantly greater values on upper floors, and reaching the edges ​​​​of the minimum living comfort criteria. The given study features the estimation for high-rise buildings under various combinations of loads. High-rise buildings have gone under significant restrictions due to use of conventional rigid frames as structural elements in the construction process. That process tends to develop a series of new research and findings for proper architectural forms, which became possible using the new generation of fast digital computing software and hardware.</p>

Investigating Surface Settlements During Shield Tunneling Using Numerical Analysis

The development of the subway system in Shenyang City, China, plays a vital role in alleviating traffic congestion and promoting sustainable societal growth. However, the deformation of the surface caused by the tunneling of the shield presents a significant threat to the structural integrity of Shenyang Subway Line 2 and adjacent geotechnical structures. To tackle this challenge, a set of FEA (finite element analysis) simulations were carried out to examine surface deformation under various construction scenarios for Line 2. These simulations were compared with empirical formulas and numerical analyses conducted using Midas GTS NX 2019 software, in addition to actual site measurements. The outcomes of the finite element analysis (FEA) demonstrated a closer alignment with the empirical data than with traditional formulas. The maximum deformation was observed to be approximately twice as large as the equivalent diameter at the back of the excavation face. The analysis indicated that surface deformation is inversely correlated with overburden thickness (H), soil elasticity (E), and the grout filling rate of the shield tail (ψ), while it is directly proportional to the shield’s outer diameter (D). This study provides important methods used in the shield tunneling process employed in the Shenyang subway and suggests that the developed methodologies may be applicable to similar subway projects.

Dynamically Cyclic Fe2+/Fe3+ Active Sites as Electron and Proton-Feeding Centers Boosting CO2 Photoreduction Powered by Benzyl Alcohol Oxidation.

Solar-driven CO2 photoreduction holds promise for sustainable fuel and chemical productions, but the complex proton-coupled multi-electron transfer processes and sluggish oxidation half-reaction kinetics substantially hinder its efficiency. Here, we devised a rational catalyst design to address these challenges by fabricating ferrocene carboxylic acid-functionalized Cs3Sb2Br9 nanocrystals (CSB-Fc NCs), which facilitate simultaneous benzyl alcohol oxidation and CO2 reduction reactions under visible-light irradiation. The synchronized proton-coupled electron transfer processes between the reduction and oxidation half-reactions on CSB-Fc NCs resulted in a 5-fold increase in the CO2 reduction rate (45.56 μmol g-1 h-1, 97.9% CO selectivity) and a 5.8-fold enhancement in benzyl alcohol conversion (97.7% selectivity for benzaldehyde) compared to the CSB. Insitu Raman and ultraviolet-visible diffuse reflectance spectra revealed that the dynamic Fe2+/Fe3+ redox loop within the Fc unit serves as the actual active site, facilitating the activation of substrate molecules. More importantly, insitu attenuated total reflection Fourier transform infrared spectroscopy and gas chromatography-mass spectrometry spectroscopy, with isotope labeling of Deuteron-benzyl alcohol and 13CO2, confirmed that proton transfer from the hydroxyl group generates reactive protons at the Fe2+/Fe3+ site, enabling efficient CO2 photoreduction through subsequent protonation steps. This work offers a cost-effective and efficient approach for synergetic CO2 photoreduction driven by organic synthesis, advancing solar energy utilization.

Fra steinalderforskningens pionértid på Jæren

The formative years of Stone Age research in Jæren (Southwest Norway) 1895–1920, with later contributions and future challenges This paper presents an overview of the efforts and scientific results of Stone Age research in Rogaland 1895–1920 by the archaeologists Gabriel Gustafson, Anton Brøgger and Helge Gjessing, and the entomologist Tor Helliesen, in investigating and trying to understand the early human settlement on the coastal plain of Jæren in Southwest Norway. They all were, in different respects, true pioneers working through a formative period of research 100–130 years ago, influenced and inspired by earlier achievements of Danish archaeology and biology. The results of the first excavations of the open-air site Holeheia in Klepp 1898–1901 and the Svarthåla (Viste) cave in Randaberg 1907–1910 are presented and evaluated in the light of later field investigations, analyses and radiocarbon dating of the actual sites and a few comparable Stone Age localities. Finally, the present status of research, some recent trends and future archaeological prospects in the Jæren region are emphasized.

Study on the impact of vehicle-induced vibration on the flexural behavior of UHPC joints in widened bridges

Bridge widening involves phased construction of adjacent structures to maintain uninterrupted traffic flow. This process exposes freshly placed longitudinal joints between staged deck constructions to vehicle-induced vibrations, potentially compromising their mechanical integrity. This study investigates the flexural behavior of ultra-high-performance concrete (UHPC) longitudinal joints under such vibrations through model tests. To simulate actual site conditions, we developed a novel vibration test setup that replicates the dynamic environment experienced by these joints during construction. Micro- and meso-scale tests were conducted to examine the flexural behavior of longitudinal joints following vibration exposure. Results revealed that vibration amplitude significantly influences fiber orientation and flexural strength of ultra-high-performance concrete (UHPC) wet joint specimens. Low-amplitude vibrations (3 Hz at 1 mm and 3 mm) enhanced fiber orientation, increasing flexural strength by 11.5% to 19.8% and ultimate load capacity by 17% compared to non-vibrated specimens. Conversely, high-amplitude vibrations (3 Hz at 5 mm) adversely affected fiber orientation, decreasing flexural strength by 23.9% and ultimate load capacity by 19% relative to non-vibrated specimens.

Research on Intelligent Diagnosis of Corrosion in the Operation and Maintenance Stage of Steel Structure Engineering Based on U-Net Attention

Intelligent corrosion diagnosis plays a crucial role in enhancing the efficiency of operation and maintenance for steel structures. Presently, corrosion detection primarily depends on manual visual inspections and non-destructive testing methods, which are inefficient, costly, and subject to human bias. While machine vision has demonstrated significant potential in controlled laboratory settings, most studies have focused on environments with limited background interference, restricting their practical applicability. To tackle the challenges posed by complex backgrounds and multiple interference factors in field-collected images of steel components, this study introduces an intelligent corrosion grading method designed specifically for images containing background elements. By integrating an attention mechanism into the traditional U-Net network, we achieve precise segmentation of component pixels from background pixels in engineering images, attaining an accuracy of up to 94.1%. The proposed framework is validated using images collected from actual engineering sites. A sliding window sampling technique divides on-site images into several rectangular windows, which are filtered based on U-Net Attention segmentation results. Leveraging a dataset of steel plate corrosion images with known grades, we train an Inception v3 corrosion classification model. Transfer learning techniques are then applied to determine the corrosion grade of each filtered window, culminating in a weighted average to estimate the overall corrosion grade of the target component. This study provides a quantitative index for assessing large-scale steel structure corrosion, significantly impacting the improvement of construction and maintenance quality while laying a solid foundation for further research and development in related fields.

Butterfly Diversity Patterns Provide New Insights Into Biodiversity Conservation in China

ABSTRACTAimGlobally, the knowledge of insect distributions is largely insufficient, and that hinders conservation actions against biodiversity loss. Focusing on butterfly diversity, we aimed to fill knowledge gaps and provide new insights into biodiversity conservation in China.LocationChina.Time PeriodOccurrence records from 1950 to 2023.Major Taxa StudiedButterflies, Lepidoptera.MethodsWe collected butterfly occurrence records from published literature, online databases and our butterfly specimens, and then used either species distribution models or expansion from actual occurrence sites to estimate species distribution ranges in China. We identified key environmental variables that are related to butterfly biodiversity patterns, and delineated priority conservation areas based on butterfly distributions.ResultsWe report the first country‐wide inventory and mapping of China's 1920 butterfly species. The identified hotspots of species richness are distributed mainly in southwestern, southern and southeastern China. Among variables strongly correlated with species richness, the most important one is actual evapotranspiration. Conservation priority areas under the Convention on Biological Diversity 17% area target overlapped well with the hotspots, but only 29.8% of them are covered by existing nature reserves. When that protection target increases to 30%, the additional areas are located mainly in southern China, with its large cities and intensive agriculture.Main ConclusionsWe find that some protected butterfly species have a larger area of habitat than most species, implying that the list of protected butterflies in China should be revised. Urban and farmland landscapes may help sustain butterfly diversity and they should be considered in conservation planning.

Seismic response of monopile offshore wind turbines in liquefiable sand considering vertical ground motion

Offshore wind turbines experience environmental loads such as winds and waves throughout their operational lifespan, as well as accidental earthquakes in earthquake-prone regions. Earthquakes typically include high-frequency components in the vertical direction, which can be close to the natural frequency of offshore wind turbines. This study aims to assess the impact of the vertical seismic component on the dynamic response of monopile offshore wind turbines. A three-dimensional fully coupled finite element model is developed to realistically consider the dynamic interaction between the actual site and the structure. The method for simulating the interaction between water and soil is incorporated into the numerical model. Stochastic theory is utilized to simulate wind and wave. The model's accuracy is validated by comparing numerical predictions with centrifuge experimental results. The results of the parameters analysis indicate that the vertical seismic component significantly impacts the seismic response of offshore wind turbines and induces complex nonlinear behavior in sandy soil.

Operando elucidation of hydrogen production mechanisms on sub-nanometric high-entropy metallenes

Precise morphological control and identification of structure-property relationships pose formidable challenges for high-entropy alloys, severely limiting their rational design and application in multistep and tandem reactions. Herein, we report the synthesis of sub-nanometric high-entropy metallenes with up to eight metallic elements via a one-pot wet-chemical approach. The PdRhMoFeMn high-entropy metallenes exhibit high electrocatalytic hydrogen evolution performances with 6, 23, and 26 mV overpotentials at −10 mA cm−2 in acidic, neutral, and alkaline media, respectively, and high stability. The electrochemical measurements, theoretical simulations, and operando X-ray absorption spectroscopy reveal the actual active sites along with their dynamics and synergistic mechanisms in various electrolytes. Specially, Mn sites have strong binding affinity to hydroxyl groups, which enhances the water dissociation process at Pd sites with low energy barrier while Rh sites with optimal hydrogen adsorption free energy accelerate hydride coupling, thereby markedly boosting its intrinsic ability for hydrogen production.

Investigation and evaluation of gastrointestinal toxicity biomarkers in rats with different sites of gastrointestinal injury

There are few reliable biomarkers for gastrointestinal toxicity, and the further identification of such markers can improve the accuracy and speed of toxicological evaluations. This study aimed to evaluate the effectiveness of several recently proposed biomarkers—plasma citrulline, fecal calprotectin, fecal bile acid, and plasma miRNAs (miR-194 and -215)—in detecting intestinal toxicity. To this end, cysteamine hydrocholoride (cysteamine, 600 or 900 mg/kg, PO), indomethacin (10 mg/kg, PO), or 2,4-Dinitrobenzenesulfonic acid hydrate (DNBS, 20 mg/kg, IR) were administered to male Wistar rats to establish models of gastric/duodenal, jejunum/ileum, or colonic damage, respectively. Both novel biomarkers and traditional toxicological parameters were evaluated in these rat models. Standard in-life observations, such as fecal properties or body weight, were inadequate for monitoring intestinal toxicity, as there were few observable changes indicative of intestinal injury, especially in the cysteamine and indomethacin models. Plasma citrulline drastically decreased in cysteamine or indomethacin-treated rats, with a milder decrease in DNBS-treated animals. Fecal total bile acids and calprotectin levels increased only in rats treated with indomethacin or DNBS, but not with cysteamine. While plasma miR-194 remained unchanged across all models, miR-215 levels decreased after cysteamine treatment. Together, these results suggest that plasma citrulline and fecal calprotectin may be effective biomarkers for monitoring intestinal injury. Fecal TBA and plasma miR-215 also show potential as useful biomarkers, but further research is needed to confirm their efficacy. Specifically, plasma citrulline is indicative of damage from the stomach to the ileum, fecal total bile acids and calprotectin are indicative of damage from the jejunum to the rectum, and plasma miR-215 is indicative of damage from the stomach and the duodenum. Integrating these novel biomarkers with standard toxicological parameters will help in predicting actual intestinal sites of damage. This integration has the potential to improve the quality of toxicological evaluations. Our findings support the use of these biomarkers in clinical settings.

Carboxymethyl Chitosan as a Reversible Template of Calcium Phosphate for Multifunctional Conservation of Carbonate Stone.

The accelerated deterioration of carbonate stone artifacts under climate change has long been an urgent issue. Inspired by biomineralization, we developed carboxymethyl chitosan-diammonium hydrogen phosphate (CD) composite and investigated the conservation effectiveness of the CD composite compared to diammonium hydrogen phosphate (DAP) on limestone. The morphologies and microstructures were characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), and Fourier-transform infrared spectroscopy (FT-IR). The consolidating effectiveness was investigated through a compressive strength test. The protective ability was assessed by disintegration resistance test, acid attack resistance test, salt attack resistance test, and freeze-thaw aging cycle test. SEM observations revealed that carboxymethyl chitosan (CMCS) served as an effective template, inducing the in situ formation of a uniform and continuous calcium phosphate coating on both the surface and interior of the stone. The tests indicated that the CD composite further enhanced the consolidating effectiveness and improved resistance to disintegration and freeze-thaw cycles. Notably, as an amphiphilic polyelectrolyte, CMCS functioned as a pH buffer and a protective barrier against sodium sulfate salts, which improved the resistance to acid and salt attacks. Additionally, the CD composite did not cause significant variations in the esthetic appearance or water vapor permeability. We then applied the CD composite at an actual carbonate stone cultural heritage site, successfully demonstrating the feasibility of CD application and the reversibility of CMCS in a real-world setting. Based on the study's results, our approach provides a new perspective for developing multifunctional and sustainable conservation materials for carbonate stone artifacts.

Predictors of Recurrence After Curative Surgery for Stage I Colon Cancer: Retrospective Cohort Analysis of the Italian Society of Surgical Oncology Colorectal Cancer Network Collaborative Group.

The aim of this study is to provide solid evidence to update the management of stage I colon cancer (CC) after surgery. Given the low risk of recurrence of stage I CC, some international guidelines do not recommend intensive follow-up after surgery. However, data on the actual incidence, risk factors, and site of recurrences are scarce. This is a retrospective multicenter cohort study considering patients who underwent surgery at 25 Italian centers between 2010 and 2019, with a minimum follow-up of 24 months. A total of 1883 consecutive adult patients with stage I CC treated with curative surgery were considered, and 1611 fulfilled the inclusion criteria. The primary outcome was the rate of recurrence. Secondary outcomes included survival and risk factors for recurrence. Eighty patients developed cancer recurrence (5.0%), of which 90% was systemic relapse. The event was more frequent in pT2 (6.0% vs 3.2%, P = 0.013), male patients (6.1% vs 3.6%, P = 0.021), in the presence of lymphovascular invasion (7.2% vs 3.6%, P = 0.01), and in cases of partial resection (11.1% vs 4.6%, P = 0.011). Also, preoperative carcinoembryonic antigen (P = 0.007) and tumor diameter (P < 0.001) were higher in the group who relapsed. Most patients had isolated cancer recurrence (90%). Recurrences peaked between 10 and 18 months after surgery and declined over time. Adjusted Cox regression analysis identified tumor diameter, carcinoembryonic antigen level, lymphovascular invasion, male gender, and less than 12 analyzed lymph nodes as significant risk factors for worse recurrence-free survival. This study showed that a not negligible rate of stage I CC recur after curative surgery. Most relapses occur at a single site within the first 3 years after surgery. This evidence could be used to optimize postoperative follow-up.

Overlooked Role of Electrostatic Interactions in HER Kinetics on MXenes: Beyond the Conventional Descriptor ΔG ∼ 0 to Identify the Real Active Site.

Understanding the atomic-level mechanism of the hydrogen evolution reaction (HER) on MXene materials is crucial for developing affordable HER catalysts, while their complex surface terminations present a substantial challenge. Herein, employing constant-potential grand canonical density functional theory calculations, we elucidate the reaction kinetics of the HER on MXenes with various surface terminations by taking experimentally reported Mo2C as a prototype. We observe a contradictory scenario on Mo2C MXene when using conventional thermodynamic descriptor ΔGH* (hydrogen binding energy). Both competing surface phases that emerge close to the equilibrium potential meet the ΔGH* ∼ 0 criterion, while they exhibit distinctly different reaction kinetics. Contrary to previous studies that identified surface *O species as active sites, our research reveals that these *O sites are kinetically inert for producing H2 but are easily reduced to H2O. Consequently, the surface Mo atoms, exposed from the rapid reduction of the surface *O species, serve as the actual active sites catalyzing the HER via the Volmer-Heyrovsky mechanism, as confirmed by experimental studies. Our findings highlight the overlooked role of electrostatic repulsion in HER kinetics, a factor not captured by thermodynamic descriptor ΔGH*. This work provides new insights into the HER mechanism and emphasizes the importance of kinetic investigations for a comprehensive understanding of the HER.

Classification of Hyperspectral Images of Explosive Fragments Based on Spatial-Spectral Combination.

The identification and recovery of explosive fragments can provide a reference for the evaluation of explosive power and the design of explosion-proof measures. At present, fragment detection usually uses a few bands in the visible light or infrared bands for imaging, without fully utilizing multi-band spectral information. Hyperspectral imaging has high spectral resolution and can provide multidimensional reference information for the fragments to be classified. Therefore, this article proposed a spatial-spectral joint method for explosive fragment classification by combining hyperspectral imaging technology. In a laboratory environment, this article collected hyperspectral images of explosion fragments scattered in simulated scenes. In order to extract effective features from redundant spectral information and improve classification accuracy, this paper adopted a classification framework based on deep learning. This framework used a convolutional neural network-bidirectional long short-term memory network (CNN-BiLSTM) as the spectral information classification model and a U-shaped network (U-Net) as the spatial segmentation model. The experimental results showed that the overall accuracy exceeds 95.2%. The analysis results indicated that the method of spatial-spectral combination can accurately identify explosive fragment targets. It validated the feasibility of using hyperspectral imaging for explosive fragment classification in laboratory environments. Due to the complex environment of the actual explosion site, this study still needs to be validated in outdoor environments. Our next step is to use airborne hyperspectral imaging to identify explosive fragments in outdoor environments.

Activating the Mn Single Atomic Center for an Efficient Actual Active Site of the Oxygen Reduction Reaction by Spin-State Regulation.

The ligand engineering for single-atom catalysts (SACs) is considered a cutting-edge strategy to tailor their electrocatalytic activity. However, the fundamental reasons underlying the reaction mechanism and the contemplation for which the actual active site for the catalytic reaction depends on the pyrrolic and pyridinic N ligand structure remain to be fully understood. Herein, we first reveal the relationship between the oxygen reduction reaction (ORR) activity and the N ligand structure for the manganese (Mn) single atomic site by the precisely regulated pyrrolic and pyridinic N4 coordination environment. Experimental and theoretical analyses reveal that the long Mn-N distance in Mn-pyrrolic N4 enables a high spin state of the Mn center, which is beneficial to reduce the adsorption strength of oxygen intermediates by the high filling state in antibond orbitals, thereby activating the Mn single atomic site to achieve a half-wave potential of 0.896 V vs RHE with outstanding stability in acidic media. This work provides a new fundamental insight into understanding the ORR catalytic origin of Mn SACs and the rational design strategy of SACs for various electrocatalytic reactions.

Deep-Learning-Based Automated Building Construction Progress Monitoring for Prefabricated Prefinished Volumetric Construction.

Prefabricated prefinished volumetric construction (PPVC) is a relatively new technique that has recently gained popularity for its ability to improve flexibility in scheduling and resource management. Given the modular nature of PPVC assembly and the large amounts of visual data amassed throughout a construction project today, PPVC building construction progress monitoring can be conducted by quantifying assembled PPVC modules within images or videos. As manually processing high volumes of visual data can be extremely time consuming and tedious, building construction progress monitoring can be automated to be more efficient and reliable. However, the complex nature of construction sites and the presence of nearby infrastructure could occlude or distort visual data. Furthermore, imaging constraints can also result in incomplete visual data. Therefore, it is hard to apply existing purely data-driven object detectors to automate building progress monitoring at construction sites. In this paper, we propose a novel 2D window-based automated visual building construction progress monitoring (WAVBCPM) system to overcome these issues by mimicking human decision making during manual progress monitoring with a primary focus on PPVC building construction. WAVBCPM is segregated into three modules. A detection module first conducts detection of windows on the target building. This is achieved by detecting windows within the input image at two scales by using YOLOv5 as a backbone network for object detection before using a window detection filtering process to omit irrelevant detections from the surrounding areas. Next, a rectification module is developed to account for missing windows in the mid-section and near-ground regions of the constructed building that may be caused by occlusion and poor detection. Lastly, a progress estimation module checks the processed detections for missing or excess information before performing building construction progress estimation. The proposed method is tested on images from actual construction sites, and the experimental results demonstrate that WAVBCPM effectively addresses real-world challenges. By mimicking human inference, it overcomes imperfections in visual data, achieving higher accuracy in progress monitoring compared to purely data-driven object detectors.

A Case Study from Karbala, Iraq, of Geospatial Strategies for Optimal Asphalt Concrete Plant Placement

Having a new capital improvement facility means a significant and lasting commitment and the selection of an actual site is the key factor in success. This decision affects a wide range of undertakings including land use planning and location of industrial structures. The arrangement for asphaltic concrete plants is thus surrounded by several factors that include economic, social, technological and environmental factors. Site selection in a nutshell is the process of choosing sites that have suitable characteristics and within certain parameters to produce a good site from an operational point of view. In this study, we adopt the combination of GIS techniques and cost breakdown methods to overcome the hurdles facing choices of the most appropriate sites to establish asphaltic concrete plants. This research, undertaken in the Karbala Governorate of Iraq, systematically employs tools to map areas optimising economic, environmental and social costs. The study shows that there could be around 120 possible locations for asphaltic concrete plants, on a minimum area of 40,000 square meters. This paper takes GIS modelling and cost breakdown approaches to offer an efficient approach in determining suitable plant locations and thus serves as a useful guideline in decisions of infrastructural investments

Improving construction site efficiency through automated progress monitoring of underground pipe installation sites using image color analysis of iPhone LiDAR camera data

This study presents an innovative method utilizing smartphone for automated progress monitoring at underground pipe installation sites. Leveraging the LiDAR iPhone camera, the method captures detailed point cloud data of construction sites. Sophisticated color analysis of images accurately distinguishes between areas with and without pipes within excavations. Key aspects of the proposed workflow include segmentation of the excavation area, differentiation between main and side excavations, and application of an earth color mask in the RGB space to isolate pipes. The research focuses on enhancing measurement precision for excavation width, depth, and pipe burial depth, significantly reducing the manual labor traditionally required at construction sites, thereby offering an efficient and cost-effective solution. We further demonstrated the robustness of the proposed algorithm by applying it to two types of data acquired at actual construction sites. This approach is expected to contribute significantly to the digital transformation in the construction industry.